diff --git a/www/services/file_extractor.py b/www/services/file_extractor.py
index 33a38fa6..a8a66b09 100644
--- a/www/services/file_extractor.py
+++ b/www/services/file_extractor.py
@@ -58,7 +58,7 @@ def extract_from_file(file_path: str, source: str) -> list[dict]:
# FILE TABELLARI (CSV/XLSX/XLS)
# Scopus, Dimensions, Lens, OpenAlex e WoS tabellare
-
+
elif file_extension in ['.csv', '.xlsx', '.xls']:
print(f"[{source_upper}] Lettura file tabellare {file_extension}: {file_path}")
@@ -66,24 +66,33 @@ def extract_from_file(file_path: str, source: str) -> list[dict]:
if file_extension == '.csv':
df = pd.read_csv(
file_path,
- dtype=str, # Legge tutto come stringa.
- on_bad_lines='skip', # Se una riga del CSV è corrotta la salta.
+ dtype=str,
+ on_bad_lines='skip',
encoding='utf-8'
)
else:
df = pd.read_excel(file_path, dtype=str)
+ # --- PATCH DIMENSIONS (Risoluzione del preambolo) ---
+ if source_upper == "DIMENSIONS":
+ # Se l'intestazione corretta è finita nella prima riga di dati a causa del preambolo:
+ if "Publication ID" not in df.columns:
+ # Rinomina le colonne usando la prima riga di dati
+ df.columns = df.iloc[0]
+ # Elimina la prima riga (che ormai è diventata l'intestazione)
+ df = df[1:].reset_index(drop=True)
+ # ----------------------------------------------------
+
# Sostituiamo i NaN con stringhe vuote.
df = df.fillna("")
- # Converte il DataFrame in una una lista di dizionari, dove ogni dizionario è una riga della tabella.
+ # Converte il DataFrame in una una lista di dizionari.
return df.to_dict(orient="records")
except pd.errors.EmptyDataError:
print(f"[ERRORE] Il file '{file_path}' è vuoto.")
return []
-
except Exception as e:
print(f"[ERRORE] Impossibile leggere il file tabellare: {e}")
return []
diff --git a/www/services/risultati_finali.csv b/www/services/risultati_finali.csv
deleted file mode 100644
index 08bfc680..00000000
--- a/www/services/risultati_finali.csv
+++ /dev/null
@@ -1,101 +0,0 @@
-DB,UT,DI,PMID,TI,SO,JI,PY,DT,LA,RP,AB,VL,IS,BP,EP,SR,TC,AU,AF,C1,CR,DE,ID
-OPENALEX,https://openalex.org/W3198357836,https://doi.org/10.1016/j.jbef.2021.100577,,"Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis",JOURNAL OF BEHAVIORAL AND EXPERIMENTAL FINANCE,JOURNAL OF BEHAVIORAL AND EXPERIMENTAL FINANCE,2021,article,en,University of Akron,,32,,100577,100577,"Goodell, 2021, JOURNAL OF BEHAVIORAL AND EXPERIMENTAL FINANCE",658,"Goodell, John W.;Kumar, Satish;Lim, Weng Marc;Pattnaik, Debidutta","Goodell, John W.;Kumar, Satish;Lim, Weng Marc;Pattnaik, Debidutta",University of Akron;Malaviya National Institute of Technology Jaipur;Swinburne University of Technology Sarawak Campus;Woxsen School of Business,https://openalex.org/W1565746575;https://openalex.org/W1648383461;https://openalex.org/W1963859450;https://openalex.org/W1968560054;https://openalex.org/W1970140636;https://openalex.org/W1970859146;https://openalex.org/W1975584591;https://openalex.org/W1995800062;https://openalex.org/W2004076523;https://openalex.org/W2005207065;https://openalex.org/W2005311637;https://openalex.org/W2015950201;https://openalex.org/W2021993444;https://openalex.org/W2028618347;https://openalex.org/W2033522691;https://openalex.org/W2035285495;https://openalex.org/W2048658075;https://openalex.org/W2051235345;https://openalex.org/W2064270391;https://openalex.org/W2064978316;https://openalex.org/W2069009481;https://openalex.org/W2071288491;https://openalex.org/W2076738050;https://openalex.org/W2077791698;https://openalex.org/W2078301133;https://openalex.org/W2078684405;https://openalex.org/W2083862258;https://openalex.org/W2085573882;https://openalex.org/W2090968438;https://openalex.org/W2093195672;https://openalex.org/W2094665138;https://openalex.org/W2095629301;https://openalex.org/W2113769477;https://openalex.org/W2124532504;https://openalex.org/W2131681506;https://openalex.org/W2131773668;https://openalex.org/W2132966115;https://openalex.org/W2136120210;https://openalex.org/W2145482038;https://openalex.org/W2147824299;https://openalex.org/W2149509893;https://openalex.org/W2154210517;https://openalex.org/W2158339117;https://openalex.org/W2171468534;https://openalex.org/W2172852798;https://openalex.org/W2222723904;https://openalex.org/W2222916728;https://openalex.org/W2232810130;https://openalex.org/W2275696275;https://openalex.org/W2297801999;https://openalex.org/W2346862349;https://openalex.org/W2492054430;https://openalex.org/W2521494838;https://openalex.org/W2529087958;https://openalex.org/W2553031590;https://openalex.org/W2557567230;https://openalex.org/W2574011124;https://openalex.org/W2593613340;https://openalex.org/W2602868873;https://openalex.org/W2610250061;https://openalex.org/W2735575534;https://openalex.org/W2740605354;https://openalex.org/W2762466482;https://openalex.org/W2790822776;https://openalex.org/W2794880420;https://openalex.org/W2802685835;https://openalex.org/W2807909115;https://openalex.org/W2810156540;https://openalex.org/W2884544303;https://openalex.org/W2888056875;https://openalex.org/W2897791100;https://openalex.org/W2904224565;https://openalex.org/W2906573737;https://openalex.org/W2911964244;https://openalex.org/W2947060296;https://openalex.org/W2957520325;https://openalex.org/W2963453445;https://openalex.org/W2963751193;https://openalex.org/W2973020765;https://openalex.org/W2983541357;https://openalex.org/W2994445360;https://openalex.org/W2996608372;https://openalex.org/W3000438457;https://openalex.org/W3000582720;https://openalex.org/W3003204057;https://openalex.org/W3013063141;https://openalex.org/W3013505582;https://openalex.org/W3015889394;https://openalex.org/W3022076500;https://openalex.org/W3023036503;https://openalex.org/W3034960190;https://openalex.org/W3036262830;https://openalex.org/W3038368984;https://openalex.org/W3039271964;https://openalex.org/W3081258743;https://openalex.org/W3086312560;https://openalex.org/W3089252064;https://openalex.org/W3092199418;https://openalex.org/W3092415316;https://openalex.org/W3093799916;https://openalex.org/W3093853589;https://openalex.org/W3096690806;https://openalex.org/W3099768174;https://openalex.org/W3120298458;https://openalex.org/W3121138196;https://openalex.org/W3121451803;https://openalex.org/W3121545263;https://openalex.org/W3121664121;https://openalex.org/W3122563224;https://openalex.org/W3122628491;https://openalex.org/W3122752921;https://openalex.org/W3122944446;https://openalex.org/W3123286026;https://openalex.org/W3123726371;https://openalex.org/W3123807607;https://openalex.org/W3125591525;https://openalex.org/W3125707221;https://openalex.org/W3125952890;https://openalex.org/W3126053622;https://openalex.org/W3126729572;https://openalex.org/W3126911807;https://openalex.org/W3128244637;https://openalex.org/W3129724093;https://openalex.org/W3131436671;https://openalex.org/W3134642500;https://openalex.org/W3145296828;https://openalex.org/W3160856016;https://openalex.org/W3185262611;https://openalex.org/W3196384457;https://openalex.org/W4205539948;https://openalex.org/W4206045084;https://openalex.org/W4211170237;https://openalex.org/W4214825689;https://openalex.org/W4231546411;https://openalex.org/W4247451115;https://openalex.org/W4255497883;https://openalex.org/W4287684164;https://openalex.org/W6601893370;https://openalex.org/W6695147765;https://openalex.org/W6723153376;https://openalex.org/W6741094427;https://openalex.org/W6749868668;https://openalex.org/W6754229210;https://openalex.org/W6754995310;https://openalex.org/W6767779838;https://openalex.org/W6770736415;https://openalex.org/W6776778857;https://openalex.org/W6788504199;https://openalex.org/W6789700754;https://openalex.org/W6791347996,Scholarship;Bibliographic coupling;Valuation (finance);Corporate finance;Finance;Citation;Portfolio;Artificial intelligence;Sociology;Economics;Computer science;Library science,Financial Markets and Investment Strategies;Stock Market Forecasting Methods;Market Dynamics and Volatility
-OPENALEX,https://openalex.org/W4224037372,https://doi.org/10.1016/j.ribaf.2022.101646,,Artificial intelligence and machine learning in finance: A bibliometric review,RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE,RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE,2022,review,en,Philadelphia University,,61,,101646,101646,"Ahmed, 2022, RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE",409,"Ahmed, Shamima;Alshater, Muneer M.;Ammari, Anis El;Hammami, Helmi","Ahmed, Shamima;Alshater, Muneer M.;Ammari, Anis El;Hammami, Helmi",Philadelphia University;Liwa College;University of Monastir;École Supérieure de Commerce de Rennes,https://openalex.org/W623237932;https://openalex.org/W1678356000;https://openalex.org/W1949087994;https://openalex.org/W1964159032;https://openalex.org/W1977627101;https://openalex.org/W1982629662;https://openalex.org/W1983476407;https://openalex.org/W1986968751;https://openalex.org/W1991989348;https://openalex.org/W1995281062;https://openalex.org/W2000295574;https://openalex.org/W2005596732;https://openalex.org/W2020245109;https://openalex.org/W2048801439;https://openalex.org/W2071096576;https://openalex.org/W2078774137;https://openalex.org/W2085831731;https://openalex.org/W2103467996;https://openalex.org/W2105973145;https://openalex.org/W2121970262;https://openalex.org/W2124532504;https://openalex.org/W2124617452;https://openalex.org/W2125943170;https://openalex.org/W2126434678;https://openalex.org/W2150220236;https://openalex.org/W2161625377;https://openalex.org/W2275696275;https://openalex.org/W2490971013;https://openalex.org/W2510541067;https://openalex.org/W2580253239;https://openalex.org/W2598046176;https://openalex.org/W2604829436;https://openalex.org/W2605413713;https://openalex.org/W2743470191;https://openalex.org/W2755950973;https://openalex.org/W2767307339;https://openalex.org/W2790797354;https://openalex.org/W2791624324;https://openalex.org/W2801421082;https://openalex.org/W2918642940;https://openalex.org/W2937631243;https://openalex.org/W2938504806;https://openalex.org/W2979085846;https://openalex.org/W2980944511;https://openalex.org/W2991273648;https://openalex.org/W2992584342;https://openalex.org/W3001272657;https://openalex.org/W3003204057;https://openalex.org/W3004424255;https://openalex.org/W3033217760;https://openalex.org/W3036262830;https://openalex.org/W3045742910;https://openalex.org/W3047520959;https://openalex.org/W3081171087;https://openalex.org/W3094829982;https://openalex.org/W3107898512;https://openalex.org/W3108051016;https://openalex.org/W3111916360;https://openalex.org/W3121532596;https://openalex.org/W3121588992;https://openalex.org/W3121662370;https://openalex.org/W3121759971;https://openalex.org/W3121822240;https://openalex.org/W3122648113;https://openalex.org/W3126999983;https://openalex.org/W3128501064;https://openalex.org/W3128601027;https://openalex.org/W3133357608;https://openalex.org/W3135338132;https://openalex.org/W3156409915;https://openalex.org/W3160856016;https://openalex.org/W3181448069;https://openalex.org/W3198357836;https://openalex.org/W3204670277;https://openalex.org/W4231591459;https://openalex.org/W4286815637;https://openalex.org/W4404193242;https://openalex.org/W6637404493;https://openalex.org/W6646488788;https://openalex.org/W6675975338;https://openalex.org/W6695147765;https://openalex.org/W6735566887;https://openalex.org/W6749152699;https://openalex.org/W6776408548;https://openalex.org/W6786268635;https://openalex.org/W6786792165;https://openalex.org/W6787144041;https://openalex.org/W6789892906,Scopus;Bankruptcy;Big data;Corporate finance;China;Finance;Computational finance;Artificial intelligence;Computer science;Data science;Economics;Political science;Data mining,Financial Distress and Bankruptcy Prediction;Stock Market Forecasting Methods;Financial Markets and Investment Strategies
-OPENALEX,https://openalex.org/W3032935427,https://doi.org/10.21873/invivo.11951,https://pubmed.ncbi.nlm.nih.gov/32503819,Coronavirus Disease (COVID-19): A Machine Learning Bibliometric Analysis,IN VIVO,IN VIVO,2020,review,en,Sapienza University of Rome,"BACKGROUND/AIM: To evaluate the research trends in coronavirus disease (COVID-19). MATERIALS AND METHODS: A bibliometric analysis was performed using a machine learning bibliometric methodology. Information regarding publication outputs, countries, institutions, journals, keywords, funding and citation counts was retrieved from Scopus database. RESULTS: A total of 1883 eligible papers were returned. An exponential increase in the COVID-19 publications occurred in the last months. As expected, China produced the majority of articles, followed by the United States of America, the United Kingdom and Italy. There is greater collaboration between highly contributing authors and institutions. The ""BMJ"" published the highest number of papers (n=129) and ""The Lancet"" had the most citations (n=1439). The most ubiquitous topic was COVID-19 clinical features. CONCLUSION: This bibliometric analysis presents the most influential references related to COVID-19 during this time and could be useful to improve understanding and management of COVID-19.",34,3 suppl,1613,1617,"Felice, 2020, IN VIVO",124,"Felice, Francesca De;Polimeni, Antonella","Felice, Francesca De;Polimeni, Antonella",Sapienza University of Rome;Policlinico Umberto I,https://openalex.org/W3003217347;https://openalex.org/W3003465021;https://openalex.org/W3004239190;https://openalex.org/W3004280078;https://openalex.org/W3004318991;https://openalex.org/W3008028633;https://openalex.org/W3014249633,Coronavirus disease 2019 (COVID-19);2019-20 coronavirus outbreak;Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2);Coronavirus;Pandemic;Virology;Disease;Coronavirus Infections;Betacoronavirus;Medicine;Computer science;Computational biology;Infectious disease (medical specialty);Biology;Outbreak;Pathology,COVID-19 Clinical Research Studies;Long-Term Effects of COVID-19;Academic Publishing and Open Access
-OPENALEX,https://openalex.org/W3135727734,https://doi.org/10.2196/24870,https://pubmed.ncbi.nlm.nih.gov/33683209,Machine Learning for Mental Health in Social Media: Bibliometric Study,JOURNAL OF MEDICAL INTERNET RESEARCH,JOURNAL OF MEDICAL INTERNET RESEARCH,2021,review,en,Sungkyunkwan University,"BACKGROUND: Social media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention. OBJECTIVE: We aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media. METHODS: Publications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described. RESULTS: We obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with Lecture Notes in Computer Science and Journal of Medical Internet Research as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated. CONCLUSIONS: The results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners.",23,3,e24870,e24870,"Kim, 2021, JOURNAL OF MEDICAL INTERNET RESEARCH",132,"Kim, Jina;Lee, Daeun;Park, Eunil","Kim, Jina;Lee, Daeun;Park, Eunil",Sungkyunkwan University,https://openalex.org/W1554040650;https://openalex.org/W2023136833;https://openalex.org/W2068264290;https://openalex.org/W2092598885;https://openalex.org/W2117699623;https://openalex.org/W2162051395;https://openalex.org/W2311329665;https://openalex.org/W2553776800;https://openalex.org/W2602628430;https://openalex.org/W2612630960;https://openalex.org/W2619542576;https://openalex.org/W2725890240;https://openalex.org/W2729540173;https://openalex.org/W2750994301;https://openalex.org/W2767870452;https://openalex.org/W2780483464;https://openalex.org/W2786077666;https://openalex.org/W2792479193;https://openalex.org/W2883944442;https://openalex.org/W2889391310;https://openalex.org/W2895763047;https://openalex.org/W2896750841;https://openalex.org/W2912581524;https://openalex.org/W2912654919;https://openalex.org/W2916048747;https://openalex.org/W2953301966;https://openalex.org/W2953532875;https://openalex.org/W2959711199;https://openalex.org/W2979610116;https://openalex.org/W2986704516;https://openalex.org/W2988595016;https://openalex.org/W2995608792;https://openalex.org/W2996219887;https://openalex.org/W3013908145;https://openalex.org/W3018892856;https://openalex.org/W3040470474;https://openalex.org/W3043553083,Scopus;Social media;Mental health;The Internet;Ranking (information retrieval);Citation;Bibliometrics;Field (mathematics);Productivity;Computer science;Data science;Scale (ratio);Medical education;Psychology;World Wide Web;MEDLINE;Medicine;Information retrieval;Political science;Psychiatry,Mental Health via Writing;Digital Mental Health Interventions;Social Media in Health Education
-OPENALEX,https://openalex.org/W4389670785,https://doi.org/10.1016/j.heliyon.2023.e23492,https://pubmed.ncbi.nlm.nih.gov/38187262,Applications of artificial intelligence and machine learning in the financial services industry: A bibliometric review,HELIYON,HELIYON,2023,review,en,International Management Institute,"This bibliometric review examines the research state of artificial intelligence (AI) and machine learning (ML) applications in the Banking, Financial Services, and Insurance (BFSI) sector. The study focuses on Scopus-indexed articles to identify key research clusters. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, 39,498 articles were screened, resulting in 1045 articles meeting the inclusion criteria. N-gram analysis identified 177 unique terms in the article titles and abstracts. Co-occurrence analysis revealed nine distinct clusters covering fintech, risk management, anti-money laundering, and actuarial science, among others. These clusters offer a comprehensive overview of the multifaceted research landscape. The identified clusters can guide future research and inform study design. Policymakers, researchers, and practitioners in the BFSI sector can benefit from the study's findings, which identify research gaps and opportunities. This study contributes to the growing literature on bibliometrics, providing insights into AI and ML applications in the BFSI sector. The findings have practical implications, advancing our understanding of AI and ML's role in benefiting academia and industry.",10,1,e23492,e23492,"Pattnaik, 2023, HELIYON",112,"Pattnaik, Debidutta;Ray, Sougata;Raman, Raghu","Pattnaik, Debidutta;Ray, Sougata;Raman, Raghu",International Management Institute;Amrita Vishwa Vidyapeetham,https://openalex.org/W1896042795;https://openalex.org/W1965577080;https://openalex.org/W1971757271;https://openalex.org/W2022605399;https://openalex.org/W2037378589;https://openalex.org/W2038295678;https://openalex.org/W2039526160;https://openalex.org/W2060249914;https://openalex.org/W2064648688;https://openalex.org/W2064946026;https://openalex.org/W2084582960;https://openalex.org/W2106997360;https://openalex.org/W2143297831;https://openalex.org/W2143723415;https://openalex.org/W2147448014;https://openalex.org/W2151044110;https://openalex.org/W2156742813;https://openalex.org/W2171656377;https://openalex.org/W2185896400;https://openalex.org/W2287059042;https://openalex.org/W2330735624;https://openalex.org/W2518773311;https://openalex.org/W2612196085;https://openalex.org/W2742460930;https://openalex.org/W2767795179;https://openalex.org/W2780299545;https://openalex.org/W2795321837;https://openalex.org/W2799494244;https://openalex.org/W2804327630;https://openalex.org/W2883964862;https://openalex.org/W2890434712;https://openalex.org/W2898505289;https://openalex.org/W2900285243;https://openalex.org/W2919652104;https://openalex.org/W2953178564;https://openalex.org/W2964266530;https://openalex.org/W2977520682;https://openalex.org/W2978896080;https://openalex.org/W2984254864;https://openalex.org/W3007615437;https://openalex.org/W3007883824;https://openalex.org/W3026280043;https://openalex.org/W3026650415;https://openalex.org/W3046629791;https://openalex.org/W3047230602;https://openalex.org/W3047520959;https://openalex.org/W3085053389;https://openalex.org/W3086924043;https://openalex.org/W3091163231;https://openalex.org/W3092542259;https://openalex.org/W3093195402;https://openalex.org/W3100431062;https://openalex.org/W3109489865;https://openalex.org/W3112354324;https://openalex.org/W3112896429;https://openalex.org/W3115683849;https://openalex.org/W3116796363;https://openalex.org/W3117086259;https://openalex.org/W3120298458;https://openalex.org/W3122739625;https://openalex.org/W3123594716;https://openalex.org/W3125211656;https://openalex.org/W3125328255;https://openalex.org/W3125895112;https://openalex.org/W3126288553;https://openalex.org/W3133970970;https://openalex.org/W3134008570;https://openalex.org/W3134642500;https://openalex.org/W3135152838;https://openalex.org/W3135420303;https://openalex.org/W3136631171;https://openalex.org/W3139261556;https://openalex.org/W3146142859;https://openalex.org/W3148740534;https://openalex.org/W3149488304;https://openalex.org/W3165478349;https://openalex.org/W3166472517;https://openalex.org/W3168104213;https://openalex.org/W3177828825;https://openalex.org/W3184840371;https://openalex.org/W3185964676;https://openalex.org/W3198357836;https://openalex.org/W3198495944;https://openalex.org/W3199461169;https://openalex.org/W3199592373;https://openalex.org/W3209643071;https://openalex.org/W3212160720;https://openalex.org/W3214758903;https://openalex.org/W4200236940;https://openalex.org/W4200247608;https://openalex.org/W4200333906;https://openalex.org/W4205145754;https://openalex.org/W4205200408;https://openalex.org/W4205359906;https://openalex.org/W4205781500;https://openalex.org/W4205844635;https://openalex.org/W4207027020;https://openalex.org/W4210669237;https://openalex.org/W4210859647;https://openalex.org/W4213415872;https://openalex.org/W4220755099;https://openalex.org/W4220905253;https://openalex.org/W4220968399;https://openalex.org/W4221044666;https://openalex.org/W4224272950;https://openalex.org/W4225143248;https://openalex.org/W4229375993;https://openalex.org/W4280617747;https://openalex.org/W4281256213;https://openalex.org/W4281651535;https://openalex.org/W4281712444;https://openalex.org/W4281858363;https://openalex.org/W4281878156;https://openalex.org/W4286267577;https://openalex.org/W4288073997;https://openalex.org/W4313478940;https://openalex.org/W4323928863;https://openalex.org/W4385155135;https://openalex.org/W4385579627;https://openalex.org/W4385732811;https://openalex.org/W4386246508;https://openalex.org/W6643206498;https://openalex.org/W6685233297;https://openalex.org/W6755670483;https://openalex.org/W6781267923;https://openalex.org/W6787097816;https://openalex.org/W6804571031;https://openalex.org/W6806029913;https://openalex.org/W6838752380;https://openalex.org/W6840134568;https://openalex.org/W7029865937,Scopus;Bibliometrics;Financial services;Systematic review;Web of science;Original research;Computer science;Business;MEDLINE;Political science;Library science;Finance,"FinTech, Crowdfunding, Digital Finance;Blockchain Technology Applications and Security;Imbalanced Data Classification Techniques"
-OPENALEX,https://openalex.org/W4405131368,https://doi.org/10.1007/978-3-031-73545-5_121,,The Impact of Machine Learning on Business Processes: A Bibliometric Analysis,"STUDIES IN SYSTEMS, DECISION AND CONTROL","STUDIES IN SYSTEMS, DECISION AND CONTROL",2024,book-chapter,en,Petra University,,,,1299,1309,"Aburub, 2024, STUDIES IN SYSTEMS, DECISION AND CONTROL",89,"Aburub, Faisal;Mohammad, Sulieman Ibraheem;Jahmani, Khaldoon;Vasudevan, Asokan;Al-Momani, Ala’a M.;Barhoom, Firas Nawwaf Ibraheem;Alrfai, Mohammad Motasem;Alzyoud, Mazen;Mohammad, Abdullah Ibrahim","Aburub, Faisal;Mohammad, Sulieman Ibraheem;Jahmani, Khaldoon;Vasudevan, Asokan;Al-Momani, Ala’a M.;Barhoom, Firas Nawwaf Ibraheem;Alrfai, Mohammad Motasem;Alzyoud, Mazen;Mohammad, Abdullah Ibrahim",Petra University;INTI International University;Zarqa University;Jadara University;Amman Arab University;Universiti Sains Malaysia;Irbid National University;Al al-Bayt University;Al-Balqa Applied University,https://openalex.org/W2147371011;https://openalex.org/W3084813718;https://openalex.org/W3160856016;https://openalex.org/W3170725296;https://openalex.org/W4205141813;https://openalex.org/W4206929493;https://openalex.org/W4210285549;https://openalex.org/W4226213040;https://openalex.org/W4226216859;https://openalex.org/W4226296954;https://openalex.org/W4239059848;https://openalex.org/W4256671596;https://openalex.org/W4283793198;https://openalex.org/W4285224342;https://openalex.org/W4290755137;https://openalex.org/W4292959163;https://openalex.org/W4292959208;https://openalex.org/W4293214651;https://openalex.org/W4294636343;https://openalex.org/W4304606386;https://openalex.org/W4304606464;https://openalex.org/W4309040008;https://openalex.org/W4309582879;https://openalex.org/W4320011710;https://openalex.org/W4320149785;https://openalex.org/W4321499414;https://openalex.org/W4322733823;https://openalex.org/W4328024576;https://openalex.org/W4328024750;https://openalex.org/W4360778050;https://openalex.org/W4376622728;https://openalex.org/W4385671851;https://openalex.org/W4386010746;https://openalex.org/W4388004267;https://openalex.org/W4388029131;https://openalex.org/W4388180055;https://openalex.org/W4390747040;https://openalex.org/W4390747253;https://openalex.org/W4390974949;https://openalex.org/W4399087030;https://openalex.org/W4399087083;https://openalex.org/W4399087105;https://openalex.org/W4399087177;https://openalex.org/W4399087399;https://openalex.org/W4399087412;https://openalex.org/W4399109533;https://openalex.org/W4399250438;https://openalex.org/W4399250471;https://openalex.org/W4399250488;https://openalex.org/W4399250506;https://openalex.org/W4399250524;https://openalex.org/W4399250528;https://openalex.org/W4399260120;https://openalex.org/W4399260158;https://openalex.org/W4399260446;https://openalex.org/W4401388217;https://openalex.org/W4401388396;https://openalex.org/W4401388738;https://openalex.org/W4401389382;https://openalex.org/W4406593706;https://openalex.org/W4406593728;https://openalex.org/W4406593761;https://openalex.org/W4406593811;https://openalex.org/W4406593830;https://openalex.org/W4406593855;https://openalex.org/W4406593889;https://openalex.org/W4406677734;https://openalex.org/W4412586642;https://openalex.org/W4412616985;https://openalex.org/W4412616995;https://openalex.org/W4412617123;https://openalex.org/W4412617196,Computer science,Organizational and Employee Performance;Cyberloafing and Workplace Behavior;Technology Adoption and User Behaviour
-OPENALEX,https://openalex.org/W3181204626,https://doi.org/10.1016/j.eswa.2021.115561,,Big data analytics and machine learning: A retrospective overview and bibliometric analysis,EXPERT SYSTEMS WITH APPLICATIONS,EXPERT SYSTEMS WITH APPLICATIONS,2021,article,en,University of North Florida,,184,,115561,115561,"Zhang, 2021, EXPERT SYSTEMS WITH APPLICATIONS",119,"Zhang, Zuopeng;Srivastava, Praveen Ranjan;Sharma, Dheeraj;Eachempati, Prajwal","Zhang, Zuopeng;Srivastava, Praveen Ranjan;Sharma, Dheeraj;Eachempati, Prajwal",University of North Florida;Indian Institute of Management Rohtak,https://openalex.org/W40420203;https://openalex.org/W88484647;https://openalex.org/W626313615;https://openalex.org/W1494137514;https://openalex.org/W1601795611;https://openalex.org/W1663973292;https://openalex.org/W1755227063;https://openalex.org/W1774848501;https://openalex.org/W1911451788;https://openalex.org/W2007343074;https://openalex.org/W2015846187;https://openalex.org/W2021324335;https://openalex.org/W2024237844;https://openalex.org/W2064675550;https://openalex.org/W2066636486;https://openalex.org/W2098615148;https://openalex.org/W2101234009;https://openalex.org/W2105103777;https://openalex.org/W2110646369;https://openalex.org/W2120751691;https://openalex.org/W2136922672;https://openalex.org/W2141975087;https://openalex.org/W2159128662;https://openalex.org/W2165093166;https://openalex.org/W2171468534;https://openalex.org/W2171469118;https://openalex.org/W2173213060;https://openalex.org/W2191867853;https://openalex.org/W2261525379;https://openalex.org/W2302800291;https://openalex.org/W2340139852;https://openalex.org/W2404353601;https://openalex.org/W2412782625;https://openalex.org/W2486221806;https://openalex.org/W2487200295;https://openalex.org/W2508563792;https://openalex.org/W2540365088;https://openalex.org/W2574134800;https://openalex.org/W2574572133;https://openalex.org/W2578336118;https://openalex.org/W2586702902;https://openalex.org/W2590273312;https://openalex.org/W2598484699;https://openalex.org/W2606916050;https://openalex.org/W2614355139;https://openalex.org/W2693176153;https://openalex.org/W2740098507;https://openalex.org/W2747765175;https://openalex.org/W2751427740;https://openalex.org/W2752267564;https://openalex.org/W2755950973;https://openalex.org/W2762694993;https://openalex.org/W2768534111;https://openalex.org/W2772164149;https://openalex.org/W2785537869;https://openalex.org/W2799902558;https://openalex.org/W2896298459;https://openalex.org/W2898861515;https://openalex.org/W2900562530;https://openalex.org/W2906422317;https://openalex.org/W2913077324;https://openalex.org/W2922441867;https://openalex.org/W2945940803;https://openalex.org/W2950775047;https://openalex.org/W2951942892;https://openalex.org/W2953501988;https://openalex.org/W2955285339;https://openalex.org/W2965780400;https://openalex.org/W2969018281;https://openalex.org/W2973556997;https://openalex.org/W2973734499;https://openalex.org/W2974124990;https://openalex.org/W2974678924;https://openalex.org/W2976017899;https://openalex.org/W2977926554;https://openalex.org/W2979610116;https://openalex.org/W2985684656;https://openalex.org/W2989739077;https://openalex.org/W2990450011;https://openalex.org/W2996745037;https://openalex.org/W3000910650;https://openalex.org/W3001554335;https://openalex.org/W3004906665;https://openalex.org/W3007404761;https://openalex.org/W3011931926;https://openalex.org/W3013863638;https://openalex.org/W3024511269;https://openalex.org/W3028022888;https://openalex.org/W3039091139;https://openalex.org/W3042710080;https://openalex.org/W3046653697;https://openalex.org/W3080913451;https://openalex.org/W3121177474;https://openalex.org/W3122944446;https://openalex.org/W3123115705;https://openalex.org/W3123613287;https://openalex.org/W3140136943;https://openalex.org/W3150796314;https://openalex.org/W3175452479;https://openalex.org/W3186766739;https://openalex.org/W3193793166;https://openalex.org/W4285719527;https://openalex.org/W6602586656;https://openalex.org/W6624503512;https://openalex.org/W6631138889;https://openalex.org/W6637618429;https://openalex.org/W6675354045;https://openalex.org/W6676693144;https://openalex.org/W6713691922;https://openalex.org/W6741640790;https://openalex.org/W6756345613;https://openalex.org/W6764905802;https://openalex.org/W6768162700;https://openalex.org/W6768420565;https://openalex.org/W6780797579,Big data;Scopus;Computer science;Data science;LEAPS;Bibliometrics;Learning analytics;Cluster (spacecraft);Analytics;Cloud computing;Cluster analysis;The Internet;Bibliographic coupling;Discipline;Sample (material);Data mining;World Wide Web;Artificial intelligence;Social science;Sociology;Citation;Political science,Big Data and Business Intelligence;Blockchain Technology Applications and Security;Data Quality and Management
-OPENALEX,https://openalex.org/W2797694788,https://doi.org/10.1016/j.cmpb.2018.04.005,https://pubmed.ncbi.nlm.nih.gov/29852952,Deep learning for healthcare applications based on physiological signals: A review,COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,2018,review,en,Sheffield Hallam University,,161,,1,13,"Faust, 2018, COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE",1014,"Faust, Oliver;Hagiwara, Yuki;Hong, Tan Jen;Lih, Oh Shu;Acharya, U. Rajendra","Faust, Oliver;Hagiwara, Yuki;Hong, Tan Jen;Lih, Oh Shu;Acharya, U. Rajendra",Sheffield Hallam University;Ngee Ann Polytechnic;University of Malaya;Singapore University of Social Sciences,https://openalex.org/W60493759;https://openalex.org/W89197320;https://openalex.org/W119403003;https://openalex.org/W121410702;https://openalex.org/W140777655;https://openalex.org/W150292108;https://openalex.org/W197865394;https://openalex.org/W210506677;https://openalex.org/W363355628;https://openalex.org/W599959547;https://openalex.org/W1480980134;https://openalex.org/W1490308602;https://openalex.org/W1491697777;https://openalex.org/W1495061682;https://openalex.org/W1528285574;https://openalex.org/W1538131130;https://openalex.org/W1551271717;https://openalex.org/W1603219075;https://openalex.org/W1617232655;https://openalex.org/W1813659000;https://openalex.org/W1819910625;https://openalex.org/W1883664232;https://openalex.org/W1916292342;https://openalex.org/W1948981500;https://openalex.org/W1966262416;https://openalex.org/W1968274879;https://openalex.org/W1972148060;https://openalex.org/W1977210227;https://openalex.org/W1977931720;https://openalex.org/W1984020445;https://openalex.org/W1994422401;https://openalex.org/W1998523455;https://openalex.org/W2002055708;https://openalex.org/W2004104731;https://openalex.org/W2007945931;https://openalex.org/W2009787667;https://openalex.org/W2017337590;https://openalex.org/W2026430219;https://openalex.org/W2032536435;https://openalex.org/W2036801659;https://openalex.org/W2038569194;https://openalex.org/W2042716610;https://openalex.org/W2043596210;https://openalex.org/W2044170013;https://openalex.org/W2044455804;https://openalex.org/W2050265252;https://openalex.org/W2055735905;https://openalex.org/W2055821135;https://openalex.org/W2064675550;https://openalex.org/W2070804684;https://openalex.org/W2082480549;https://openalex.org/W2084174584;https://openalex.org/W2089034242;https://openalex.org/W2095409369;https://openalex.org/W2099579406;https://openalex.org/W2099899182;https://openalex.org/W2100495367;https://openalex.org/W2101591109;https://openalex.org/W2103308415;https://openalex.org/W2105577021;https://openalex.org/W2109930285;https://openalex.org/W2110798204;https://openalex.org/W2112796928;https://openalex.org/W2113141977;https://openalex.org/W2116570678;https://openalex.org/W2118023920;https://openalex.org/W2119351517;https://openalex.org/W2126698740;https://openalex.org/W2127430888;https://openalex.org/W2128935152;https://openalex.org/W2131103247;https://openalex.org/W2131442495;https://openalex.org/W2131740154;https://openalex.org/W2133565549;https://openalex.org/W2133693888;https://openalex.org/W2136897104;https://openalex.org/W2136922672;https://openalex.org/W2137317612;https://openalex.org/W2138580453;https://openalex.org/W2138857742;https://openalex.org/W2141330748;https://openalex.org/W2144691514;https://openalex.org/W2150665176;https://openalex.org/W2150765527;https://openalex.org/W2153677276;https://openalex.org/W2153912116;https://openalex.org/W2156694087;https://openalex.org/W2162480886;https://openalex.org/W2162800060;https://openalex.org/W2164082066;https://openalex.org/W2164179736;https://openalex.org/W2164700406;https://openalex.org/W2165061769;https://openalex.org/W2165611870;https://openalex.org/W2168231600;https://openalex.org/W2169812774;https://openalex.org/W2169931829;https://openalex.org/W2170635294;https://openalex.org/W2176823577;https://openalex.org/W2181785117;https://openalex.org/W2202063965;https://openalex.org/W2244991220;https://openalex.org/W2254715784;https://openalex.org/W2273832011;https://openalex.org/W2277546892;https://openalex.org/W2291961022;https://openalex.org/W2293585160;https://openalex.org/W2311607323;https://openalex.org/W2314438496;https://openalex.org/W2322503732;https://openalex.org/W2323693010;https://openalex.org/W2328786298;https://openalex.org/W2329160650;https://openalex.org/W2334909404;https://openalex.org/W2337970775;https://openalex.org/W2342619534;https://openalex.org/W2395817516;https://openalex.org/W2411311483;https://openalex.org/W2414309931;https://openalex.org/W2488164446;https://openalex.org/W2504629029;https://openalex.org/W2507528282;https://openalex.org/W2508171209;https://openalex.org/W2516710120;https://openalex.org/W2518549849;https://openalex.org/W2527796983;https://openalex.org/W2545231353;https://openalex.org/W2555541061;https://openalex.org/W2557283755;https://openalex.org/W2559655401;https://openalex.org/W2561645127;https://openalex.org/W2564965427;https://openalex.org/W2584401907;https://openalex.org/W2602226095;https://openalex.org/W2604272474;https://openalex.org/W2605056515;https://openalex.org/W2606856422;https://openalex.org/W2607671730;https://openalex.org/W2610332124;https://openalex.org/W2610751394;https://openalex.org/W2617052765;https://openalex.org/W2617669016;https://openalex.org/W2621205740;https://openalex.org/W2626621447;https://openalex.org/W2684229413;https://openalex.org/W2702116941;https://openalex.org/W2735293316;https://openalex.org/W2735394685;https://openalex.org/W2741907166;https://openalex.org/W2742829803;https://openalex.org/W2743850381;https://openalex.org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learning;Computer science;Artificial intelligence;Machine learning;Quality (philosophy);Big data;Health care;Data science;Data mining,ECG Monitoring and Analysis;EEG and Brain-Computer Interfaces;Non-Invasive Vital Sign Monitoring
-OPENALEX,https://openalex.org/W3158613175,https://doi.org/10.1108/jeim-09-2020-0361,,Machine learning applications for sustainable manufacturing: a bibliometric-based review for future research,JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT,JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT,2021,review,en,Malaviya National Institute of Technology Jaipur,"Purpose The role of data analytics is significantly important in manufacturing industries as it holds the key to address sustainability challenges and handle the large amount of data generated from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM). Design/methodology/approach In the present study, the authors use a bibliometric review approach that is focused on the statistical analysis of published scientific documents with an unbiased objective of the current status and future research potential of ML applications in sustainable manufacturing. Findings The present study highlights how manufacturing industries can benefit from ML techniques when applied to address SM issues. Based on the findings, a ML-SM framework is proposed. The framework will be helpful to researchers, policymakers and practitioners to provide guidelines on the successful management of SM practices. Originality/value A comprehensive and bibliometric review of opportunities for ML techniques in SM with a framework is still limited in the available literature. This study addresses the bibliometric analysis of ML applications in SM, which further adds to the originality.",35,2,566,596,"Jamwal, 2021, JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT",97,"Jamwal, Anbesh;Agrawal, Rajeev;Sharma, Monica;Kumar, Anil;Kumar, Vikas;Garza‐Reyes, Jose Arturo","Jamwal, Anbesh;Agrawal, Rajeev;Sharma, Monica;Kumar, Anil;Kumar, Vikas;Garza‐Reyes, Jose Arturo",Malaviya National Institute of Technology Jaipur;London Metropolitan University;University of the West of England;University of Derby,https://openalex.org/W1117743625;https://openalex.org/W1975428977;https://openalex.org/W1978611080;https://openalex.org/W1982445933;https://openalex.org/W2015550419;https://openalex.org/W2024983677;https://openalex.org/W2028372401;https://openalex.org/W2028837775;https://openalex.org/W2056499735;https://openalex.org/W2061724928;https://openalex.org/W2067835942;https://openalex.org/W2069423954;https://openalex.org/W2094189035;https://openalex.org/W2098953300;https://openalex.org/W2129924558;https://openalex.org/W2133658853;https://openalex.org/W2155132830;https://openalex.org/W2163743285;https://openalex.org/W2190869063;https://openalex.org/W2276201574;https://openalex.org/W2303531378;https://openalex.org/W2366778135;https://openalex.org/W2464234006;https://openalex.org/W2531224885;https://openalex.org/W2551343411;https://openalex.org/W2574375405;https://openalex.org/W2601486059;https://openalex.org/W2742516448;https://openalex.org/W2758606443;https://openalex.org/W2763846966;https://openalex.org/W2788989613;https://openalex.org/W2791288760;https://openalex.org/W2808847377;https://openalex.org/W2885831476;https://openalex.org/W2890098390;https://openalex.org/W2891387304;https://openalex.org/W2905864531;https://openalex.org/W2916460808;https://openalex.org/W2947402339;https://openalex.org/W2947788863;https://openalex.org/W2963296061;https://openalex.org/W2965501895;https://openalex.org/W2968279542;https://openalex.org/W2969750664;https://openalex.org/W2990079491;https://openalex.org/W3004548543;https://openalex.org/W3007397514;https://openalex.org/W3033629518;https://openalex.org/W3036992121;https://openalex.org/W3039091875;https://openalex.org/W3039465098;https://openalex.org/W3039627084;https://openalex.org/W3041975366;https://openalex.org/W3042971228;https://openalex.org/W3048281029;https://openalex.org/W3113379038;https://openalex.org/W3115033935;https://openalex.org/W3120619572;https://openalex.org/W3125505924;https://openalex.org/W4211163385,Originality;Sustainability;Analytics;Bibliometrics;Management science;Computer science;Data science;Engineering;Knowledge management;Process management;Data mining;Sociology;Social science,Sustainable Supply Chain Management;Digital Transformation in Industry;Environmental Sustainability in Business
-OPENALEX,https://openalex.org/W4220862305,https://doi.org/10.3390/healthcare10030541,https://pubmed.ncbi.nlm.nih.gov/35327018,Machine-Learning-Based Disease Diagnosis: A Comprehensive Review,HEALTHCARE,HEALTHCARE,2022,review,en,University of Oklahoma,"Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area.",10,3,541,541,"Ahsan, 2022, HEALTHCARE",546,"Ahsan, Md Manjurul;Luna, Shahana Akter;Siddique, Zahed","Ahsan, Md Manjurul;Luna, Shahana Akter;Siddique, Zahed",University of Oklahoma;Dhaka Medical College and Hospital,https://openalex.org/W807187018;https://openalex.org/W1075124913;https://openalex.org/W1536883614;https://openalex.org/W1581627189;https://openalex.org/W1965746216;https://openalex.org/W1967320885;https://openalex.org/W1978639885;https://openalex.org/W1994670735;https://openalex.org/W2014418634;https://openalex.org/W2041677347;https://openalex.org/W2060947741;https://openalex.org/W2061079740;https://openalex.org/W2069914810;https://openalex.org/W2073052156;https://openalex.org/W2092594036;https://openalex.org/W2113325369;https://openalex.org/W2114536962;https://openalex.org/W2126414602;https://openalex.org/W2129374946;https://openalex.org/W2168490582;https://openalex.org/W2169384781;https://openalex.org/W2182573986;https://openalex.org/W2203920572;https://openalex.org/W2283772232;https://openalex.org/W2370924594;https://openalex.org/W2408866005;https://openalex.org/W2466438457;https://openalex.org/W2533982810;https://openalex.org/W2559256361;https://openalex.org/W2561962159;https://openalex.org/W2573624449;https://openalex.org/W2588755956;https://openalex.org/W2611248927;https://openalex.org/W2620540240;https://openalex.org/W2621028221;https://openalex.org/W2736804899;https://openalex.org/W2744692634;https://openalex.org/W2748902594;https://openalex.org/W2749212198;https://openalex.org/W2750178884;https://openalex.org/W2752073414;https://openalex.org/W2766920682;https://openalex.org/W2781824996;https://openalex.org/W2800028583;https://openalex.org/W2800197878;https://openalex.org/W2805394410;https://openalex.org/W2806091641;https://openalex.org/W2848154351;https://openalex.org/W2885568874;https://openalex.org/W2886034601;https://openalex.org/W2889556559;https://openalex.org/W2894660534;https://openalex.org/W2895906293;https://openalex.org/W2899479069;https://openalex.org/W2906622753;https://openalex.org/W2920853473;https://openalex.org/W2939881857;https://openalex.org/W2942594154;https://openalex.org/W2943491685;https://openalex.org/W2949574507;https://openalex.org/W2949767632;https://openalex.org/W2965858371;https://openalex.org/W2967240347;https://openalex.org/W2969822717;https://openalex.org/W2977192698;https://openalex.org/W2979164675;https://openalex.org/W2995209114;https://openalex.org/W2995942064;https://openalex.org/W3000707849;https://openalex.org/W3001481174;https://openalex.org/W3005486005;https://openalex.org/W3010274200;https://openalex.org/W3011149445;https://openalex.org/W3011921457;https://openalex.org/W3013507463;https://openalex.org/W3013601031;https://openalex.org/W3016610966;https://openalex.org/W3017382074;https://openalex.org/W3017644243;https://openalex.org/W3018492956;https://openalex.org/W3018662283;https://openalex.org/W3019449959;https://openalex.org/W3021399285;https://openalex.org/W3025200925;https://openalex.org/W3033712884;https://openalex.org/W3033721673;https://openalex.org/W3033732377;https://openalex.org/W3035162004;https://openalex.org/W3036032735;https://openalex.org/W3036552116;https://openalex.org/W3037666819;https://openalex.org/W3041936671;https://openalex.org/W3042127975;https://openalex.org/W3047434002;https://openalex.org/W3047700074;https://openalex.org/W3081746618;https://openalex.org/W3085331204;https://openalex.org/W3089651655;https://openalex.org/W3091940685;https://openalex.org/W3094329596;https://openalex.org/W3094377101;https://openalex.org/W3094910079;https://openalex.org/W3096802791;https://openalex.org/W3096956107;https://openalex.org/W3100981678;https://openalex.org/W3105081694;https://openalex.org/W3109097605;https://openalex.org/W3112328469;https://openalex.org/W3127668365;https://openalex.org/W3131806186;https://openalex.org/W3135243128;https://openalex.org/W3138980969;https://openalex.org/W3162351260;https://openalex.org/W3165688482;https://openalex.org/W3173933139;https://openalex.org/W3175644683;https://openalex.org/W3177781640;https://openalex.org/W3185256938;https://openalex.org/W3191228666;https://openalex.org/W3194248593;https://openalex.org/W3195607207;https://openalex.org/W3198350258;https://openalex.org/W3199499508;https://openalex.org/W3200191661;https://openalex.org/W3200757042;https://openalex.org/W3202272080;https://openalex.org/W4211214771;https://openalex.org/W4225632040;https://openalex.org/W4244895750;https://openalex.org/W4245025224;https://openalex.org/W4246554954;https://openalex.org/W6714245873;https://openalex.org/W6732181618;https://openalex.org/W6752007736;https://openalex.org/W6782621685;https://openalex.org/W6784883126,Scopus;Machine learning;Artificial intelligence;Computer science;Identification (biology);Disease;Web of science;Data science;MEDLINE;Medicine;Pathology;Meta-analysis,COVID-19 diagnosis using AI;Artificial Intelligence in Healthcare;Artificial Intelligence in Healthcare and Education
-OPENALEX,https://openalex.org/W4361299052,https://doi.org/10.1108/ejim-09-2022-0531,,Digital transformation in tourism: bibliometric literature review based on machine learning approach,EUROPEAN JOURNAL OF INNOVATION MANAGEMENT,EUROPEAN JOURNAL OF INNOVATION MANAGEMENT,2023,article,en,Comenius University Bratislava,"Purpose This bibliometric study provides an overview of research related to digital transformation (DT) in the tourism industry from 2013 to 2022. The goals of the research are as follows: (1) to identify the development of academic papers related to DT in the tourism industry, (2) to analyze dominant research topics and the development of research interest and research impact over time and (3) to analyze the change in research topics during the pandemic. Design/methodology/approach In this study, the authors processed 3,683 papers retrieved from the Web of Science and Scopus. The authors performed different types of bibliometric analyses to identify the development of papers related to DT in the tourism industry. To reveal latent topics, the authors implemented topic modeling based on latent Dirichlet allocation with Gibbs sampling. Findings The authors identified eight topics related to DT in the tourism industry: City and urban planning, Social media, Data analytics, Sustainable and economic development, Technology-based experience and interaction, Cultural heritage, Digital destination marketing and Smart tourism management. The authors also identified seven topics related to DT in the tourism industry during the Covid-19 pandemic; the largest ones are smart analytics, marketing strategies and sustainability. Originality/value To identify research topics and their development over time, the authors applied a novel methodological approach – a smart literature review. This machine learning approach is able to analyze a huge amount of documents. At the same time, it can also identify topics that would remain unrevealed by a standard bibliometric analysis.",26,7,177,205,"Madzík, 2023, EUROPEAN JOURNAL OF INNOVATION MANAGEMENT",91,"Madzík, Peter;Falát, Lukáš;Copuš, Lukáš;Valeri, Marco","Madzík, Peter;Falát, Lukáš;Copuš, Lukáš;Valeri, Marco",Comenius University Bratislava;University of Žilina;University Niccolò Cusano,https://openalex.org/W202426229;https://openalex.org/W623754725;https://openalex.org/W1084343582;https://openalex.org/W1609508549;https://openalex.org/W1826077916;https://openalex.org/W1965001395;https://openalex.org/W1977821585;https://openalex.org/W1985691092;https://openalex.org/W1986297682;https://openalex.org/W2040915741;https://openalex.org/W2044734026;https://openalex.org/W2059979238;https://openalex.org/W2063904661;https://openalex.org/W2069781291;https://openalex.org/W2077407395;https://openalex.org/W2089199234;https://openalex.org/W2108680868;https://openalex.org/W2133260307;https://openalex.org/W2143250669;https://openalex.org/W2148535836;https://openalex.org/W2150220236;https://openalex.org/W2158804744;https://openalex.org/W2161374186;https://openalex.org/W2265611211;https://openalex.org/W2284774080;https://openalex.org/W2476338024;https://openalex.org/W2499940354;https://openalex.org/W2534913524;https://openalex.org/W2567052792;https://openalex.org/W2574989679;https://openalex.org/W2606875775;https://openalex.org/W2613620747;https://openalex.org/W2735332871;https://openalex.org/W2747564439;https://openalex.org/W2768455259;https://openalex.org/W2771359204;https://openalex.org/W2772812561;https://openalex.org/W2783264548;https://openalex.org/W2785926151;https://openalex.org/W2790148163;https://openalex.org/W2884365163;https://openalex.org/W2900951404;https://openalex.org/W2902513771;https://openalex.org/W2903375072;https://openalex.org/W2908815325;https://openalex.org/W2909796576;https://openalex.org/W2912800036;https://openalex.org/W2917486745;https://openalex.org/W2937594066;https://openalex.org/W2962686197;https://openalex.org/W2970135849;https://openalex.org/W2974541658;https://openalex.org/W2978346165;https://openalex.org/W2990436242;https://openalex.org/W2991179304;https://openalex.org/W3017089210;https://openalex.org/W3020637112;https://openalex.org/W3028352916;https://openalex.org/W3033915886;https://openalex.org/W3035276234;https://openalex.org/W3035597798;https://openalex.org/W3037591541;https://openalex.org/W3038273726;https://openalex.org/W3043590597;https://openalex.org/W3091831113;https://openalex.org/W3092336047;https://openalex.org/W3094868074;https://openalex.org/W3095898096;https://openalex.org/W3105556866;https://openalex.org/W3108703075;https://openalex.org/W3118615836;https://openalex.org/W3131635118;https://openalex.org/W3155924591;https://openalex.org/W3163089655;https://openalex.org/W3192110884;https://openalex.org/W3198752715;https://openalex.org/W3208276111;https://openalex.org/W3208843561;https://openalex.org/W3213644999;https://openalex.org/W4221129123;https://openalex.org/W4230471757;https://openalex.org/W4255846079;https://openalex.org/W4281720136;https://openalex.org/W4282961548;https://openalex.org/W4283155197;https://openalex.org/W4283163087;https://openalex.org/W4283219953;https://openalex.org/W4283575946;https://openalex.org/W4283587087;https://openalex.org/W4283808859;https://openalex.org/W4291270656;https://openalex.org/W4293235962;https://openalex.org/W4296285773;https://openalex.org/W4302990361;https://openalex.org/W4306382498;https://openalex.org/W4393175063;https://openalex.org/W6650853526,Latent Dirichlet allocation;Originality;Tourism;Scopus;Topic model;Social media;Digital transformation;Analytics;Data science;Bibliometrics;Computer science;Knowledge management;Marketing;Sociology;Business;Political science;World Wide Web;Social science;Qualitative research;Artificial intelligence,Digital Marketing and Social Media;Diverse Aspects of Tourism Research;Sport and Mega-Event Impacts
-OPENALEX,https://openalex.org/W2021944660,https://doi.org/10.1007/s11192-010-0160-5,,Using content-based and bibliometric features for machine learning models to predict citation counts in the biomedical literature,SCIENTOMETRICS,SCIENTOMETRICS,2010,article,en,Columbia University Irving Medical Center,,85,1,257,270,"Fu, 2010, SCIENTOMETRICS",109,"Fu, Lawrence D.;Aliferis, Constantin","Fu, Lawrence D.;Aliferis, Constantin",Columbia University Irving Medical Center;New York University,https://openalex.org/W93570834;https://openalex.org/W1493036841;https://openalex.org/W1511527373;https://openalex.org/W1564518192;https://openalex.org/W1969666528;https://openalex.org/W2008516411;https://openalex.org/W2014147337;https://openalex.org/W2026273736;https://openalex.org/W2043465687;https://openalex.org/W2048402950;https://openalex.org/W2080547938;https://openalex.org/W2081228826;https://openalex.org/W2098162425;https://openalex.org/W2120450109;https://openalex.org/W2133091666;https://openalex.org/W2138745909;https://openalex.org/W2139212933;https://openalex.org/W2140584250;https://openalex.org/W2154703852;https://openalex.org/W4313635346;https://openalex.org/W6680669223,Citation;Computer science;Citation analysis;Information retrieval;Term (time);Data science;Machine learning;Artificial intelligence;Library science,Biomedical Text Mining and Ontologies;scientometrics and bibliometrics research;Advanced Text Analysis Techniques
-OPENALEX,https://openalex.org/W2979610116,https://doi.org/10.1016/j.cie.2019.106120,,Data mining and machine learning techniques applied to public health problems: A bibliometric analysis from 2009 to 2018,COMPUTERS & INDUSTRIAL ENGINEERING,COMPUTERS & INDUSTRIAL ENGINEERING,2019,article,en,Pontifícia Universidade Católica do Paraná,,138,,106120,106120,"Santos, 2019, COMPUTERS & INDUSTRIAL ENGINEERING",124,"Santos, Bruno Samways dos;Steiner, María Teresinha Arns;Fenerich, Amanda Trojan;Lima, Rafael Henrique Palma","Santos, Bruno Samways dos;Steiner, María Teresinha Arns;Fenerich, Amanda Trojan;Lima, Rafael Henrique Palma",Pontifícia Universidade Católica do Paraná;Universidade Tecnológica Federal do Paraná,https://openalex.org/W1512104599;https://openalex.org/W1514609038;https://openalex.org/W1562463929;https://openalex.org/W1571935154;https://openalex.org/W1573600402;https://openalex.org/W1874217810;https://openalex.org/W1929181352;https://openalex.org/W1980640719;https://openalex.org/W1988023854;https://openalex.org/W2000127653;https://openalex.org/W2020974619;https://openalex.org/W2026343919;https://openalex.org/W2036965053;https://openalex.org/W2052264970;https://openalex.org/W2053276830;https://openalex.org/W2062580623;https://openalex.org/W2079591709;https://openalex.org/W2081284664;https://openalex.org/W2088712730;https://openalex.org/W2095315710;https://openalex.org/W2102614609;https://openalex.org/W2106618845;https://openalex.org/W2107035852;https://openalex.org/W2118414527;https://openalex.org/W2120054760;https://openalex.org/W2141161236;https://openalex.org/W2147953360;https://openalex.org/W2149228247;https://openalex.org/W2151103962;https://openalex.org/W2159181336;https://openalex.org/W2159663239;https://openalex.org/W2163598528;https://openalex.org/W2168894761;https://openalex.org/W2170112669;https://openalex.org/W2171469118;https://openalex.org/W2250240141;https://openalex.org/W2471643592;https://openalex.org/W2501156986;https://openalex.org/W2519289376;https://openalex.org/W2529025493;https://openalex.org/W2553160069;https://openalex.org/W2560678864;https://openalex.org/W2565522107;https://openalex.org/W2569214105;https://openalex.org/W2587326473;https://openalex.org/W2588978790;https://openalex.org/W2606945656;https://openalex.org/W2610984530;https://openalex.org/W2614092723;https://openalex.org/W2638254231;https://openalex.org/W2732050589;https://openalex.org/W2734832579;https://openalex.org/W2739988252;https://openalex.org/W2745642918;https://openalex.org/W2751427740;https://openalex.org/W2755058106;https://openalex.org/W2765883401;https://openalex.org/W2768650472;https://openalex.org/W2769135762;https://openalex.org/W2773642388;https://openalex.org/W2775182341;https://openalex.org/W2779908660;https://openalex.org/W2782172275;https://openalex.org/W2782182435;https://openalex.org/W2790360578;https://openalex.org/W2791730151;https://openalex.org/W2792307024;https://openalex.org/W2792705987;https://openalex.org/W2796774316;https://openalex.org/W2801570955;https://openalex.org/W2810021747;https://openalex.org/W2810207020;https://openalex.org/W2810623657;https://openalex.org/W2811095531;https://openalex.org/W2883860074;https://openalex.org/W2885069035;https://openalex.org/W2887903337;https://openalex.org/W2892090722;https://openalex.org/W2894726598;https://openalex.org/W2900399448;https://openalex.org/W2901460192;https://openalex.org/W2902282961;https://openalex.org/W2905241670;https://openalex.org/W2905983446;https://openalex.org/W2906028509;https://openalex.org/W3125505924;https://openalex.org/W4232575633;https://openalex.org/W4243782038;https://openalex.org/W6633796851;https://openalex.org/W6684327724;https://openalex.org/W6684456340;https://openalex.org/W6740206376;https://openalex.org/W6746800349;https://openalex.org/W6747672686;https://openalex.org/W6755542333,Scopus;Computer science;Context (archaeology);Public health;Data science;Support vector machine;Web of science;The Internet;Field (mathematics);Bibliometrics;Data mining;Artificial intelligence;MEDLINE;World Wide Web;Medicine;Mathematics;Geography;Political science,Artificial Intelligence in Healthcare;Data-Driven Disease Surveillance;Imbalanced Data Classification Techniques
-OPENALEX,https://openalex.org/W4225394315,https://doi.org/10.33166/aetic.2022.02.002,,Machine Learning and Artificial Intelligence in Circular Economy: A Bibliometric Analysis and Systematic Literature Review,ANNALS OF EMERGING TECHNOLOGIES IN COMPUTING,ANNALS OF EMERGING TECHNOLOGIES IN COMPUTING,2022,article,en,North South University,"With unorganized, unplanned and improper use of limited raw materials, an abundant amount of waste is being produced, which is harmful to our environment and ecosystem. While traditional linear production lines fail to address far-reaching issues like waste production and a shorter product life cycle, a prospective concept, namely circular economy (CE), has shown promising prospects to be adopted at industrial and governmental levels. CE aims to complete the product life cycle loop by bringing out the highest values from raw materials in the design phase and later on by reusing, recycling, and remanufacturing. Innovative technologies like artificial intelligence (AI) and machine learning(ML) provide vital assistance in effectively adopting and implementing CE in real-world practices. This study explores the adoption and integration of applied AI techniques in CE. First, we conducted bibliometric analysis on a collection of 104 SCOPUS indexed documents exploring the critical research criteria in AI and CE. Forty papers were picked to conduct a systematic literature review from these documents. The selected documents were further divided into six categories: sustainable development, reverse logistics, waste management, supply chain management, recycle & reuse, and manufacturing development. Comprehensive research insights and trends have been extracted and delineated. Finally, the research gap needing further attention has been identified and the future research directions have also been discussed.",6,2,13,40,"Noman, 2022, ANNALS OF EMERGING TECHNOLOGIES IN COMPUTING",103,"Noman, Abdulla All;Akter, Umma Habiba;Pranto, Tahmid Hasan;Haque, AKM Bahalul","Noman, Abdulla All;Akter, Umma Habiba;Pranto, Tahmid Hasan;Haque, AKM Bahalul",North South University;Lappeenranta-Lahti University of Technology,https://openalex.org/W129336749;https://openalex.org/W1021000864;https://openalex.org/W1539532232;https://openalex.org/W1565680682;https://openalex.org/W1732240353;https://openalex.org/W1980757397;https://openalex.org/W1981678541;https://openalex.org/W1982564000;https://openalex.org/W1984377442;https://openalex.org/W2032093585;https://openalex.org/W2077139171;https://openalex.org/W2168155916;https://openalex.org/W2173791728;https://openalex.org/W2198256821;https://openalex.org/W2303531378;https://openalex.org/W2520668169;https://openalex.org/W2565277564;https://openalex.org/W2592734766;https://openalex.org/W2735575534;https://openalex.org/W2736219668;https://openalex.org/W2745880093;https://openalex.org/W2753436765;https://openalex.org/W2756283300;https://openalex.org/W2756966076;https://openalex.org/W2890522215;https://openalex.org/W2897570098;https://openalex.org/W2903445525;https://openalex.org/W2913927778;https://openalex.org/W2919810952;https://openalex.org/W2939663913;https://openalex.org/W2947011708;https://openalex.org/W2949138301;https://openalex.org/W2952577115;https://openalex.org/W2955437544;https://openalex.org/W2966506874;https://openalex.org/W2966648806;https://openalex.org/W2970869624;https://openalex.org/W2972579622;https://openalex.org/W2987161473;https://openalex.org/W2987691368;https://openalex.org/W2998431313;https://openalex.org/W2998585003;https://openalex.org/W3011287826;https://openalex.org/W3013727784;https://openalex.org/W3014334385;https://openalex.org/W3014341985;https://openalex.org/W3016773298;https://openalex.org/W3017795897;https://openalex.org/W3020159756;https://openalex.org/W3021483748;https://openalex.org/W3021514663;https://openalex.org/W3022158042;https://openalex.org/W3028508503;https://openalex.org/W3033849459;https://openalex.org/W3036543018;https://openalex.org/W3039480138;https://openalex.org/W3042647574;https://openalex.org/W3082897346;https://openalex.org/W3089215304;https://openalex.org/W3089649929;https://openalex.org/W3091532666;https://openalex.org/W3092150212;https://openalex.org/W3094805524;https://openalex.org/W3097839885;https://openalex.org/W3100920799;https://openalex.org/W3108884865;https://openalex.org/W3110398427;https://openalex.org/W3110857549;https://openalex.org/W3111762745;https://openalex.org/W3114749054;https://openalex.org/W3118633827;https://openalex.org/W3118670782;https://openalex.org/W3122358591;https://openalex.org/W3122790749;https://openalex.org/W3129523205;https://openalex.org/W3130026625;https://openalex.org/W3130226006;https://openalex.org/W3131122480;https://openalex.org/W3132330847;https://openalex.org/W3134388662;https://openalex.org/W3135028703;https://openalex.org/W3135798940;https://openalex.org/W3137357157;https://openalex.org/W3139411728;https://openalex.org/W3143158417;https://openalex.org/W3146907769;https://openalex.org/W3147956902;https://openalex.org/W3148451833;https://openalex.org/W3158947599;https://openalex.org/W3161815634;https://openalex.org/W3162044998;https://openalex.org/W3168614558;https://openalex.org/W3169653105;https://openalex.org/W3170077347;https://openalex.org/W3171226157;https://openalex.org/W3172997754;https://openalex.org/W3202972492;https://openalex.org/W4229597643;https://openalex.org/W4234321069;https://openalex.org/W4251359136;https://openalex.org/W4252562554;https://openalex.org/W4285542071;https://openalex.org/W4307061405;https://openalex.org/W4312278356,Remanufacturing;Reuse;Circular economy;Scopus;Product (mathematics);Reverse logistics;Life-cycle assessment;Supply chain;Computer science;Production (economics);New product development;Business;Artificial intelligence;Manufacturing engineering;Engineering;Marketing;Waste management;Economics;Political science;Mathematics,Sustainable Supply Chain Management;Recycling and Waste Management Techniques;Municipal Solid Waste Management
-OPENALEX,https://openalex.org/W3193226555,https://doi.org/10.1016/j.eswa.2021.115728,,A visualized bibliometric analysis of mapping research trends of machine learning in engineering (MLE),EXPERT SYSTEMS WITH APPLICATIONS,EXPERT SYSTEMS WITH APPLICATIONS,2021,article,en,"University of California, Berkeley",,186,,115728,115728,"Su, 2021, EXPERT SYSTEMS WITH APPLICATIONS",70,"Su, Miao;Peng, Hui;Li, Shaofan","Su, Miao;Peng, Hui;Li, Shaofan","University of California, Berkeley;Changsha University of Science and Technology",https://openalex.org/W1480376833;https://openalex.org/W1482021765;https://openalex.org/W1496929357;https://openalex.org/W1572181180;https://openalex.org/W1663973292;https://openalex.org/W1723619723;https://openalex.org/W1740585449;https://openalex.org/W1750490368;https://openalex.org/W1809800090;https://openalex.org/W1843099843;https://openalex.org/W1975428268;https://openalex.org/W1976405057;https://openalex.org/W1977411522;https://openalex.org/W1989906353;https://openalex.org/W1999461467;https://openalex.org/W2015648984;https://openalex.org/W2016864600;https://openalex.org/W2023261859;https://openalex.org/W2031360841;https://openalex.org/W2045732268;https://openalex.org/W2056401416;https://openalex.org/W2063922127;https://openalex.org/W2069262928;https://openalex.org/W2069929199;https://openalex.org/W2071923040;https://openalex.org/W2079390796;https://openalex.org/W2091365875;https://openalex.org/W2101234009;https://openalex.org/W2103537693;https://openalex.org/W2111072639;https://openalex.org/W2119821739;https://openalex.org/W2136922672;https://openalex.org/W2137881949;https://openalex.org/W2148603752;https://openalex.org/W2150220236;https://openalex.org/W2153635508;https://openalex.org/W2154400911;https://openalex.org/W2156909104;https://openalex.org/W2218047931;https://openalex.org/W2239232218;https://openalex.org/W2268331518;https://openalex.org/W2270470215;https://openalex.org/W2290425607;https://openalex.org/W2295400067;https://openalex.org/W2404692435;https://openalex.org/W2480364715;https://openalex.org/W2518557595;https://openalex.org/W2556345765;https://openalex.org/W2559969670;https://openalex.org/W2560228494;https://openalex.org/W2581063857;https://openalex.org/W2584696667;https://openalex.org/W2592084954;https://openalex.org/W2603112626;https://openalex.org/W2604842920;https://openalex.org/W2618530766;https://openalex.org/W2621019941;https://openalex.org/W2725541287;https://openalex.org/W2729101176;https://openalex.org/W2747278505;https://openalex.org/W2759284092;https://openalex.org/W2762840986;https://openalex.org/W2766140847;https://openalex.org/W2771590529;https://openalex.org/W2803678638;https://openalex.org/W2809899558;https://openalex.org/W2811102151;https://openalex.org/W2811266281;https://openalex.org/W2866575265;https://openalex.org/W2883434573;https://openalex.org/W2889666927;https://openalex.org/W2905485021;https://openalex.org/W2908064175;https://openalex.org/W2911964244;https://openalex.org/W2916772210;https://openalex.org/W2919115771;https://openalex.org/W2929130519;https://openalex.org/W2939407295;https://openalex.org/W2952136798;https://openalex.org/W2953301966;https://openalex.org/W2963453445;https://openalex.org/W2969066554;https://openalex.org/W2970304580;https://openalex.org/W2979048634;https://openalex.org/W2981650146;https://openalex.org/W2993535041;https://openalex.org/W2995621105;https://openalex.org/W2996219887;https://openalex.org/W3103799692;https://openalex.org/W3133945275;https://openalex.org/W3145506661;https://openalex.org/W3147809485;https://openalex.org/W4239510810;https://openalex.org/W4247162069;https://openalex.org/W4298304654;https://openalex.org/W6675354045;https://openalex.org/W6730587673;https://openalex.org/W6764868099;https://openalex.org/W6771633300,Computer science;Data science;Machine learning;Artificial intelligence;Data mining;Information retrieval,Machine Learning and Data Classification;Neural Networks and Applications;Anomaly Detection Techniques and Applications
-OPENALEX,https://openalex.org/W4312223667,https://doi.org/10.1007/s12063-022-00335-y,,Mapping the Role and Impact of Artificial Intelligence and Machine Learning Applications in Supply Chain Digital Transformation: A Bibliometric Analysis,OPERATIONS MANAGEMENT RESEARCH,OPERATIONS MANAGEMENT RESEARCH,2022,article,en,Indian Institute of Management Lucknow,,16,4,1641,1666,"Rana, 2022, OPERATIONS MANAGEMENT RESEARCH",94,"Rana, Jeetu;Daultani, Yash","Rana, Jeetu;Daultani, Yash",Indian Institute of Management Lucknow,https://openalex.org/W972842902;https://openalex.org/W2011542859;https://openalex.org/W2015415650;https://openalex.org/W2058729910;https://openalex.org/W2070032609;https://openalex.org/W2090353581;https://openalex.org/W2104925392;https://openalex.org/W2113348250;https://openalex.org/W2166304961;https://openalex.org/W2291128798;https://openalex.org/W2470242011;https://openalex.org/W2751325923;https://openalex.org/W2753155801;https://openalex.org/W2758571161;https://openalex.org/W2765422372;https://openalex.org/W2789444712;https://openalex.org/W2888648656;https://openalex.org/W2890622118;https://openalex.org/W2892727770;https://openalex.org/W2902553738;https://openalex.org/W2947436355;https://openalex.org/W2954217333;https://openalex.org/W2963453445;https://openalex.org/W2989523152;https://openalex.org/W3001124561;https://openalex.org/W3013165905;https://openalex.org/W3028158573;https://openalex.org/W3081491601;https://openalex.org/W3089035854;https://openalex.org/W3089252064;https://openalex.org/W3131345956;https://openalex.org/W3170102960;https://openalex.org/W3177949898;https://openalex.org/W3192208786;https://openalex.org/W3193620804;https://openalex.org/W3194250276;https://openalex.org/W3197052056;https://openalex.org/W3205172848;https://openalex.org/W3209152117;https://openalex.org/W4220894906;https://openalex.org/W4256681118;https://openalex.org/W4289855647,Supply chain;Computer science;Scope (computer science);Digital transformation;Data science;Emerging technologies;Industry 4.0;Supply chain management;Knowledge management;Process management;Artificial intelligence;Business;Data mining;Marketing,Digital Transformation in Industry;Quality and Supply Management;Sustainable Supply Chain Management
-OPENALEX,https://openalex.org/W4280610169,https://doi.org/10.1016/j.compag.2022.107017,,Drones in agriculture: A review and bibliometric analysis,COMPUTERS AND ELECTRONICS IN AGRICULTURE,COMPUTERS AND ELECTRONICS IN AGRICULTURE,2022,review,en,MODUL University Vienna,"Drones, also called Unmanned Aerial Vehicles (UAV), have witnessed a remarkable development in recent decades. In agriculture, they have changed farming practices by offering farmers substantial cost savings, increased operational efficiency, and better profitability. Over the past decades, the topic of agricultural drones has attracted remarkable academic attention. We therefore conduct a comprehensive review based on bibliometrics to summarize and structure existing academic literature and reveal current research trends and hotspots. We apply bibliometric techniques and analyze the literature surrounding agricultural drones to summarize and assess previous research. Our analysis indicates that remote sensing, precision agriculture, deep learning, machine learning, and the Internet of Things are critical topics related to agricultural drones. The co-citation analysis reveals six broad research clusters in the literature. This study is one of the first attempts to summarize drone research in agriculture and suggest future research directions.",198,,107017,107017,"Rejeb, 2022, COMPUTERS AND ELECTRONICS IN AGRICULTURE",643,"Rejeb, Abderahman;Abdollahi, Alireza;Rejeb, Karim;Treiblmaier, Horst","Rejeb, Abderahman;Abdollahi, Alireza;Rejeb, Karim;Treiblmaier, Horst",University of Rome Tor Vergata;Kharazmi University;University of Carthage;MODUL University Vienna,https://openalex.org/W30209551;https://openalex.org/W82576261;https://openalex.org/W982801857;https://openalex.org/W1123106775;https://openalex.org/W1200922351;https://openalex.org/W1442930683;https://openalex.org/W1462825729;https://openalex.org/W1518672027;https://openalex.org/W1559528524;https://openalex.org/W1577297395;https://openalex.org/W1594573182;https://openalex.org/W1645840676;https://openalex.org/W1848144067;https://openalex.org/W1871840861;https://openalex.org/W1955749066;https://openalex.org/W1966538856;https://openalex.org/W1966579280;https://openalex.org/W1968314959;https://openalex.org/W1968565953;https://openalex.org/W1969234587;https://openalex.org/W1978331315;https://openalex.org/W1979086491;https://openalex.org/W1979329989;https://openalex.org/W1980140622;https://openalex.org/W1980467157;https://openalex.org/W1985345354;https://openalex.org/W1989600108;https://openalex.org/W1991739869;https://openalex.org/W1996933319;https://openalex.org/W2002008272;https://openalex.org/W2005207065;https://openalex.org/W2005404611;https://openalex.org/W2006588449;https://openalex.org/W2009918649;https://openalex.org/W2012816307;https://openalex.org/W2016718980;https://openalex.org/W2019400639;https://openalex.org/W2022193520;https://openalex.org/W2030083859;https://openalex.org/W2030843614;https://openalex.org/W2039409148;https://openalex.org/W2040403200;https://openalex.org/W2042553430;https://openalex.org/W2054397552;https://openalex.org/W2055186043;https://openalex.org/W2059862423;https://openalex.org/W2062982970;https://openalex.org/W2063472265;https://openalex.org/W2064636932;https://openalex.org/W2068139671;https://openalex.org/W2069209512;https://openalex.org/W2071190035;https://openalex.org/W2071427061;https://openalex.org/W2071525319;https://openalex.org/W2072611758;https://openalex.org/W2072866698;https://openalex.org/W2074464158;https://openalex.org/W2080091930;https://openalex.org/W2082278455;https://openalex.org/W2085635066;https://openalex.org/W2087991080;https://openalex.org/W2097536090;https://openalex.org/W2103911239;https://openalex.org/W2110562193;https://openalex.org/W2116277900;https://openalex.org/W2117007244;https://openalex.org/W2119059400;https://openalex.org/W2122348296;https://openalex.org/W2129047267;https://openalex.org/W2129936978;https://openalex.org/W2133125644;https://openalex.org/W2134852861;https://openalex.org/W2143192685;https://openalex.org/W2145982493;https://openalex.org/W2150220236;https://openalex.org/W2150664932;https://openalex.org/W2151499786;https://openalex.org/W2165854046;https://openalex.org/W2188767531;https://openalex.org/W2201333553;https://openalex.org/W2207083369;https://openalex.org/W2243003515;https://openalex.org/W2275696275;https://openalex.org/W2313974443;https://openalex.org/W2315894413;https://openalex.org/W2319859377;https://openalex.org/W2328015724;https://openalex.org/W2342626385;https://openalex.org/W2395869423;https://openalex.org/W2413512417;https://openalex.org/W2462474087;https://openalex.org/W2467491686;https://openalex.org/W2471543519;https://openalex.org/W2479938810;https://openalex.org/W2513851811;https://openalex.org/W2515492367;https://openalex.org/W2520082337;https://openalex.org/W2522615148;https://openalex.org/W2539185528;https://openalex.org/W2550198355;https://openalex.org/W2565531507;https://openalex.org/W2584232616;https://openalex.org/W2587807122;https://openalex.org/W2593778105;https://openalex.org/W2605401590;https://openalex.org/W2607615935;https://openalex.org/W2613697771;https://openalex.org/W2615516218;https://openalex.org/W2618732405;https://openalex.org/W2624387057;https://openalex.org/W2646675373;https://openalex.org/W2648242067;https://openalex.org/W2729164367;https://openalex.org/W2736116482;https://openalex.org/W2742577398;https://openalex.org/W2751567280;https://openalex.org/W2754367764;https://openalex.org/W2757806273;https://openalex.org/W2765366036;https://openalex.org/W2767327746;https://openalex.org/W2767657507;https://openalex.org/W2768508481;https://openalex.org/W2771058985;https://openalex.org/W2787441870;https://openalex.org/W2790858865;https://openalex.org/W2792286873;https://openalex.org/W2793263498;https://openalex.org/W2793328538;https://openalex.org/W2800002789;https://openalex.org/W2804241935;https://openalex.org/W2806576037;https://openalex.org/W2810670912;https://openalex.org/W2811148000;https://openalex.org/W2883113516;https://openalex.org/W2884040439;https://openalex.org/W2884438462;https://openalex.org/W2884690740;https://openalex.org/W2885770726;https://openalex.org/W2888404827;https://openalex.org/W2888766590;https://openalex.org/W2889454130;https://openalex.org/W2890513934;https://openalex.org/W2890637087;https://openalex.org/W2896242594;https://openalex.org/W2898498213;https://openalex.org/W2899338010;https://openalex.org/W2900330501;https://openalex.org/W2900477065;https://openalex.org/W2902949125;https://openalex.org/W2903808251;https://openalex.org/W2904027073;https://openale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;https://openalex.org/W6781459431;https://openalex.org/W6791557914;https://openalex.org/W6794790404;https://openalex.org/W6796844681;https://openalex.org/W6802344170;https://openalex.org/W6803202188;https://openalex.org/W6806997867,Drone;Bibliometrics;Agriculture;Citation;Data science;Precision agriculture;Profitability index;Computer science;Geography;Business;Library science,UAV Applications and Optimization;Smart Agriculture and AI;Remote Sensing and LiDAR Applications
-OPENALEX,https://openalex.org/W4367671427,https://doi.org/10.1007/s11135-023-01673-0,https://pubmed.ncbi.nlm.nih.gov/37359968,"Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis",QUALITY & QUANTITY,QUALITY & QUANTITY,2023,article,en,University of Kerala,,58,1,849,878,"Biju, 2023, QUALITY & QUANTITY",78,"Biju, Ajithakumari Vijayappan Nair;Thomas, Ann Susan;Thasneem, J","Biju, Ajithakumari Vijayappan Nair;Thomas, Ann Susan;Thasneem, J",University of Kerala,https://openalex.org/W150292108;https://openalex.org/W643606810;https://openalex.org/W1021000864;https://openalex.org/W1999102038;https://openalex.org/W2004076523;https://openalex.org/W2012533078;https://openalex.org/W2015780725;https://openalex.org/W2048658075;https://openalex.org/W2069613886;https://openalex.org/W2079228007;https://openalex.org/W2084674986;https://openalex.org/W2095293504;https://openalex.org/W2097933633;https://openalex.org/W2102152810;https://openalex.org/W2103441516;https://openalex.org/W2120109270;https://openalex.org/W2131681506;https://openalex.org/W2137503943;https://openalex.org/W2148905674;https://openalex.org/W2159722025;https://openalex.org/W2164856000;https://openalex.org/W2167456135;https://openalex.org/W2171567624;https://openalex.org/W2269863304;https://openalex.org/W2327706228;https://openalex.org/W2342352817;https://openalex.org/W2344786740;https://openalex.org/W2403312433;https://openalex.org/W2514828644;https://openalex.org/W2592542840;https://openalex.org/W2593936570;https://openalex.org/W2606916050;https://openalex.org/W2613650002;https://openalex.org/W2621796148;https://openalex.org/W2624385633;https://openalex.org/W2626389465;https://openalex.org/W2734777338;https://openalex.org/W2735575534;https://openalex.org/W2755950973;https://openalex.org/W2766088648;https://openalex.org/W2776003688;https://openalex.org/W2783850934;https://openalex.org/W2786341358;https://openalex.org/W2788025656;https://openalex.org/W2789364533;https://openalex.org/W2791987208;https://openalex.org/W2803148772;https://openalex.org/W2807909115;https://openalex.org/W2886191303;https://openalex.org/W2889880961;https://openalex.org/W2898153843;https://openalex.org/W2898514850;https://openalex.org/W2905074124;https://openalex.org/W2906573737;https://openalex.org/W2911450871;https://openalex.org/W2911543806;https://openalex.org/W2912066933;https://openalex.org/W2917490808;https://openalex.org/W2919115771;https://openalex.org/W2923437336;https://openalex.org/W2940136728;https://openalex.org/W2946494228;https://openalex.org/W2967019654;https://openalex.org/W2968923792;https://openalex.org/W2969625533;https://openalex.org/W2969839986;https://openalex.org/W2973702823;https://openalex.org/W2984563716;https://openalex.org/W2990450011;https://openalex.org/W2992191029;https://openalex.org/W2992584342;https://openalex.org/W2997443965;https://openalex.org/W2999884159;https://openalex.org/W3000463950;https://openalex.org/W3000895385;https://openalex.org/W3005880472;https://openalex.org/W3011387630;https://openalex.org/W3013063141;https://openalex.org/W3016378723;https://openalex.org/W3017193407;https://openalex.org/W3029156716;https://openalex.org/W3035669514;https://openalex.org/W3035797136;https://openalex.org/W3042981023;https://openalex.org/W3043220749;https://openalex.org/W3044711781;https://openalex.org/W3048283392;https://openalex.org/W3082157970;https://openalex.org/W3084037690;https://openalex.org/W3097106009;https://openalex.org/W3099768174;https://openalex.org/W3106886953;https://openalex.org/W3118423825;https://openalex.org/W3122779978;https://openalex.org/W3124071524;https://openalex.org/W3126032681;https://openalex.org/W3126443786;https://openalex.org/W3126729572;https://openalex.org/W3135028703;https://openalex.org/W3165340137;https://openalex.org/W3168924973;https://openalex.org/W3170454297;https://openalex.org/W3186529101;https://openalex.org/W3194250276;https://openalex.org/W3198357836;https://openalex.org/W4200065055;https://openalex.org/W4211211006;https://openalex.org/W4230692838;https://openalex.org/W6903600625,Archetype;Artificial intelligence;Extant taxon;Empirical research;Social sphere;Finance;Zhàng;Machine learning;China;Sociology;Computer science;Economics;Political science;Social science;Mathematics;Statistics,"FinTech, Crowdfunding, Digital Finance;Stock Market Forecasting Methods;Financial Distress and Bankruptcy Prediction"
-OPENALEX,https://openalex.org/W3013838778,https://doi.org/10.3390/sym12040495,,Machine Learning and Big Data in the Impact Literature. A Bibliometric Review with Scientific Mapping in Web of Science,SYMMETRY,SYMMETRY,2020,review,en,Universidad de Granada,"Combined use of machine learning and large data allows us to analyze data and find explanatory models that would not be possible with traditional techniques, which is basic within the principles of symmetry. The present study focuses on the analysis of the scientific production and performance of the Machine Learning and Big Data (MLBD) concepts. A bibliometric methodology of scientific mapping has been used, based on processes of estimation, quantification, analytical tracking, and evaluation of scientific research. A total of 4240 scientific publications from the Web of Science (WoS) have been analyzed. Our results show a constant and ascending evolution of the scientific production on MLBD, 2018 and 2019 being the most productive years. The productions are mainly in English language. The topics are variable in the different periods analyzed, where “machine-learning” is the one that shows the greatest bibliometric indicators, it is found in most of motor topics and is the one that offers the greatest line of continuity between the different periods. It can be concluded that research on MLBD is of interest and relevance to the scientific community, which focuses its studies on the branch of machine-learning.",12,4,495,495,"López-Belmonte, 2020, SYMMETRY",61,"López-Belmonte, Jesús;Robles, Adrián Segura;Moreno-Guerrero, Antonio-José;González, María Elena Parra","López-Belmonte, Jesús;Robles, Adrián Segura;Moreno-Guerrero, Antonio-José;González, María Elena Parra",Universidad de Granada,https://openalex.org/W1500693574;https://openalex.org/W1863277889;https://openalex.org/W1870882775;https://openalex.org/W2075424814;https://openalex.org/W2076063813;https://openalex.org/W2100954244;https://openalex.org/W2105822516;https://openalex.org/W2108680868;https://openalex.org/W2117497767;https://openalex.org/W2128438887;https://openalex.org/W2139087717;https://openalex.org/W2153803020;https://openalex.org/W2163634312;https://openalex.org/W2164364358;https://openalex.org/W2488558416;https://openalex.org/W2576683119;https://openalex.org/W2610052998;https://openalex.org/W2742835787;https://openalex.org/W2757042342;https://openalex.org/W2767547957;https://openalex.org/W2770848963;https://openalex.org/W2772434162;https://openalex.org/W2774008574;https://openalex.org/W2794329575;https://openalex.org/W2900765267;https://openalex.org/W2915147983;https://openalex.org/W2919115771;https://openalex.org/W2931283717;https://openalex.org/W2945453951;https://openalex.org/W2965388179;https://openalex.org/W2975867066;https://openalex.org/W2980095825;https://openalex.org/W2985942842;https://openalex.org/W2992582827;https://openalex.org/W2997933987;https://openalex.org/W3001491100;https://openalex.org/W3041004109;https://openalex.org/W4407271179;https://openalex.org/W6723273074;https://openalex.org/W6741788004,Computer science;Big data;Relevance (law);Data science;Artificial intelligence;Machine learning;Data mining,Big Data and Business Intelligence
-OPENALEX,https://openalex.org/W3205155573,https://doi.org/10.1016/j.ijpe.2021.108340,,Understanding product returns: A systematic literature review using machine learning and bibliometric analysis,INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS,INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS,2021,article,en,University of Greenwich,,243,,108340,108340,"Duong, 2021, INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS",79,"Duong, Quang Huy;Zhou, Li;Meng, Meng;Nguyen, Truong Van;Ieromonachou, Petros;Nguyen, Tiep Duy","Duong, Quang Huy;Zhou, Li;Meng, Meng;Nguyen, Truong Van;Ieromonachou, Petros;Nguyen, Tiep Duy",University of Greenwich;University of Bath;Brunel University of London,https://openalex.org/W104939753;https://openalex.org/W150292108;https://openalex.org/W206359983;https://openalex.org/W1497772944;https://openalex.org/W1520584404;https://openalex.org/W1540967267;https://openalex.org/W1581625869;https://openalex.org/W1601951354;https://openalex.org/W1880262756;https://openalex.org/W1947595544;https://openalex.org/W1964623804;https://openalex.org/W1967139445;https://openalex.org/W1967625562;https://openalex.org/W1969123556;https://openalex.org/W1970859146;https://openalex.org/W1971463466;https://openalex.org/W1974434592;https://openalex.org/W1982256043;https://openalex.org/W1982924738;https://openalex.org/W1982935960;https://openalex.org/W1989229635;https://openalex.org/W2001701100;https://openalex.org/W2002317935;https://openalex.org/W2003860137;https://openalex.org/W2005207065;https://openalex.org/W2005311637;https://openalex.org/W2005570417;https://openalex.org/W2006852464;https://openalex.org/W2006940952;https://openalex.org/W2008054637;https://openalex.org/W2011923969;https://openalex.org/W2019932115;https://openalex.org/W2020833085;https://openalex.org/W2022160839;https://openalex.org/W2022920407;https://openalex.org/W2023171327;https://openalex.org/W2029123070;https://openalex.org/W2029798215;https://openalex.org/W2030227729;https://openalex.org/W2030252188;https://openalex.org/W2031628688;https://openalex.org/W2032072016;https://openalex.org/W2040280177;https://openalex.org/W2040513702;https://openalex.org/W2041777999;https://openalex.org/W2045108252;https://openalex.org/W2047911768;https://openalex.org/W2049458779;https://openalex.org/W2050529056;https://openalex.org/W2050547803;https://openalex.org/W2051930841;https://openalex.org/W2053313148;https://openalex.org/W2053313509;https://openalex.org/W2057386011;https://openalex.org/W2061115987;https://openalex.org/W2064967389;https://openalex.org/W2069940389;https://openalex.org/W2073145055;https://openalex.org/W2075217160;https://openalex.org/W2080108864;https://openalex.org/W2081653786;https://openalex.org/W2082034461;https://openalex.org/W2087792930;https://openalex.org/W2088193903;https://openalex.org/W2089609741;https://openalex.org/W2090420549;https://openalex.org/W2094995270;https://openalex.org/W2095229032;https://openalex.org/W2100019901;https://openalex.org/W2102298099;https://openalex.org/W2107743791;https://openalex.org/W2111877104;https://openalex.org/W2112186058;https://openalex.org/W2116126561;https://openalex.org/W2117355159;https://openalex.org/W2119702521;https://openalex.org/W2121248249;https://openalex.org/W2125208917;https://openalex.org/W2125910575;https://openalex.org/W2126022285;https://openalex.org/W2128821753;https://openalex.org/W2132327276;https://openalex.org/W2132784107;https://openalex.org/W2136612012;https://openalex.org/W2137174507;https://openalex.org/W2137593399;https://openalex.org/W2138184479;https://openalex.org/W2141616240;https://openalex.org/W2144947015;https://openalex.org/W2147152072;https://openalex.org/W2150060171;https://openalex.org/W2150220236;https://openalex.org/W2151024273;https://openalex.org/W2153729696;https://openalex.org/W2155774482;https://openalex.org/W2156822248;https://openalex.org/W2160170318;https://openalex.org/W2160444496;https://openalex.org/W2161169387;https://openalex.org/W2164617481;https://openalex.org/W2165713616;https://openalex.org/W2183705853;https://openalex.org/W2215353773;https://openalex.org/W2245932802;https://openalex.org/W2288874350;https://openalex.org/W2340695878;https://openalex.org/W2440237703;https://openalex.org/W2467872127;https://openalex.org/W2480377045;https://openalex.org/W2481463970;https://openalex.org/W2482679439;https://openalex.org/W2510695141;https://openalex.org/W2563961554;https://openalex.org/W2568407129;https://openalex.org/W2570282734;https://openalex.org/W2606989030;https://openalex.org/W2732521237;https://openalex.org/W2738289515;https://openalex.org/W2740182096;https://openalex.org/W2755872041;https://openalex.org/W2769482885;https://openalex.org/W2769984510;https://openalex.org/W2781611303;https://openalex.org/W2783840809;https://openalex.org/W2806331492;https://openalex.org/W2807209880;https://openalex.org/W2883001636;https://openalex.org/W2885251002;https://openalex.org/W2888750138;https://openalex.org/W2890992194;https://openalex.org/W2892463090;https://openalex.org/W2894490845;https://openalex.org/W2898564950;https://openalex.org/W2901278912;https://openalex.org/W2901627956;https://openalex.org/W2901664574;https://openalex.org/W2904513285;https://openalex.org/W2909182647;https://openalex.org/W2911874551;https://openalex.org/W2912115945;https://openalex.org/W2917471231;https://openalex.org/W2935608639;https://openalex.org/W2937040248;https://openalex.org/W2939285397;https://openalex.org/W2941478914;https://openalex.org/W2942978268;https://openalex.org/W2951434526;https://openalex.org/W2953043123;https://openalex.org/W2953226774;https://openalex.org/W2954642792;https://openalex.org/W2955203191;https://openalex.org/W2959456427;https://openalex.org/W2965971711;https://openalex.org/W2969197168;https://openalex.org/W2979646543;https://openalex.org/W2984994429;https://openalex.org/W2986887219;https://openalex.org/W2992392140;https://openalex.org/W2995551760;https://openalex.org/W2996339339;https://openalex.org/W2997869386;https://openalex.org/W2998626673;https://openalex.org/W3000910650;https://openalex.org/W3002119723;https://openalex.org/W3003964569;https://openalex.org/W3004938766;https://openalex.org/W3006670468;https://openalex.org/W3010416066;https://openalex.org/W3012116377;https://openalex.org/W3012374868;https://openalex.org/W3012501803;https://openalex.org/W3014283919;https://openalex.org/W3015337523;https://openalex.org/W3015749997;https://openalex.org/W3017951096;https://openalex.org/W3018179126;https://openalex.org/W3022047015;https://openalex.org/W3022128117;https://openalex.org/W3022261864;https://openalex.org/W3022778240;https://openalex.org/W3022958555;https://openalex.org/W3024961770;https://openalex.org/W3026226282;https://openalex.org/W3026663849;https://openalex.org/W3034835549;https://openalex.org/W3037477013;https://openalex.org/W3038456961;https://openalex.org/W3040708147;https://openalex.org/W3043168668;https://openalex.org/W3047489808;https://openalex.org/W3048647615;https://openalex.org/W3080681202;https://openalex.org/W3082963869;https://openalex.org/W3083555389;https://openalex.org/W3083895391;https://openalex.org/W3087787590;https://openalex.org/W3088014247;https://openalex.org/W3094700648;https://openalex.org/W3095370396;https://openalex.org/W3097599689;https://openalex.org/W3099350049;https://openalex.org/W3111121152;https://openalex.org/W3111528201;https://openalex.org/W3112484444;https://openalex.org/W3121219238;https://openalex.org/W3123223435;https://openalex.org/W3123278189;https://openalex.org/W3123308521;https://openalex.org/W3131542013;https://openalex.org/W3132311892;https://openalex.org/W3135064254;https://openalex.org/W3136788966;https://openalex.org/W3137429312;https://openalex.org/W3145145494;https://openalex.org/W3152332785;https://openalex.org/W3156894835;https://openalex.org/W3159148328;https://openalex.org/W3163609607;https://openalex.org/W3168454824;https://openalex.org/W3170619139;https://openalex.org/W3177279685;https://openalex.org/W4231510805;https://openalex.org/W4233135949;https://openalex.org/W4236170518;https://openalex.org/W4255497883;https://openalex.org/W4285719527;https://openalex.org/W6608371920;https://openalex.org/W6639619044;https://openalex.org/W6670699361;https://openalex.org/W6678276291;https://openalex.org/W6679070811;https://openalex.org/W6696274433;https://openalex.org/W6773651783;https://openalex.org/W6774786534;https://openalex.org/W6775937791;https://openalex.org/W6781078373;https://openalex.org/W6782932187;https://openalex.org/W6784570525;https://openalex.org/W6786623845;https://openalex.org/W6787076512;https://openalex.org/W6791865867;https://openalex.org/W6796375415;https://openalex.org/W6796824591;https://openalex.org/W6797864088;https://openalex.org/W6815875558,Computer science;Context (archaeology);Bespoke;Product (mathematics);Cluster analysis;Marketing;Data science;Knowledge management;Artificial intelligence;Business,Supply Chain and Inventory Management;Sustainable Supply Chain Management;Forecasting Techniques and Applications
-OPENALEX,https://openalex.org/W4319663647,https://doi.org/10.1109/access.2023.3243635,,"Strategies to Measure Soil Moisture Using Traditional Methods, Automated Sensors, Remote Sensing, and Machine Learning Techniques: Review, Bibliometric Analysis, Applications, Research Findings, and Future Directions",IEEE ACCESS,IEEE ACCESS,2023,article,en,"Indian Institute of Science Education and Research, Bhopal","This review provides a detailed synthesis of various in-situ, remote sensing, and machine learning approaches to estimate soil moisture. Bibliometric analysis of the published literature on soil moisture shows that Time-Domain Reflectometry (TDR) is the most widely used in-situ instrument, while remote sensing is the most preferred application, and the random forest is the widely applied algorithm to simulate surface soil moisture. We have applied ten most widely used machine learning models on a publicly available dataset (in-situ soil moisture measurement and satellite images) to predict soil moisture and compared their results. We have briefly discussed the potential of using the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission images to estimate soil moisture. Finally, this review discusses the capabilities of physics-informed and automated machine learning (AutoML) models to predict surface soil moisture at higher spatial and temporal resolutions. This review will assist researchers in investigating the applications of soil moisture in the broad domain of earth sciences.",11,,13605,13635,"Singh, 2023, IEEE ACCESS",93,"Singh, Abhilash;Gaurav, Kumar;Sonkar, Gaurav Kailash;Lee, Cheng‐Chi","Singh, Abhilash;Gaurav, Kumar;Sonkar, Gaurav Kailash;Lee, Cheng‐Chi","Indian Institute of Science Education and Research, Bhopal;Fu Jen Catholic University;Asia University",https://openalex.org/W607088829;https://openalex.org/W654542702;https://openalex.org/W747352923;https://openalex.org/W1096706567;https://openalex.org/W1451730414;https://openalex.org/W1493904465;https://openalex.org/W1528117200;https://openalex.org/W1570245028;https://openalex.org/W1575584547;https://openalex.org/W1862592918;https://openalex.org/W1873607511;https://openalex.org/W1931431119;https://openalex.org/W1964785899;https://openalex.org/W1968729022;https://openalex.org/W1968946567;https://openalex.org/W1974180061;https://openalex.org/W1974866669;https://openalex.org/W1975682355;https://openalex.org/W1978268894;https://openalex.org/W1985817801;https://openalex.org/W1990869551;https://openalex.org/W1998742682;https://openalex.org/W2003036298;https://openalex.org/W2003104708;https://openalex.org/W2006457820;https://openalex.org/W2013173853;https://openalex.org/W2014238150;https://openalex.org/W2020004539;https://openalex.org/W2021765748;https://openalex.org/W2025515353;https://openalex.org/W2028979033;https://openalex.org/W2033730365;https://openalex.org/W2034956981;https://openalex.org/W2037125412;https://openalex.org/W2037744170;https://openalex.org/W2038782607;https://openalex.org/W2038949241;https://openalex.org/W2039853184;https://openalex.org/W2044927495;https://openalex.org/W2046547379;https://openalex.org/W2047199896;https://openalex.org/W2048069199;https://openalex.org/W2050310403;https://openalex.org/W2052918983;https://openalex.org/W2058891717;https://openalex.org/W2059150651;https://openalex.org/W2059266940;https://openalex.org/W2059933426;https://openalex.org/W2060794423;https://openalex.org/W2062781596;https://openalex.org/W2063907334;https://openalex.org/W2067426984;https://openalex.org/W2071323141;https://openalex.org/W2071440331;https://openalex.org/W2075411628;https://openalex.org/W2076196252;https://openalex.org/W2082162486;https://openalex.org/W2082937524;https://openalex.org/W2084952127;https://openalex.org/W2089333997;https://openalex.org/W2090683582;https://openalex.org/W2090684728;https://openalex.org/W2091213485;https://openalex.org/W2092055157;https://openalex.org/W2092869579;https://openalex.org/W2094807568;https://openalex.org/W2096801161;https://openalex.org/W2100351139;https://openalex.org/W2100401723;https://openalex.org/W2102998469;https://openalex.org/W2109898366;https://openalex.org/W2110614570;https://openalex.org/W2114569030;https://openalex.org/W2116705363;https://openalex.org/W2123744475;https://openalex.org/W2124561353;https://openalex.org/W2126835024;https://openalex.org/W2130515708;https://openalex.org/W2136630510;https://openalex.org/W2138149909;https://openalex.org/W2143682006;https://openalex.org/W2144137496;https://openalex.org/W2144648550;https://openalex.org/W2145846654;https://openalex.org/W2147241431;https://openalex.org/W2147847739;https://openalex.org/W2150220236;https://openalex.org/W2154586386;https://openalex.org/W2155923880;https://openalex.org/W2156909104;https://openalex.org/W2161371786;https://openalex.org/W2163753399;https://openalex.org/W2166884697;https://openalex.org/W2169678197;https://openalex.org/W2270330859;https://openalex.org/W2278075763;https://openalex.org/W2330218200;https://openalex.org/W2338584982;https://openalex.org/W2496225726;https://openalex.org/W2498466302;https://openalex.org/W2509917403;https://openalex.org/W2549384537;https://openalex.org/W2573100662;https://openalex.org/W2582566895;https://openalex.org/W2587345921;https://openalex.org/W2588540686;https://openalex.org/W2623824808;https://openalex.org/W2781501238;https://openalex.org/W2792886864;https://openalex.org/W2795466254;https://openalex.org/W2803867848;https://openalex.org/W2847407800;https://openalex.org/W2888908144;https://openalex.org/W2889554869;https://openalex.org/W2894712623;https://openalex.org/W2901761027;https://openalex.org/W2911964244;https://openalex.org/W2923750943;https://openalex.org/W2937810286;https://openalex.org/W2943184968;https://openalex.org/W2944994884;https://openalex.org/W2948503857;https://openalex.org/W2959400106;https://openalex.org/W2963453445;https://openalex.org/W2966284335;https://openalex.org/W2969248106;https://openalex.org/W2971117720;https://openalex.org/W2982031239;https://openalex.org/W2997833137;https://openalex.org/W2998199582;https://openalex.org/W2998216295;https://openalex.org/W3001491100;https://openalex.org/W3003550074;https://openalex.org/W3004747764;https://openalex.org/W3012621877;https://openalex.org/W3015987273;https://openalex.org/W3017205434;https://openalex.org/W3019614043;https://openalex.org/W3035012985;https://openalex.org/W3042908761;https://openalex.org/W3045288728;https://openalex.org/W3088034280;https://openalex.org/W3090886373;https://openalex.org/W3100492273;https://openalex.org/W3103753913;https://openalex.org/W3106832215;https://openalex.org/W3112824784;https://openalex.org/W3115137215;https://openalex.org/W3121469503;https://openalex.org/W3125515083;https://openalex.org/W3125537303;https://openalex.org/W3131057326;https://openalex.org/W3134876352;https://openalex.org/W3135049088;https://openalex.org/W3154189120;https://openalex.org/W3160856016;https://openalex.org/W3162927183;https://openalex.org/W3163993681;https://openalex.org/W3177208529;https://openalex.org/W3181435465;https://openalex.org/W3195353059;https://openalex.org/W3197483324;https://openalex.org/W3201972326;https://openalex.org/W3205275279;https://openalex.org/W4210505033;https://openalex.org/W4231166535;https://openalex.org/W4234292501;https://openalex.org/W4236546708;https://openalex.org/W4243081636;https://openalex.org/W4281757528;https://openalex.org/W4283776044;https://openalex.org/W4283813344;https://openalex.org/W4294233994;https://openalex.org/W4319593977;https://openalex.org/W6602115970;https://openalex.org/W6606403862;https://openalex.org/W6618700822;https://openalex.org/W6621573692;https://openalex.org/W6628657155;https://openalex.org/W6631904961;https://openalex.org/W6634034818;https://openalex.org/W6680532697;https://openalex.org/W6688612899;https://openalex.org/W6732310830;https://openalex.org/W6787645134;https://openalex.org/W6824317741;https://openalex.org/W7066667914,Remote sensing;Water content;Synthetic aperture radar;Reflectometry;Environmental science;Moisture;Computer science;Soil science;Machine learning;Meteorology;Time domain;Engineering;Geology;Geography;Computer vision,Soil Moisture and Remote Sensing;Soil and Unsaturated Flow;Climate change and permafrost
-OPENALEX,https://openalex.org/W4212791338,https://doi.org/10.1016/j.eswa.2022.116659,,Machine learning techniques and data for stock market forecasting: A literature review,EXPERT SYSTEMS WITH APPLICATIONS,EXPERT SYSTEMS WITH APPLICATIONS,2022,review,en,Lappeenranta-Lahti University of Technology,"In this literature review, we investigate machine learning techniques that are applied for stock market prediction. A focus area in this literature review is the stock markets investigated in the literature as well as the types of variables used as input in the machine learning techniques used for predicting these markets. We examined 138 journal articles published between 2000 and 2019. The main contributions of this review are: (1) an extensive examination of the data, in particular, the markets and stock indices covered in the predictions, as well as the 2173 unique variables used for stock market predictions, including technical indicators, macro-economic variables, and fundamental indicators, and (2) an in-depth review of the machine learning techniques and their variants deployed for the predictions. In addition, we provide a bibliometric analysis of these journal articles, highlighting the most influential works and articles.",197,,116659,116659,"Kumbure, 2022, EXPERT SYSTEMS WITH APPLICATIONS",483,"Kumbure, Mahinda Mailagaha;Lohrmann, Christoph;Luukka, Pasi;Porras, Jari","Kumbure, Mahinda Mailagaha;Lohrmann, Christoph;Luukka, Pasi;Porras, Jari",Lappeenranta-Lahti University of Technology,https://openalex.org/W789578048;https://openalex.org/W855508711;https://openalex.org/W1024511229;https://openalex.org/W1494124194;https://openalex.org/W1558502133;https://openalex.org/W1697853073;https://openalex.org/W1807452827;https://openalex.org/W1815264562;https://openalex.org/W1866279363;https://openalex.org/W1949087994;https://openalex.org/W1966577984;https://openalex.org/W1975675278;https://openalex.org/W1975770397;https://openalex.org/W1978049199;https://openalex.org/W1978520392;https://openalex.org/W1979290264;https://openalex.org/W1979395212;https://openalex.org/W1980836123;https://openalex.org/W1984500452;https://openalex.org/W1986078433;https://openalex.org/W1986145156;https://openalex.org/W1988518729;https://openalex.org/W1988715797;https://openalex.org/W1994668012;https://openalex.org/W1995319408;https://openalex.org/W1997342558;https://openalex.org/W1997994299;https://openalex.org/W2001751530;https://openalex.org/W2003555953;https://openalex.org/W2004463884;https://openalex.org/W2005346797;https://openalex.org/W2005424446;https://openalex.org/W2011327086;https://openalex.org/W2011368107;https://openalex.org/W2011782945;https://openalex.org/W2012079387;https://openalex.org/W2013722099;https://openalex.org/W2017537474;https://openalex.org/W2017812666;https://openalex.org/W2021938316;https://openalex.org/W2023959308;https://openalex.org/W2025053102;https://openalex.org/W2031505816;https://openalex.org/W2031820816;https://openalex.org/W2032170121;https://openalex.org/W2039381705;https://openalex.org/W2039935421;https://openalex.org/W2041403160;https://openalex.org/W2041723890;https://openalex.org/W2042105482;https://openalex.org/W2043379390;https://openalex.org/W2043805990;https://openalex.org/W2046346480;https://openalex.org/W2047080235;https://openalex.org/W2048370582;https://openalex.org/W2049916782;https://openalex.org/W2050801485;https://openalex.org/W2053615983;https://openalex.org/W2056981468;https://openalex.org/W2058777398;https://openalex.org/W2065060269;https://openalex.org/W2066456070;https://openalex.org/W2066795664;https://openalex.org/W2066995518;https://openalex.org/W2070181657;https://openalex.org/W2080265874;https://openalex.org/W2083036265;https://openalex.org/W2085692898;https://openalex.org/W2085708398;https://openalex.org/W2089809028;https://openalex.org/W2090637028;https://openalex.org/W2098063401;https://openalex.org/W2101420345;https://openalex.org/W2101825885;https://openalex.org/W2103997983;https://openalex.org/W2104444668;https://openalex.org/W2108591703;https://openalex.org/W2111255674;https://openalex.org/W2121224351;https://openalex.org/W2124493593;https://openalex.org/W2125804487;https://openalex.org/W2126172796;https://openalex.org/W2128633294;https://openalex.org/W2129413312;https://openalex.org/W2144217557;https://openalex.org/W2144487825;https://openalex.org/W2145316193;https://openalex.org/W2145344497;https://openalex.org/W2148074536;https://openalex.org/W2162389778;https://openalex.org/W2168577773;https://openalex.org/W2168894761;https://openalex.org/W2202071898;https://openalex.org/W2210245339;https://openalex.org/W2260992041;https://openalex.org/W2301106258;https://openalex.org/W2324196090;https://openalex.org/W2345563409;https://openalex.org/W2385866669;https://openalex.org/W2400770063;https://openalex.org/W2409641346;https://openalex.org/W2468989783;https://openalex.org/W2479166638;https://openalex.org/W2500104392;https://openalex.org/W2523498403;https://openalex.org/W2554780437;https://openalex.org/W2566564364;https://openalex.org/W2571399401;https://openalex.org/W2580110346;https://openalex.org/W2582365220;https://openalex.org/W2585869517;https://openalex.org/W2587781392;https://openalex.org/W2593740144;https://openalex.org/W2593842564;https://openalex.org/W2594142095;https://openalex.org/W2601643873;https://openalex.org/W2607162077;https://openalex.org/W2624385633;https://openalex.org/W2625540161;https://openalex.org/W2728943311;https://openalex.org/W2751263409;https://openalex.org/W2754191969;https://openalex.org/W2762976654;https://openalex.org/W2768174908;https://openalex.org/W2773057751;https://openalex.org/W2780013296;https://openalex.org/W2784381726;https://openalex.org/W2789399411;https://openalex.org/W2791077645;https://openalex.org/W2791844767;https://openalex.org/W2792307024;https://openalex.org/W2793037577;https://openalex.org/W2797889333;https://openalex.org/W2806709681;https://openalex.org/W2809282474;https://openalex.org/W2810156540;https://openalex.org/W2833425706;https://openalex.org/W2845688424;https://openalex.org/W2865675487;https://openalex.org/W2874218187;https://openalex.org/W2886621583;https://openalex.org/W2888821844;https://openalex.org/W2891929938;https://openalex.org/W2894041752;https://openalex.org/W2895790973;https://openalex.org/W2897733922;https://openalex.org/W2897787857;https://openalex.org/W2900329809;https://openalex.org/W2902087482;https://openalex.org/W2902640113;https://openalex.org/W2906628967;https://openalex.org/W2910107358;https://openalex.org/W2910401125;https://openalex.org/W2912784131;https://openalex.org/W2917600866;https://openalex.org/W2920934919;https://openalex.org/W2922995703;https://openalex.org/W2931437238;https://openalex.org/W2936018573;https://openalex.org/W2938308298;https://openalex.org/W2945346514;https://openalex.org/W2946975908;https://openalex.org/W2947053643;https://openalex.org/W2947836816;https://openalex.org/W2949202718;https://openalex.org/W2949785328;https://openalex.org/W2949985842;https://openalex.org/W2950213400;https://openalex.org/W2950843237;https://openalex.org/W2964523010;https://openalex.org/W2970016095;https://openalex.org/W2975308768;https://openalex.org/W2979835384;https://openalex.org/W2987294427;https://openalex.org/W2993958139;https://openalex.org/W2997512255;https://openalex.org/W2998216295;https://openalex.org/W3002756429;https://openalex.org/W3009650506;https://openalex.org/W3016597555;https://openalex.org/W3019427697;https://openalex.org/W3036172083;https://openalex.org/W3041929891;https://openalex.org/W3046223032;https://openalex.org/W3081799531;https://openalex.org/W3084045086;https://openalex.org/W3110420963;https://openalex.org/W3120269867;https://openalex.org/W3122305330;https://openalex.org/W3123909942;https://openalex.org/W3124185353;https://openalex.org/W3124818628;https://openalex.org/W3125462345;https://openalex.org/W3130367027;https://openalex.org/W3152934775;https://openalex.org/W3155398915;https://openalex.org/W3162417502;https://openalex.org/W3193962426;https://openalex.org/W4211007335;https://openalex.org/W4231546411;https://openalex.org/W4300511110;https://openalex.org/W6662584374;https://openalex.org/W6663058530;https://openalex.org/W6693203454;https://openalex.org/W6704825425;https://openalex.org/W6744414302;https://openalex.org/W6771550436;https://openalex.org/W6788313817;https://openalex.org/W7066667914,Computer science;Machine learning;Stock market;Stock (firearms);Artificial intelligence;Macro;Stock market prediction;Econometrics;Economics,Stock Market Forecasting Methods;Financial Markets and Investment Strategies;Forecasting Techniques and Applications
-OPENALEX,https://openalex.org/W4365812881,https://doi.org/10.1016/j.ecoenv.2023.114911,https://pubmed.ncbi.nlm.nih.gov/37154080,The application of machine learning to air pollution research: A bibliometric analysis,ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY,ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY,2023,article,en,China Agricultural University,"Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future.",257,,114911,114911,"Li, 2023, ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY",53,"Li, Yunzhe;Sha, Zhipeng;Tang, Aohan;Goulding, K. W. T.;Liu, Xuejun","Li, Yunzhe;Sha, Zhipeng;Tang, Aohan;Goulding, K. W. T.;Liu, Xuejun",China Agricultural University;Rothamsted Research,https://openalex.org/W1968015368;https://openalex.org/W1968840994;https://openalex.org/W1970214772;https://openalex.org/W1972993779;https://openalex.org/W1973798705;https://openalex.org/W1983097169;https://openalex.org/W1995265224;https://openalex.org/W2006881475;https://openalex.org/W2009329344;https://openalex.org/W2017936885;https://openalex.org/W2024550526;https://openalex.org/W2026083729;https://openalex.org/W2042460614;https://openalex.org/W2081990052;https://openalex.org/W2088217228;https://openalex.org/W2089273527;https://openalex.org/W2094183206;https://openalex.org/W2101896997;https://openalex.org/W2110725020;https://openalex.org/W2143481518;https://openalex.org/W2150220236;https://openalex.org/W2268953907;https://openalex.org/W2323483937;https://openalex.org/W2471323753;https://openalex.org/W2543678400;https://openalex.org/W2559599946;https://openalex.org/W2620300958;https://openalex.org/W2760506659;https://openalex.org/W2784031884;https://openalex.org/W2800133189;https://openalex.org/W2901899013;https://openalex.org/W2903595533;https://openalex.org/W2912731314;https://openalex.org/W2990513755;https://openalex.org/W2990965051;https://openalex.org/W2991648381;https://openalex.org/W2994917295;https://openalex.org/W3010308848;https://openalex.org/W3013384466;https://openalex.org/W3021168369;https://openalex.org/W3034234413;https://openalex.org/W3034249623;https://openalex.org/W3038966175;https://openalex.org/W3041036705;https://openalex.org/W3044025067;https://openalex.org/W3046577772;https://openalex.org/W3071204575;https://openalex.org/W3081625936;https://openalex.org/W3093369049;https://openalex.org/W3094330861;https://openalex.org/W3094905049;https://openalex.org/W3106341490;https://openalex.org/W3107146702;https://openalex.org/W3115103108;https://openalex.org/W3119223558;https://openalex.org/W3119665391;https://openalex.org/W3120019038;https://openalex.org/W3120228528;https://openalex.org/W3129151888;https://openalex.org/W3134223197;https://openalex.org/W3135551422;https://openalex.org/W3158635464;https://openalex.org/W3165356482;https://openalex.org/W3183100395;https://openalex.org/W3193638860;https://openalex.org/W3197238626;https://openalex.org/W3203663665;https://openalex.org/W3215556695;https://openalex.org/W3216978572;https://openalex.org/W4206454612;https://openalex.org/W4210248900;https://openalex.org/W4210270671;https://openalex.org/W4211205884;https://openalex.org/W4220705625;https://openalex.org/W4221000442;https://openalex.org/W4221045736;https://openalex.org/W4224240129;https://openalex.org/W4280628125;https://openalex.org/W4281644131;https://openalex.org/W4285807826;https://openalex.org/W4286500596;https://openalex.org/W4292318061;https://openalex.org/W4304124303;https://openalex.org/W4310225776;https://openalex.org/W4311635257;https://openalex.org/W4315631859;https://openalex.org/W6671079822;https://openalex.org/W6791520476;https://openalex.org/W6799744991,Air pollution;Pollutant;Air pollutants;Pollution;Air quality index;Environmental science;Computer science;Process (computing);Field (mathematics);Meteorology;Machine learning;Operations research;Mathematics;Chemistry;Geography,Air Quality Monitoring and Forecasting;Air Quality and Health Impacts;Atmospheric chemistry and aerosols
-OPENALEX,https://openalex.org/W4312172943,https://doi.org/10.3390/ijerph20010173,https://pubmed.ncbi.nlm.nih.gov/36612493,"Conceptual Structure and Current Trends in Artificial Intelligence, Machine Learning, and Deep Learning Research in Sports: A Bibliometric Review",INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH,INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH,2022,review,en,University of Kaiserslautern,"Artificial intelligence and its subcategories of machine learning and deep learning are gaining increasing importance and attention in the context of sports research. This has also meant that the number of corresponding publications has become complex and unmanageably large in human terms. In the current state of the research field, there is a lack of bibliometric analysis, which would prove useful for obtaining insights into the large amounts of available literature. Therefore, the present work aims to identify important research issues, elucidate the conceptual structure of the research field, and unpack the evolutionary trends and the direction of hot topics regarding key themes in the research field of artificial intelligence in sports. Using the Scopus database, 1215 documents (reviews and articles) were selected. Bibliometric analysis was performed using VOSviewer and bibliometrix R package. The main findings are as follows: (a) the literature and research interest concerning AI and its subcategories is growing exponentially; (b) the top 20 most cited works comprise 32.52% of the total citations; (c) the top 10 journals are responsible for 28.64% of all published documents; (d) strong collaborative relationships are present, along with small, isolated collaboration networks of individual institutions; (e) the three most productive countries are China, the USA, and Germany; (f) different research themes can be characterized using author keywords with current trend topics, e.g., in the fields of biomechanics, injury prevention or prediction, new algorithms, and learning approaches. AI research activities in the fields of sports pedagogy, sports sociology, and sports economics seem to have played a subordinate role thus far. Overall, the findings of this study expand knowledge on the research situation as well as the development of research topics regarding the use of artificial intelligence in sports, and may guide researchers to identify currently relevant topics and gaps in the research.",20,1,173,173,"Dindorf, 2022, INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH",63,"Dindorf, Carlo;Bartaguiz, Eva;Gassmann, Freya;Fröhlich, Michael","Dindorf, Carlo;Bartaguiz, Eva;Gassmann, Freya;Fröhlich, Michael",University of Kaiserslautern;Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau;University of Koblenz and Landau,https://openalex.org/W327991062;https://openalex.org/W1622838444;https://openalex.org/W1767272795;https://openalex.org/W1976546217;https://openalex.org/W1981886524;https://openalex.org/W1990844260;https://openalex.org/W1995987707;https://openalex.org/W2044707851;https://openalex.org/W2056380775;https://openalex.org/W2064675550;https://openalex.org/W2067767241;https://openalex.org/W2072750586;https://openalex.org/W2088037265;https://openalex.org/W2089515781;https://openalex.org/W2101234009;https://openalex.org/W2137687977;https://openalex.org/W2144348409;https://openalex.org/W2146199357;https://openalex.org/W2150220236;https://openalex.org/W2156897283;https://openalex.org/W2160815625;https://openalex.org/W2169282617;https://openalex.org/W2178471959;https://openalex.org/W2267691291;https://openalex.org/W2287065228;https://openalex.org/W2346491476;https://openalex.org/W2397341258;https://openalex.org/W2514295870;https://openalex.org/W2563686712;https://openalex.org/W2620158569;https://openalex.org/W2736217281;https://openalex.org/W2754846726;https://openalex.org/W2755950973;https://openalex.org/W2783089003;https://openalex.org/W2788948370;https://openalex.org/W2792919287;https://openalex.org/W2795342689;https://openalex.org/W2805442627;https://openalex.org/W2883370365;https://openalex.org/W2897764506;https://openalex.org/W2899771611;https://openalex.org/W2903150444;https://openalex.org/W2905808289;https://openalex.org/W2905810301;https://openalex.org/W2908201961;https://openalex.org/W2909645133;https://openalex.org/W2910096450;https://openalex.org/W2911563717;https://openalex.org/W2911964244;https://openalex.org/W2912903863;https://openalex.org/W2913592589;https://openalex.org/W2914043053;https://openalex.org/W2919115771;https://openalex.org/W2939403565;https://openalex.org/W2942736276;https://openalex.org/W2951082895;https://openalex.org/W2952505933;https://openalex.org/W2954570505;https://openalex.org/W2978430085;https://openalex.org/W2981679558;https://openalex.org/W2996735620;https://openalex.org/W3001491100;https://openalex.org/W3007920314;https://openalex.org/W3014781586;https://openalex.org/W3016063666;https://openalex.org/W3033413502;https://openalex.org/W3038128212;https://openalex.org/W3038227888;https://openalex.org/W3038273726;https://openalex.org/W3041517607;https://openalex.org/W3048201280;https://openalex.org/W3048726839;https://openalex.org/W3080187696;https://openalex.org/W3083704612;https://openalex.org/W3113461818;https://openalex.org/W3121153159;https://openalex.org/W3123979837;https://openalex.org/W3125707221;https://openalex.org/W3132317639;https://openalex.org/W3152783591;https://openalex.org/W3154205755;https://openalex.org/W3157070004;https://openalex.org/W3160856016;https://openalex.org/W3165086753;https://openalex.org/W3185030651;https://openalex.org/W3194494914;https://openalex.org/W3195409530;https://openalex.org/W3202628454;https://openalex.org/W3210710412;https://openalex.org/W3215032978;https://openalex.org/W3217221256;https://openalex.org/W4206285355;https://openalex.org/W4207034206;https://openalex.org/W4210440155;https://openalex.org/W4210762790;https://openalex.org/W4220718512;https://openalex.org/W4220765315;https://openalex.org/W4225753362;https://openalex.org/W4226197148;https://openalex.org/W4231639996;https://openalex.org/W4232584455;https://openalex.org/W4244530851;https://openalex.org/W4253385319;https://openalex.org/W4255835337;https://openalex.org/W4280526311;https://openalex.org/W4285055012;https://openalex.org/W6675354045;https://openalex.org/W6704559304;https://openalex.org/W6757720333;https://openalex.org/W6793868969;https://openalex.org/W6801883050;https://openalex.org/W6810378660;https://openalex.org/W7075275322,Scopus;Field (mathematics);Context (archaeology);Artificial intelligence;Bibliometrics;Computer science;Data science;Sociology;Political science;Library science;MEDLINE;Mathematics,Sports Analytics and Performance;Artificial Intelligence in Healthcare and Education;Explainable Artificial Intelligence (XAI)
-OPENALEX,https://openalex.org/W4224307709,https://doi.org/10.3390/math10091397,,Military Applications of Machine Learning: A Bibliometric Perspective,MATHEMATICS,MATHEMATICS,2022,article,en,Universidad Complutense de Madrid,"The military environment generates a large amount of data of great importance, which makes necessary the use of machine learning for its processing. Its ability to learn and predict possible scenarios by analyzing the huge volume of information generated provides automatic learning and decision support. This paper aims to present a model of a machine learning architecture applied to a military organization, carried out and supported by a bibliometric study applied to an architecture model of a nonmilitary organization. For this purpose, a bibliometric analysis up to the year 2021 was carried out, making a strategic diagram and interpreting the results. The information used has been extracted from one of the main databases widely accepted by the scientific community, ISI WoS. No direct military sources were used. This work is divided into five parts: the study of previous research related to machine learning in the military world; the explanation of our research methodology using the SciMat, Excel and VosViewer tools; the use of this methodology based on data mining, preprocessing, cluster normalization, a strategic diagram and the analysis of its results to investigate machine learning in the military context; based on these results, a conceptual architecture of the practical use of ML in the military context is drawn up; and, finally, we present the conclusions, where we will see the most important areas and the latest advances in machine learning applied, in this case, to a military environment, to analyze a large set of data, providing utility, machine learning and decision support.",10,9,1397,1397,"Galán-Hernández, 2022, MATHEMATICS",34,"Galán-Hernández, José Javier;Carrasco, Ramón Alberto;LaTorre, Antonio","Galán-Hernández, José Javier;Carrasco, Ramón Alberto;LaTorre, Antonio",Universidad Complutense de Madrid;Universidad Politécnica de Madrid,https://openalex.org/W1569321962;https://openalex.org/W1847033250;https://openalex.org/W1981886524;https://openalex.org/W1983498087;https://openalex.org/W2040039177;https://openalex.org/W2047604914;https://openalex.org/W2079051740;https://openalex.org/W2114692751;https://openalex.org/W2187552894;https://openalex.org/W2261157879;https://openalex.org/W2320316254;https://openalex.org/W2421444976;https://openalex.org/W2481130031;https://openalex.org/W2747122029;https://openalex.org/W2759601165;https://openalex.org/W2793272303;https://openalex.org/W2795282312;https://openalex.org/W2800158564;https://openalex.org/W2804532080;https://openalex.org/W2844602024;https://openalex.org/W2886619050;https://openalex.org/W2888130761;https://openalex.org/W2888489905;https://openalex.org/W2889180445;https://openalex.org/W2890667190;https://openalex.org/W2897735842;https://openalex.org/W2921881483;https://openalex.org/W2931283717;https://openalex.org/W2944603520;https://openalex.org/W2949869628;https://openalex.org/W2950879898;https://openalex.org/W2953463859;https://openalex.org/W2963323326;https://openalex.org/W2968221061;https://openalex.org/W2980845355;https://openalex.org/W2981123833;https://openalex.org/W2987629254;https://openalex.org/W2994048855;https://openalex.org/W2994166400;https://openalex.org/W2997604005;https://openalex.org/W3007264885;https://openalex.org/W3011690857;https://openalex.org/W3020449132;https://openalex.org/W3020862983;https://openalex.org/W3031768672;https://openalex.org/W3035726715;https://openalex.org/W3036491774;https://openalex.org/W3040330511;https://openalex.org/W3041486090;https://openalex.org/W3048565925;https://openalex.org/W3074911901;https://openalex.org/W3083485545;https://openalex.org/W3084117197;https://openalex.org/W3084159587;https://openalex.org/W3092625934;https://openalex.org/W3101555142;https://openalex.org/W3113225871;https://openalex.org/W3117321795;https://openalex.org/W3117520861;https://openalex.org/W3117678186;https://openalex.org/W3127002054;https://openalex.org/W3127086562;https://openalex.org/W3127777658;https://openalex.org/W3127952267;https://openalex.org/W3132653939;https://openalex.org/W3135028703;https://openalex.org/W3136102896;https://openalex.org/W3137798842;https://openalex.org/W3141009123;https://openalex.org/W3151448454;https://openalex.org/W3153187775;https://openalex.org/W3159310960;https://openalex.org/W3163745152;https://openalex.org/W3170663388;https://openalex.org/W3170927860;https://openalex.org/W3173826505;https://openalex.org/W3179394107;https://openalex.org/W3187459817;https://openalex.org/W3197450565;https://openalex.org/W3198442084;https://openalex.org/W3205106854;https://openalex.org/W3210767309;https://openalex.org/W3215158158;https://openalex.org/W3216417923;https://openalex.org/W3217204768;https://openalex.org/W4200488419;https://openalex.org/W4205954621;https://openalex.org/W4210658299;https://openalex.org/W4212934852;https://openalex.org/W6687347952;https://openalex.org/W6763646235;https://openalex.org/W6764921660;https://openalex.org/W6802908963,Computer science;Artificial intelligence;Machine learning;Data pre-processing;Context (archaeology);Normalization (sociology);Data science;Architecture,Big Data and Business Intelligence;Artificial Intelligence in Healthcare and Education
-OPENALEX,https://openalex.org/W3108758487,https://doi.org/10.7717/peerj-cs.313,https://pubmed.ncbi.nlm.nih.gov/33816964,Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks,PEERJ COMPUTER SCIENCE,PEERJ COMPUTER SCIENCE,2020,article,en,National Yunlin University of Science and Technology,"BACKGROUND AND OBJECTIVE: The COVID-19 pandemic has caused severe mortality across the globe, with the USA as the current epicenter of the COVID-19 epidemic even though the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight COVID-19 pandemic from a different perspective. To the best of the authors' knowledge, no comprehensive survey with bibliometric analysis has been conducted yet on the adoption of machine learning to fight COVID-19. Therefore, the main goal of this study is to bridge this gap by carrying out an in-depth survey with bibliometric analysis on the adoption of machine learning-based technologies to fight COVID-19 pandemic from a different perspective, including an extensive systematic literature review and bibliometric analysis. METHODS: We applied a literature survey methodology to retrieved data from academic databases and subsequently employed a bibliometric technique to analyze the accessed records. Besides, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis and analysis are presented in this study. It was found that the Convolutional Neural Network (CNN) is mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest CT scan images. Similarly, in this study, we performed a bibliometric analysis of machine learning-based COVID-19 related publications in the Scopus and Web of Science citation indexes. Finally, we propose a new perspective for solving the challenges identified as direction for future research. We believe the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators. RESULTS: The findings of the analysis presented in this article reveal that machine learning-based COVID-19 diagnose tools received the most considerable attention from researchers. Specifically, the analyses of results show that energy and resources are more dispenses towards COVID-19 automated diagnose tools while COVID-19 drugs and vaccine development remains grossly underexploited. Besides, the machine learning-based algorithm that is predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images. CONCLUSIONS: The challenges hindering practical work on the application of machine learning-based technologies to fight COVID-19 and new perspective to solve the identified problems are presented in this article. Furthermore, we believed that the presented survey with bibliometric analysis could make it easier for researchers to identify areas that need further development and possibly identify potential collaborators at author, country and institutional level, with the overall aim of furthering research in the focused area of machine learning application to disease control.",6,,e313,e313,"Chiroma, 2020, PEERJ COMPUTER SCIENCE",46,"Chiroma, Haruna;Ezugwu, Absalom E.;Jauro, Fatsuma;Al-Garadi, Mohammed Ali;Abdullahi, Idris Nasir;Shuib, Liyana","Chiroma, Haruna;Ezugwu, Absalom E.;Jauro, Fatsuma;Al-Garadi, Mohammed Ali;Abdullahi, Idris Nasir;Shuib, Liyana",National Yunlin University of Science and Technology;University of KwaZulu-Natal;Ahmadu Bello University;Emory University;University of Malaya,https://openalex.org/W1495521362;https://openalex.org/W1689711448;https://openalex.org/W2028070629;https://openalex.org/W2123585936;https://openalex.org/W2131643987;https://openalex.org/W2411433310;https://openalex.org/W2573587735;https://openalex.org/W2754051771;https://openalex.org/W2758783533;https://openalex.org/W2783454406;https://openalex.org/W2784123366;https://openalex.org/W2810292802;https://openalex.org/W2919115771;https://openalex.org/W2964248614;https://openalex.org/W2969304542;https://openalex.org/W2989207397;https://openalex.org/W2993120940;https://openalex.org/W2994859409;https://openalex.org/W2999355706;https://openalex.org/W2999409984;https://openalex.org/W2999901576;https://openalex.org/W3000106795;https://openalex.org/W3001118548;https://openalex.org/W3001152983;https://openalex.org/W3001195213;https://openalex.org/W3002764620;https://openalex.org/W3003465021;https://openalex.org/W3004775012;https://openalex.org/W3005111420;https://openalex.org/W3006110666;https://openalex.org/W3006328792;https://openalex.org/W3006643024;https://openalex.org/W3006882119;https://openalex.org/W3007497549;https://openalex.org/W3007678840;https://openalex.org/W3008207212;https://openalex.org/W3008627141;https://openalex.org/W3008827533;https://openalex.org/W3008985036;https://openalex.org/W3009876049;https://openalex.org/W3009951436;https://openalex.org/W3010522809;https://openalex.org/W3010604545;https://openalex.org/W3010902474;https://openalex.org/W3010956505;https://openalex.org/W3011048075;https://openalex.org/W3011149445;https://openalex.org/W3011588331;https://openalex.org/W3011716991;https://openalex.org/W3011866596;https://openalex.org/W3012038738;https://openalex.org/W3012080801;https://openalex.org/W3012772854;https://openalex.org/W3012843799;https://openalex.org/W3012948061;https://openalex.org/W3013056994;https://openalex.org/W3013130152;https://openalex.org/W3013601031;https://openalex.org/W3013633552;https://openalex.org/W3013758358;https://openalex.org/W3014289208;https://openalex.org/W3014524604;https://openalex.org/W3014725478;https://openalex.org/W3014874133;https://openalex.org/W3015506441;https://openalex.org/W3015698531;https://openalex.org/W3015836412;https://openalex.org/W3016488464;https://openalex.org/W3016822938;https://openalex.org/W3017117984;https://openalex.org/W3017267196;https://openalex.org/W3017403618;https://openalex.org/W3017644243;https://openalex.org/W3017855299;https://openalex.org/W3017886147;https://openalex.org/W3018996808;https://openalex.org/W3019119825;https://openalex.org/W3019186020;https://openalex.org/W3019531985;https://openalex.org/W3020518471;https://openalex.org/W3021234865;https://openalex.org/W3021325664;https://openalex.org/W3021622280;https://openalex.org/W3021871585;https://openalex.org/W3022251615;https://openalex.org/W3022592783;https://openalex.org/W3022714712;https://openalex.org/W3022787740;https://openalex.org/W3022882668;https://openalex.org/W3022885394;https://openalex.org/W3023402713;https://openalex.org/W3023594394;https://openalex.org/W3023618360;https://openalex.org/W3025276991;https://openalex.org/W3025352604;https://openalex.org/W3025899282;https://openalex.org/W3025948831;https://openalex.org/W3026046290;https://openalex.org/W3031396671;https://openalex.org/W3033721958;https://openalex.org/W3038744550;https://openalex.org/W3040660552;https://openalex.org/W3042070269;https://openalex.org/W3042980950;https://openalex.org/W3046199400;https://openalex.org/W3047813901;https://openalex.org/W3048886990;https://openalex.org/W3087552217;https://openalex.org/W3104810384;https://openalex.org/W3165423827;https://openalex.org/W4211114005;https://openalex.org/W6629664863;https://openalex.org/W6678255110;https://openalex.org/W6772181936;https://openalex.org/W6774872622;https://openalex.org/W6776205382;https://openalex.org/W6776496726;https://openalex.org/W6776880506;https://openalex.org/W6960492257;https://openalex.org/W7074171492,Coronavirus disease 2019 (COVID-19);Web of science;Bibliometrics;Scopus;Citation;Pandemic;Data science;Computer science;Convolutional neural network;Perspective (graphical);Artificial intelligence;Geography;MEDLINE;Meta-analysis;Data mining;Medicine;Political science;Library science;Pathology,COVID-19 diagnosis using AI;COVID-19 epidemiological studies;COVID-19 Clinical Research Studies
-OPENALEX,https://openalex.org/W4367394483,https://doi.org/10.1007/s11831-023-09930-z,https://pubmed.ncbi.nlm.nih.gov/37359741,Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review,ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING,ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING,2023,review,en,North-West University,"The machine learning (ML) paradigm has gained much popularity today. Its algorithmic models are employed in every field, such as natural language processing, pattern recognition, object detection, image recognition, earth observation and many other research areas. In fact, machine learning technologies and their inevitable impact suffice in many technological transformation agendas currently being propagated by many nations, for which the already yielded benefits are outstanding. From a regional perspective, several studies have shown that machine learning technology can help address some of Africa's most pervasive problems, such as poverty alleviation, improving education, delivering quality healthcare services, and addressing sustainability challenges like food security and climate change. In this state-of-the-art paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 89% were articles with at least 482 citations published in 903 journals during the past three decades. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent.",30,7,4177,4207,"Ezugwu, 2023, ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING",44,"Ezugwu, Absalom E.;Oyelade, Olaide N.;Ikotun, Abiodun M.;Agushaka, Jeffrey O.;Ho, Yuh‐Shan","Ezugwu, Absalom E.;Oyelade, Olaide N.;Ikotun, Abiodun M.;Agushaka, Jeffrey O.;Ho, Yuh‐Shan",North-West University;Ahmadu Bello University;Asia University,https://openalex.org/W4952878;https://openalex.org/W48125276;https://openalex.org/W48397770;https://openalex.org/W1468262487;https://openalex.org/W1886355256;https://openalex.org/W1967551258;https://openalex.org/W1976610059;https://openalex.org/W1978331315;https://openalex.org/W1981302612;https://openalex.org/W1985715605;https://openalex.org/W2002732332;https://openalex.org/W2008192368;https://openalex.org/W2010199386;https://openalex.org/W2014928429;https://openalex.org/W2015811642;https://openalex.org/W2017335377;https://openalex.org/W2017689092;https://openalex.org/W2023211236;https://openalex.org/W2036398269;https://openalex.org/W2040395995;https://openalex.org/W2041375131;https://openalex.org/W2044451649;https://openalex.org/W2047066646;https://openalex.org/W2059980912;https://openalex.org/W2068249243;https://openalex.org/W2084290456;https://openalex.org/W2088264916;https://openalex.org/W2091878638;https://openalex.org/W2100599236;https://openalex.org/W2111892395;https://openalex.org/W2112621676;https://openalex.org/W2115743893;https://openalex.org/W2116575643;https://openalex.org/W2116660956;https://openalex.org/W2129435498;https://openalex.org/W2136375252;https://openalex.org/W2137006212;https://openalex.org/W2142537581;https://openalex.org/W2157395790;https://openalex.org/W2165167765;https://openalex.org/W2170353536;https://openalex.org/W2179909967;https://openalex.org/W2188115011;https://openalex.org/W2298779432;https://openalex.org/W2346331195;https://openalex.org/W2399016236;https://openalex.org/W2463898247;https://openalex.org/W2489886790;https://openalex.org/W2516848833;https://openalex.org/W2544947359;https://openalex.org/W2562498401;https://openalex.org/W2588003345;https://openalex.org/W2605165679;https://openalex.org/W2609731728;https://openalex.org/W2611159092;https://openalex.org/W2614850301;https://openalex.org/W2625392185;https://openalex.org/W2741922227;https://openalex.org/W2742428030;https://openalex.org/W2751668559;https://openalex.org/W2754981253;https://openalex.org/W2766624620;https://openalex.org/W2771053518;https://openalex.org/W2780099243;https://openalex.org/W2791363371;https://openalex.org/W2802762937;https://openalex.org/W2804836751;https://openalex.org/W2891503716;https://openalex.org/W2904321320;https://openalex.org/W2910381571;https://openalex.org/W2923537029;https://openalex.org/W2932700025;https://openalex.org/W2943477262;https://openalex.org/W2947174513;https://openalex.org/W2947319370;https://openalex.org/W2949006873;https://openalex.org/W2952799197;https://openalex.org/W2954771128;https://openalex.org/W2962824709;https://openalex.org/W2963837235;https://openalex.org/W2964101383;https://openalex.org/W2982439547;https://openalex.org/W2983289688;https://openalex.org/W2986821611;https://openalex.org/W2991507433;https://openalex.org/W2992584342;https://openalex.org/W2998503008;https://openalex.org/W3000451641;https://openalex.org/W3007397514;https://openalex.org/W3010655531;https://openalex.org/W3011204221;https://openalex.org/W3013998096;https://openalex.org/W3015286549;https://openalex.org/W3019166713;https://openalex.org/W3023402713;https://openalex.org/W3024243470;https://openalex.org/W3033432709;https://openalex.org/W3040736119;https://openalex.org/W3045576536;https://openalex.org/W3046981865;https://openalex.org/W3047486287;https://openalex.org/W3048581266;https://openalex.org/W3082567221;https://openalex.org/W3083228182;https://openalex.org/W3095217282;https://openalex.org/W3098965398;https://openalex.org/W3102027041;https://openalex.org/W3111249508;https://openalex.org/W3111825573;https://openalex.org/W3113742260;https://openalex.org/W3116478932;https://openalex.org/W3120155159;https://openalex.org/W3127432888;https://openalex.org/W3134939182;https://openalex.org/W3135743954;https://openalex.org/W3135875892;https://openalex.org/W3140854437;https://openalex.org/W3143865352;https://openalex.org/W3154627126;https://openalex.org/W3154815491;https://openalex.org/W3159070847;https://openalex.org/W3163849331;https://openalex.org/W3164551595;https://openalex.org/W3165261737;https://openalex.org/W3179655344;https://openalex.org/W3191945353;https://openalex.org/W3193836397;https://openalex.org/W3197928871;https://openalex.org/W3200198461;https://openalex.org/W3200532822;https://openalex.org/W3202607781;https://openalex.org/W3208011655;https://openalex.org/W3211562368;https://openalex.org/W3212019382;https://openalex.org/W3212644427;https://openalex.org/W3215748564;https://openalex.org/W3217085074;https://openalex.org/W4205767840;https://openalex.org/W4210548402;https://openalex.org/W4210634210;https://openalex.org/W4211213810;https://openalex.org/W4220798325;https://openalex.org/W4220920787;https://openalex.org/W4224006918;https://openalex.org/W4224315676;https://openalex.org/W4225616072;https://openalex.org/W4242937284;https://openalex.org/W4283362028;https://openalex.org/W4286436522;https://openalex.org/W4290043213;https://openalex.org/W4295123363;https://openalex.org/W4297124928;https://openalex.org/W4306353712;https://openalex.org/W4312328070;https://openalex.org/W4313825899;https://openalex.org/W4315701234;https://openalex.org/W4317906761;https://openalex.org/W4365814241;https://openalex.org/W4387584778,Popularity;Bibliometrics;Computer science;Data science;Artificial intelligence;Sustainability;Political science;Library science,COVID-19 diagnosis using AI;Artificial Intelligence in Healthcare and Education;Artificial Intelligence in Healthcare
-OPENALEX,https://openalex.org/W4385222884,https://doi.org/10.1109/access.2023.3298371,,Unleashing the Potential of Blockchain and Machine Learning: Insights and Emerging Trends From Bibliometric Analysis,IEEE ACCESS,IEEE ACCESS,2023,article,en,Chouaib Doukkali University,"Blockchain and machine learning (ML) has garnered growing interest as cutting-edge technologies that have witnessed tremendous strides in their respective domains. Blockchain technology provides a decentralized and immutable ledger, enabling secure and transparent transactions without intermediaries. Alternatively, ML is a sub-field of artificial intelligence (AI) that empowers systems to enhance their performance by learning from data. The integration of these data-driven paradigms holds the potential to reinforce data privacy and security, improve data analysis accuracy, and automate complex processes. The confluence of blockchain and ML has sparked increasing interest among scholars and researchers. Therefore, a bibliometric analysis is carried out to investigate the key focus areas, hotspots, potential prospects, and dynamical aspects of the field. This paper evaluates 700 manuscripts drawn from the Web of Science (WoS) core collection database, spanning from 2017 to 2022. The analysis is conducted using advanced bibliometric tools (e.g., Bibliometrix R, VOSviewer, and CiteSpace) to assess various aspects of the research area regarding publication productivity, influential articles, prolific authors, the productivity of academic countries and institutions, as well as the intellectual structure in terms of hot topics and emerging trends. The findings suggest that upcoming research should focus on blockchain technology, AI-powered 5G networks, industrial cyber-physical systems, IoT environments, and autonomous vehicles. This paper provides a valuable foundation for both academic scholars and practitioners as they contemplate future projects on the integration of blockchain and ML.",11,,78879,78903,"Akrami, 2023, IEEE ACCESS",57,"Akrami, Nouhaila El;Hanine, Mohamed;Flores, Emmanuel Soriano;Aray, Daniel Gavilanes;Ashraf, Imran","Akrami, Nouhaila El;Hanine, Mohamed;Flores, Emmanuel Soriano;Aray, Daniel Gavilanes;Ashraf, Imran",Chouaib Doukkali University;Yeungnam University,https://openalex.org/W1748004804;https://openalex.org/W2086145942;https://openalex.org/W2150220236;https://openalex.org/W2163539724;https://openalex.org/W2201903473;https://openalex.org/W2416848540;https://openalex.org/W2751179194;https://openalex.org/W2755950973;https://openalex.org/W2758225150;https://openalex.org/W2776003688;https://openalex.org/W2805930283;https://openalex.org/W2883790378;https://openalex.org/W2885100636;https://openalex.org/W2886169738;https://openalex.org/W2894710278;https://openalex.org/W2901967082;https://openalex.org/W2907683311;https://openalex.org/W2914212774;https://openalex.org/W2927143629;https://openalex.org/W2944858588;https://openalex.org/W2947400732;https://openalex.org/W2951832089;https://openalex.org/W2962621836;https://openalex.org/W2968042416;https://openalex.org/W2972432684;https://openalex.org/W2974110037;https://openalex.org/W2974429275;https://openalex.org/W2984693664;https://openalex.org/W2986442689;https://openalex.org/W2990688366;https://openalex.org/W2992245519;https://openalex.org/W2997777902;https://openalex.org/W3001491100;https://openalex.org/W3003951943;https://openalex.org/W3009535750;https://openalex.org/W3009627224;https://openalex.org/W3011602168;https://openalex.org/W3012062908;https://openalex.org/W3019945581;https://openalex.org/W3023617420;https://openalex.org/W3026150618;https://openalex.org/W3039940949;https://openalex.org/W3040937085;https://openalex.org/W3047080537;https://openalex.org/W3089288764;https://openalex.org/W3094159242;https://openalex.org/W3118615836;https://openalex.org/W3127873595;https://openalex.org/W3138069867;https://openalex.org/W3156033593;https://openalex.org/W3160856016;https://openalex.org/W3163893137;https://openalex.org/W3164573547;https://openalex.org/W3174012884;https://openalex.org/W3181089261;https://openalex.org/W3182418041;https://openalex.org/W3193492336;https://openalex.org/W3193560119;https://openalex.org/W3202284552;https://openalex.org/W3208801174;https://openalex.org/W3209723277;https://openalex.org/W4210615406;https://openalex.org/W4211068006;https://openalex.org/W4223504512;https://openalex.org/W4225407720;https://openalex.org/W4248175462;https://openalex.org/W4293197680;https://openalex.org/W4293370646;https://openalex.org/W4294215472;https://openalex.org/W4307979480;https://openalex.org/W4309007662;https://openalex.org/W4313404466;https://openalex.org/W4382176573;https://openalex.org/W6754199392,Blockchain;Computer science;Data science;Field (mathematics);Bibliometrics;Productivity;Big data;Knowledge management;World Wide Web;Computer security,Blockchain Technology Applications and Security;Artificial Intelligence in Healthcare and Education
-OPENALEX,https://openalex.org/W3008105883,https://doi.org/10.1080/09537325.2020.1732912,,A review of machine learning for big data analytics: bibliometric approach,TECHNOLOGY ANALYSIS AND STRATEGIC MANAGEMENT,TECHNOLOGY ANALYSIS AND STRATEGIC MANAGEMENT,2020,review,en,King Fahd University of Petroleum and Minerals,"The amalgamation of machine learning and big data has led to a revolution in data science with several influencing applications to various domains. To gain insights on the current research trends on machine learning for big data analytics, this study follows a bibliometric analysis methodology of citation data to review and quantitatively assess the explosion and impact of literature and research performance in this vibrant research area, which has witnessed rapid changes and rising interest in business, industry and academia. Using a variety of bibliometric measures and visualisation techniques, the paper examines and identifies several related issues including research productivity and directions, major contributors, publication trends and growth rates, citation and collaboration analysis, and others. The relevant bibliographic units for the study were collected from the Core Collection of the Web of Science bibliographic database. Nearly all the relevant publications prior to February 2018 were included in the analysis. The overwhelming productivity and wide-spread applications in several multidisciplinary domains have been revealed, with one-to-two ratio of journal to conference publications. Three countries (USA, China, India) are dominating the research output with more than two-thirds of the total productivity.",32,8,984,1005,"El-Alfy, 2020, TECHNOLOGY ANALYSIS AND STRATEGIC MANAGEMENT",42,"El-Alfy, El-Sayed M.;Mohammed, Salahadin","El-Alfy, El-Sayed M.;Mohammed, Salahadin",King Fahd University of Petroleum and Minerals,https://openalex.org/W93660433;https://openalex.org/W607505555;https://openalex.org/W1503398984;https://openalex.org/W1648296995;https://openalex.org/W1971044734;https://openalex.org/W1984020445;https://openalex.org/W1990476851;https://openalex.org/W2000432868;https://openalex.org/W2002643280;https://openalex.org/W2027540165;https://openalex.org/W2028297439;https://openalex.org/W2031075407;https://openalex.org/W2032031606;https://openalex.org/W2032510388;https://openalex.org/W2036785686;https://openalex.org/W2039027543;https://openalex.org/W2040263621;https://openalex.org/W2041588414;https://openalex.org/W2057923756;https://openalex.org/W2060437593;https://openalex.org/W2069656088;https://openalex.org/W2072750586;https://openalex.org/W2083721602;https://openalex.org/W2084898926;https://openalex.org/W2087295784;https://openalex.org/W2089961246;https://openalex.org/W2091143818;https://openalex.org/W2103956991;https://openalex.org/W2106488040;https://openalex.org/W2111791086;https://openalex.org/W2127414816;https://openalex.org/W2128438887;https://openalex.org/W2150220236;https://openalex.org/W2152204876;https://openalex.org/W2160346582;https://openalex.org/W2189203739;https://openalex.org/W2211439825;https://openalex.org/W2219903032;https://openalex.org/W2261525379;https://openalex.org/W2277011713;https://openalex.org/W2285144687;https://openalex.org/W2303147257;https://openalex.org/W2317595875;https://openalex.org/W2333300949;https://openalex.org/W2410932590;https://openalex.org/W2410952352;https://openalex.org/W2416848540;https://openalex.org/W2487200295;https://openalex.org/W2525984666;https://openalex.org/W2557283755;https://openalex.org/W2576683119;https://openalex.org/W2586281947;https://openalex.org/W2592084954;https://openalex.org/W2619769228;https://openalex.org/W2625392185;https://openalex.org/W2759832051;https://openalex.org/W2765743217;https://openalex.org/W2904029666;https://openalex.org/W2952984634;https://openalex.org/W2994602700;https://openalex.org/W3122195984;https://openalex.org/W4236133481;https://openalex.org/W4236362309;https://openalex.org/W4250810012;https://openalex.org/W6628208857;https://openalex.org/W6681348010,Data science;Big data;Productivity;Multidisciplinary approach;Bibliometrics;Variety (cybernetics);Web of science;Computer science;Citation;Analytics;Knowledge management;Library science;Political science;Social science;Data mining;Artificial intelligence;Sociology;MEDLINE,Big Data and Business Intelligence;Explainable Artificial Intelligence (XAI);Machine Learning and Data Classification
-OPENALEX,https://openalex.org/W4360602925,https://doi.org/10.1016/j.techfore.2023.122516,,Towards a precise understanding of social entrepreneurship: An integrated bibliometric–machine learning based review and research agenda,TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE,TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE,2023,article,en,Indian Institute of Management Kashipur,,191,,122516,122516,"Kaushik, 2023, TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE",49,"Kaushik, Vineet;Tewari, Shobha;Sahasranamam, Sreevas;Hota, Pradeep Kumar","Kaushik, Vineet;Tewari, Shobha;Sahasranamam, Sreevas;Hota, Pradeep Kumar",Indian Institute of Management Kashipur;University of Strathclyde;Australian National University;Thapar Institute of Engineering & Technology,https://openalex.org/W1028824834;https://openalex.org/W1510559280;https://openalex.org/W1572181180;https://openalex.org/W1748004804;https://openalex.org/W1880262756;https://openalex.org/W1963798920;https://openalex.org/W1964283292;https://openalex.org/W1965746216;https://openalex.org/W1965891434;https://openalex.org/W1969639777;https://openalex.org/W1975892393;https://openalex.org/W1983120063;https://openalex.org/W1984493033;https://openalex.org/W1991625875;https://openalex.org/W1992616122;https://openalex.org/W2000513447;https://openalex.org/W2001082470;https://openalex.org/W2002906934;https://openalex.org/W2003915431;https://openalex.org/W2005706022;https://openalex.org/W2010155324;https://openalex.org/W2016014531;https://openalex.org/W2016400365;https://openalex.org/W2018027932;https://openalex.org/W2029852869;https://openalex.org/W2032258519;https://openalex.org/W2032679313;https://openalex.org/W2033521208;https://openalex.org/W2045583177;https://openalex.org/W2052755854;https://openalex.org/W2063009632;https://openalex.org/W2074929719;https://openalex.org/W2087748205;https://openalex.org/W2088973677;https://openalex.org/W2089229253;https://openalex.org/W2099518833;https://openalex.org/W2100579062;https://openalex.org/W2105974272;https://openalex.org/W2106529082;https://openalex.org/W2110849810;https://openalex.org/W2117851943;https://openalex.org/W2120052504;https://openalex.org/W2123184168;https://openalex.org/W2137261963;https://openalex.org/W2145535121;https://openalex.org/W2151266951;https://openalex.org/W2156866271;https://openalex.org/W2164794839;https://openalex.org/W2167017067;https://openalex.org/W2171711960;https://openalex.org/W2223092947;https://openalex.org/W2313148098;https://openalex.org/W2338878073;https://openalex.org/W2341001902;https://openalex.org/W2343306044;https://openalex.org/W2358029040;https://openalex.org/W2389823280;https://openalex.org/W2403226181;https://openalex.org/W2407064033;https://openalex.org/W2409435105;https://openalex.org/W2410989775;https://openalex.org/W2433091214;https://openalex.org/W2466112528;https://openalex.org/W2466255356;https://openalex.org/W2476158099;https://openalex.org/W2489321591;https://openalex.org/W2493521008;https://openalex.org/W2508421801;https://openalex.org/W2519380966;https://openalex.org/W2519651563;https://openalex.org/W2523495911;https://openalex.org/W2569805648;https://openalex.org/W2580318018;https://openalex.org/W2581762463;https://openalex.org/W2582743722;https://openalex.org/W2615875132;https://openalex.org/W2618934163;https://openalex.org/W2626595870;https://openalex.org/W2740027236;https://openalex.org/W2750217113;https://openalex.org/W2755950973;https://openalex.org/W2766385364;https://openalex.org/W2767990394;https://openalex.org/W2779199041;https://openalex.org/W2790855988;https://openalex.org/W2791159914;https://openalex.org/W2793028062;https://openalex.org/W2794439850;https://openalex.org/W2808294899;https://openalex.org/W2808432859;https://openalex.org/W2808621201;https://openalex.org/W2887927457;https://openalex.org/W2888388036;https://openalex.org/W2888560922;https://openalex.org/W2891394434;https://openalex.org/W2904465638;https://openalex.org/W2905024335;https://openalex.org/W2911878381;https://openalex.org/W2913103480;https://openalex.org/W2913328436;https://openalex.org/W2914584698;https://openalex.org/W2915442508;https://openalex.org/W2918064950;https://openalex.org/W2930282685;https://openalex.org/W2943121181;https://openalex.org/W2950207025;https://openalex.org/W2952136798;https://openalex.org/W2955250480;https://openalex.org/W2955502065;https://openalex.org/W2956252831;https://openalex.org/W2964055349;https://openalex.org/W2965284208;https://openalex.org/W2969080693;https://openalex.org/W2986872232;https://openalex.org/W2987412628;https://openalex.org/W2992432394;https://openalex.org/W2994736955;https://openalex.org/W3001470405;https://openalex.org/W3002469857;https://openalex.org/W3004629351;https://openalex.org/W3007909602;https://openalex.org/W3008990675;https://openalex.org/W3015590642;https://openalex.org/W3016640877;https://openalex.org/W3017179834;https://openalex.org/W3020429242;https://openalex.org/W3022164605;https://openalex.org/W3024351404;https://openalex.org/W3036439430;https://openalex.org/W3041589567;https://openalex.org/W3041660485;https://openalex.org/W3081215934;https://openalex.org/W3082026966;https://openalex.org/W3091367238;https://openalex.org/W3091702329;https://openalex.org/W3093532984;https://openalex.org/W3096513170;https://openalex.org/W3106937276;https://openalex.org/W3110197688;https://openalex.org/W3110889339;https://openalex.org/W3111120448;https://openalex.org/W3112697039;https://openalex.org/W3121439200;https://openalex.org/W3122428671;https://openalex.org/W3122843079;https://openalex.org/W3125041370;https://openalex.org/W3125043858;https://openalex.org/W3125061824;https://openalex.org/W3125481785;https://openalex.org/W3128170268;https://openalex.org/W3132837365;https://openalex.org/W3136235606;https://openalex.org/W3146127354;https://openalex.org/W3155134972;https://openalex.org/W3158616546;https://openalex.org/W3163595176;https://openalex.org/W3180022188;https://openalex.org/W3204882556;https://openalex.org/W3206543326;https://openalex.org/W3207804609;https://openalex.org/W4200217033;https://openalex.org/W4200508449;https://openalex.org/W4205645601;https://openalex.org/W4210500691;https://openalex.org/W4224075166;https://openalex.org/W4255767049;https://openalex.org/W4280528531;https://openalex.org/W4281922099;https://openalex.org/W4287980879;https://openalex.org/W4300496334;https://openalex.org/W4302990361;https://openalex.org/W4391004721;https://openalex.org/W6639619044;https://openalex.org/W6646444251;https://openalex.org/W6654438158;https://openalex.org/W6658559677;https://openalex.org/W6674869481;https://openalex.org/W6676738589;https://openalex.org/W6698705156;https://openalex.org/W6703074907;https://openalex.org/W6703090467;https://openalex.org/W6703644816;https://openalex.org/W6706879832;https://openalex.org/W6724110810;https://openalex.org/W6739614057;https://openalex.org/W6753963203;https://openalex.org/W6754082054;https://openalex.org/W6757141311;https://openalex.org/W6759345062;https://openalex.org/W6770223108;https://openalex.org/W6780100161;https://openalex.org/W6784661831;https://openalex.org/W6786539349;https://openalex.org/W6792390223;https://openalex.org/W6792912639;https://openalex.org/W6794142887;https://openalex.org/W6795082674;https://openalex.org/W6803000716;https://openalex.org/W6987714012,Latent Dirichlet allocation;Topic model;Scopus;Bibliometrics;Data science;Computer science;Entrepreneurship;Citation;Systematic review;Field (mathematics);Domain (mathematical analysis);Latent semantic analysis;Scientometrics;Citation analysis;Knowledge management;Artificial intelligence;World Wide Web;Political science;MEDLINE,Entrepreneurship Studies and Influences;Community Development and Social Impact;Private Equity and Venture Capital
-OPENALEX,https://openalex.org/W4404592056,https://doi.org/10.1021/acsanm.4c04940,,Machine Learning-Driven Multidomain Nanomaterial Design: From Bibliometric Analysis to Applications,ACS APPLIED NANO MATERIALS,ACS APPLIED NANO MATERIALS,2024,article,en,Yan'an University,"Machine learning (ML), as an advanced data analysis tool, simulates the learning process of the human brain, enabling the extraction of features, discovery of patterns, and making accurate predictions or decisions from complex data. In the field of nanomaterial design, the application of ML technology not only accelerates the discovery and performance optimization of nanomaterials but also promotes the innovation of materials science research methods. Bibliometrics, as a research method based on quantitative analysis, provides us with a macro perspective to observe and understand the application of ML technology in nanomaterial design by statistically analyzing various indicators in the scientific literature. This paper quantitatively analyzes the literature related to ML-driven nanomaterial design from seven dimensions, revealing the importance and necessity of ML technology in nanomaterial design. It also systematically analyzes the diversified applications of the combination of ML technology and nanomaterial technology with the design of suitable ML algorithms being key to enhancing the performance of nanomaterials. In addition, this paper discusses current challenges and future development directions, including data quality and data set construction, algorithm innovation and optimization, and the deepening of interdisciplinary cooperation. This review not only provides researchers with a macro perspective to observe the current state and development trends of the field but also provides ideas and suggestions for future research. This is of significant importance and value for promoting scientific progress in the field of nanomaterial design, fostering the in-depth development of interdisciplinary research, and accelerating the innovative application of material technologies.",7,23,26579,26600,"Wang, 2024, ACS APPLIED NANO MATERIALS",59,"Wang, Hong;Cao, Hengyu;Yang, Liang","Wang, Hong;Cao, Hengyu;Yang, Liang",Yan'an University,https://openalex.org/W1876947117;https://openalex.org/W2150220236;https://openalex.org/W2339093665;https://openalex.org/W2498987500;https://openalex.org/W2609132363;https://openalex.org/W2731422849;https://openalex.org/W2780966672;https://openalex.org/W2782058610;https://openalex.org/W2804226360;https://openalex.org/W2883482411;https://openalex.org/W2884430236;https://openalex.org/W2884985123;https://openalex.org/W2894463230;https://openalex.org/W2904207857;https://openalex.org/W2944415948;https://openalex.org/W2952126818;https://openalex.org/W2963453445;https://openalex.org/W2985869506;https://openalex.org/W2996191685;https://openalex.org/W3007217854;https://openalex.org/W3007991303;https://openalex.org/W3012301245;https://openalex.org/W3016152816;https://openalex.org/W3031399045;https://openalex.org/W3040552910;https://openalex.org/W3045163139;https://openalex.org/W3094089673;https://openalex.org/W3095028680;https://openalex.org/W3107412307;https://openalex.org/W3113917069;https://openalex.org/W3118215188;https://openalex.org/W3120960655;https://openalex.org/W3127253295;https://openalex.org/W3134788569;https://openalex.org/W3184635088;https://openalex.org/W3209974011;https://openalex.org/W3214207699;https://openalex.org/W3216978572;https://openalex.org/W4200303692;https://openalex.org/W4200480891;https://openalex.org/W4200587103;https://openalex.org/W4205977780;https://openalex.org/W4220710693;https://openalex.org/W4220908012;https://openalex.org/W4220911441;https://openalex.org/W4220940332;https://openalex.org/W4280583100;https://openalex.org/W4283312514;https://openalex.org/W4283373613;https://openalex.org/W4285890348;https://openalex.org/W4288969942;https://openalex.org/W4289317700;https://openalex.org/W4293056493;https://openalex.org/W4294287005;https://openalex.org/W4295441317;https://openalex.org/W4295779105;https://openalex.org/W4295864679;https://openalex.org/W4296071376;https://openalex.org/W4306645378;https://openalex.org/W4309668086;https://openalex.org/W4313680957;https://openalex.org/W4319830947;https://openalex.org/W4323033656;https://openalex.org/W4323304766;https://openalex.org/W4376126638;https://openalex.org/W4378417902;https://openalex.org/W4378675844;https://openalex.org/W4379094177;https://openalex.org/W4379645088;https://openalex.org/W4380078168;https://openalex.org/W4380758787;https://openalex.org/W4381827788;https://openalex.org/W4382240865;https://openalex.org/W4383199314;https://openalex.org/W4384661668;https://openalex.org/W4386042036;https://openalex.org/W4386204390;https://openalex.org/W4388049043;https://openalex.org/W4388070567;https://openalex.org/W4389328776;https://openalex.org/W4390607201;https://openalex.org/W4390750649;https://openalex.org/W4390919496;https://openalex.org/W4391014981;https://openalex.org/W4391560002;https://openalex.org/W4391848991;https://openalex.org/W4391876345;https://openalex.org/W4391918971;https://openalex.org/W4392346759;https://openalex.org/W4392799655;https://openalex.org/W4393132067;https://openalex.org/W4394893529;https://openalex.org/W4394945461;https://openalex.org/W4397292025;https://openalex.org/W4398137864;https://openalex.org/W4398177352;https://openalex.org/W4398770837;https://openalex.org/W4399661414;https://openalex.org/W4400244247;https://openalex.org/W4400477326;https://openalex.org/W4400804197;https://openalex.org/W4401990043;https://openalex.org/W4402519652;https://openalex.org/W4403378563,Computer science;Nanotechnology;Data science;Materials science,Machine Learning in Materials Science;Computational Drug Discovery Methods;Electronic and Structural Properties of Oxides
-OPENALEX,https://openalex.org/W3109709573,https://doi.org/10.2196/23703,https://pubmed.ncbi.nlm.nih.gov/33600346,A Comprehensive Overview of the COVID-19 Literature: Machine Learning–Based Bibliometric Analysis,JOURNAL OF MEDICAL INTERNET RESEARCH,JOURNAL OF MEDICAL INTERNET RESEARCH,2020,review,en,Hamad bin Khalifa University,"BACKGROUND: Shortly after the emergence of COVID-19, researchers rapidly mobilized to study numerous aspects of the disease such as its evolution, clinical manifestations, effects, treatments, and vaccinations. This led to a rapid increase in the number of COVID-19-related publications. Identifying trends and areas of interest using traditional review methods (eg, scoping and systematic reviews) for such a large domain area is challenging. OBJECTIVE: We aimed to conduct an extensive bibliometric analysis to provide a comprehensive overview of the COVID-19 literature. METHODS: We used the COVID-19 Open Research Dataset (CORD-19) that consists of a large number of research articles related to all coronaviruses. We used a machine learning-based method to analyze the most relevant COVID-19-related articles and extracted the most prominent topics. Specifically, we used a clustering algorithm to group published articles based on the similarity of their abstracts to identify research hotspots and current research directions. We have made our software accessible to the community via GitHub. RESULTS: Of the 196,630 publications retrieved from the database, we included 28,904 in our analysis. The mean number of weekly publications was 990 (SD 789.3). The country that published the highest number of COVID-19-related articles was China (2950/17,270, 17.08%). The highest number of articles were published in bioRxiv. Lei Liu affiliated with the Southern University of Science and Technology in China published the highest number of articles (n=46). Based on titles and abstracts alone, we were able to identify 1515 surveys, 733 systematic reviews, 512 cohort studies, 480 meta-analyses, and 362 randomized control trials. We identified 19 different topics covered among the publications reviewed. The most dominant topic was public health response, followed by clinical care practices during the COVID-19 pandemic, clinical characteristics and risk factors, and epidemic models for its spread. CONCLUSIONS: We provide an overview of the COVID-19 literature and have identified current hotspots and research directions. Our findings can be useful for the research community to help prioritize research needs and recognize leading COVID-19 researchers, institutes, countries, and publishers. Our study shows that an AI-based bibliometric analysis has the potential to rapidly explore a large corpus of academic publications during a public health crisis. We believe that this work can be used to analyze other eHealth-related literature to help clinicians, administrators, and policy makers to obtain a holistic view of the literature and be able to categorize different topics of the existing research for further analyses. It can be further scaled (for instance, in time) to clinical summary documentation. Publishers should avoid noise in the data by developing a way to trace the evolution of individual publications and unique authors.",23,3,e23703,e23703,"Abd‐Alrazaq, 2020, JOURNAL OF MEDICAL INTERNET RESEARCH",63,"Abd‐Alrazaq, Alaa;Schneider, Jens;Mifsud, Borbála;Alam, Tanvir;Househ, Mowafa;Hamdi, Mounir;Shah, Zubair","Abd‐Alrazaq, Alaa;Schneider, Jens;Mifsud, Borbála;Alam, Tanvir;Househ, Mowafa;Hamdi, Mounir;Shah, Zubair",Hamad bin Khalifa University;Qatar Foundation,https://openalex.org/W2902756944;https://openalex.org/W3004172287;https://openalex.org/W3008963226;https://openalex.org/W3011075241;https://openalex.org/W3011866596;https://openalex.org/W3011940380;https://openalex.org/W3012080801;https://openalex.org/W3012333977;https://openalex.org/W3012882790;https://openalex.org/W3012921363;https://openalex.org/W3013585706;https://openalex.org/W3013627312;https://openalex.org/W3013917296;https://openalex.org/W3013933578;https://openalex.org/W3014667363;https://openalex.org/W3014874133;https://openalex.org/W3014989172;https://openalex.org/W3015137485;https://openalex.org/W3015320793;https://openalex.org/W3015506441;https://openalex.org/W3015538318;https://openalex.org/W3016713881;https://openalex.org/W3016743394;https://openalex.org/W3016853506;https://openalex.org/W3016998206;https://openalex.org/W3017185760;https://openalex.org/W3017771577;https://openalex.org/W3017981638;https://openalex.org/W3020236154;https://openalex.org/W3020571666;https://openalex.org/W3022417061;https://openalex.org/W3022605724;https://openalex.org/W3022687260;https://openalex.org/W3022853666;https://openalex.org/W3023309767;https://openalex.org/W3023617420;https://openalex.org/W3023730836;https://openalex.org/W3023787531;https://openalex.org/W3023837380;https://openalex.org/W3024219744;https://openalex.org/W3025004229;https://openalex.org/W3025009722;https://openalex.org/W3025195557;https://openalex.org/W3025398491;https://openalex.org/W3025798952;https://openalex.org/W3025947019;https://openalex.org/W3027888856;https://openalex.org/W3028532970;https://openalex.org/W3028746115;https://openalex.org/W3029693220;https://openalex.org/W3030072408;https://openalex.org/W3030381168;https://openalex.org/W3030569139;https://openalex.org/W3031378866;https://openalex.org/W3031399436;https://openalex.org/W3032567804;https://openalex.org/W3032906137;https://openalex.org/W3033399633;https://openalex.org/W3033402847;https://openalex.org/W3033459419;https://openalex.org/W3033490986;https://openalex.org/W3033511704;https://openalex.org/W3033656034;https://openalex.org/W3033995497;https://openalex.org/W3034094473;https://openalex.org/W3034168389;https://openalex.org/W3034823399;https://openalex.org/W3035086245;https://openalex.org/W3035358684;https://openalex.org/W3035738908;https://openalex.org/W3036441990;https://openalex.org/W3036565334;https://openalex.org/W3036603673;https://openalex.org/W3036720715;https://openalex.org/W3036766830;https://openalex.org/W3037019683;https://openalex.org/W3037645176;https://openalex.org/W3037651109;https://openalex.org/W3038553353;https://openalex.org/W3039652957;https://openalex.org/W3040374382;https://openalex.org/W3040542574;https://openalex.org/W3040775624;https://openalex.org/W3040877555;https://openalex.org/W3041093092;https://openalex.org/W3041250651;https://openalex.org/W3041821205;https://openalex.org/W3041878244;https://openalex.org/W3042091277;https://openalex.org/W3042239856;https://openalex.org/W3042248855;https://openalex.org/W3042399641;https://openalex.org/W3042710560;https://openalex.org/W3042898756;https://openalex.org/W3042924126;https://openalex.org/W3043065230;https://openalex.org/W3043224541;https://openalex.org/W3043415253;https://openalex.org/W3043618167;https://openalex.org/W3043697210;https://openalex.org/W3043746245;https://openalex.org/W3043764490;https://openalex.org/W3044209152;https://openalex.org/W3044846600;https://openalex.org/W3044892471;https://openalex.org/W3045956845;https://openalex.org/W3047747926;https://openalex.org/W3086236016;https://openalex.org/W3124760710;https://openalex.org/W4220693343;https://openalex.org/W4225989008;https://openalex.org/W4230559783;https://openalex.org/W4236122429;https://openalex.org/W4236624053;https://openalex.org/W4239351965;https://openalex.org/W4250704681,Coronavirus disease 2019 (COVID-19);Bibliometrics;Data science;Systematic review;MEDLINE;Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2);Cluster analysis;2019-20 coronavirus outbreak;Computer science;Library science;Medicine;Artificial intelligence;Disease;Political science;Infectious disease (medical specialty);Pathology,COVID-19 Clinical Research Studies;Academic Publishing and Open Access;COVID-19 epidemiological studies
-OPENALEX,https://openalex.org/W2904029666,https://doi.org/10.1016/j.engappai.2018.11.007,,Industry 4.0: A bibliometric analysis and detailed overview,ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2018,article,en,South Asian University,,78,,218,235,"Muhuri, 2018, ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE",542,"Muhuri, Pranab K.;Shukla, Amit K.;Abraham, Ajith","Muhuri, Pranab K.;Shukla, Amit K.;Abraham, Ajith",South Asian University;Machine Intelligence Research Labs,https://openalex.org/W203466237;https://openalex.org/W368346290;https://openalex.org/W599150255;https://openalex.org/W752225299;https://openalex.org/W762013013;https://openalex.org/W916305875;https://openalex.org/W1613130854;https://openalex.org/W1627723619;https://openalex.org/W1761827578;https://openalex.org/W1865772772;https://openalex.org/W1911193768;https://openalex.org/W1967904196;https://openalex.org/W1969324951;https://openalex.org/W1972727474;https://openalex.org/W1975309891;https://openalex.org/W1996654773;https://openalex.org/W2001377707;https://openalex.org/W2003443078;https://openalex.org/W2014939786;https://openalex.org/W2020623523;https://openalex.org/W2020851875;https://openalex.org/W2021227483;https://openalex.org/W2029608738;https://openalex.org/W2035922091;https://openalex.org/W2044049559;https://openalex.org/W2053401910;https://openalex.org/W2053730785;https://openalex.org/W2069134922;https://openalex.org/W2070665593;https://openalex.org/W2078500726;https://openalex.org/W2100297710;https://openalex.org/W2110837847;https://openalex.org/W2133720971;https://openalex.org/W2175330946;https://openalex.org/W2177391779;https://openalex.org/W2220043696;https://openalex.org/W2241928397;https://openalex.org/W2253828178;https://openalex.org/W2254680660;https://openalex.org/W2254884657;https://openalex.org/W2262056676;https://openalex.org/W2263682169;https://openalex.org/W2269884114;https://openalex.org/W2270075604;https://openalex.org/W2275696275;https://openalex.org/W2286121906;https://openalex.org/W2286131889;https://openalex.org/W2286737823;https://openalex.org/W2286761208;https://openalex.org/W2291031992;https://openalex.org/W2295939521;https://openalex.org/W2296597512;https://openalex.org/W2303531378;https://openalex.org/W2309748694;https://openalex.org/W2314026516;https://openalex.org/W2317016330;https://openalex.org/W2321296019;https://openalex.org/W2322277786;https://openalex.org/W2325381945;https://openalex.org/W2329719719;https://openalex.org/W2330421065;https://openalex.org/W2330559246;https://openalex.org/W2334044903;https://openalex.org/W2339884137;https://openalex.org/W2341523100;https://openalex.org/W2364839527;https://openalex.org/W2396381190;https://openalex.org/W2400465853;https://openalex.org/W2401926108;https://openalex.org/W2402219588;https://openalex.org/W2409048579;https://openalex.org/W2419409856;https://openalex.org/W2429580764;https://openalex.org/W2463378445;https://openalex.org/W2464269931;https://openalex.org/W2465639267;https://openalex.org/W2467389505;https://openalex.org/W2491710580;https://openalex.org/W2493545044;https://openalex.org/W2493990996;https://openalex.org/W2497722157;https://openalex.org/W2498966538;https://openalex.org/W2507539066;https://openalex.org/W2507578125;https://openalex.org/W2509189416;https://openalex.org/W2510192511;https://openalex.org/W2510221533;https://openalex.org/W2510393639;https://openalex.org/W2511169723;https://openalex.org/W2512081298;https://openalex.org/W2514802974;https://openalex.org/W2517238017;https://openalex.org/W2517803083;https://openalex.org/W2518724857;https://openalex.org/W2518789614;https://openalex.org/W2519166994;https://openalex.org/W2519832518;https://openalex.org/W2520848981;https://openalex.org/W2522676763;https://openalex.org/W2522848878;https://openalex.org/W2528223815;https://openalex.org/W2529256613;https://openalex.org/W2530636115;https://openalex.org/W2530806083;https://openalex.org/W2531117592;https://openalex.org/W2531237699;https://openalex.org/W2532493213;https://openalex.org/W2533615014;https://openalex.org/W2535680912;https://openalex.org/W2536185349;https://openalex.org/W2538959687;https://openalex.org/W2541556211;https://openalex.org/W2543552616;https://openalex.org/W2549899326;https://openalex.org/W2550063643;https://openalex.org/W2550133903;https://openalex.org/W2550758296;https://openalex.org/W2551586735;https://openalex.org/W2553173084;https://openalex.org/W2560319175;https://openalex.org/W2562031796;https://openalex.org/W2562594539;https://openalex.org/W2564000999;https://openalex.org/W2565337694;https://openalex.org/W2566246429;https://openalex.org/W2576683550;https://openalex.org/W2578148477;https://openalex.org/W2579952554;https://openalex.org/W2581003607;https://openalex.org/W2587530705;https://openalex.org/W2588128257;https://openalex.org/W2588244098;https://openalex.org/W2588265266;https://openalex.org/W2588941434;https://openalex.org/W2589106234;https://openalex.org/W2589264714;https://openalex.org/W2591879378;https://openalex.org/W2593168467;https://openalex.org/W2596115488;https://openalex.org/W2597937851;https://openalex.org/W2598195321;https://openalex.org/W2598614965;https://openalex.org/W2602722581;https://openalex.org/W2603008685;https://openalex.org/W2604047614;https://openalex.org/W2605350360;https://openalex.org/W2605396005;https://openalex.org/W2605617766;https://openalex.org/W2605859962;https://openalex.org/W2606007596;https://openalex.org/W2606614575;https://openalex.o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science;Field (mathematics);Bibliometrics;Industrial Revolution;Automation;Data science;Citation;Citation analysis;Subject (documents);Operations research;Engineering management;Library science,Digital Transformation in Industry
-OPENALEX,https://openalex.org/W4313575091,https://doi.org/10.3390/su15020967,,Evolution of Green Finance: A Bibliometric Analysis through Complex Networks and Machine Learning,SUSTAINABILITY,SUSTAINABILITY,2023,article,en,Universidade Estadual de Campinas (UNICAMP),"A fundamental structural transformation that must occur to break global temperature rise and advance sustainable development is the green transition to a low-carbon system. However, dismantling the carbon lock-in situation requires substantial investment in green finance. Historically, investments have been concentrated in carbon-intensive technologies. Nonetheless, green finance has blossomed in recent years, and efforts to organise this literature have emerged, but a deeper understanding of this growing field is needed. For this goal, this paper aims to delineate this literature’s existing groups and explore its heterogeneity. From a bibliometric coupling network, we identified the main groups in the literature; then, we described the characteristics of these articles through a novel combination of complex network analysis, topological measures, and a type of unsupervised machine learning technique called structural topic modelling (STM). The use of computational methods to explore literature trends is increasing as it is expected to be compatible with a large amount of information and complement the expert-based knowledge approach. The contribution of this article is twofold: first, identifying the most relevant articles in the network related to each group and, second, the most prestigious topics in the field and their contributions to the literature. A final sample of 3275 articles shows three main groups in the literature. The more mature is mainly related to the distribution of climate finance from the developed to the developing world. In contrast, the most recent ones are related to climate financial risks, green bonds, and the insertion of financial development in energy-emissions-economics models. Researchers and policy-makers can recognise current research challenges and make better decisions with the help of the central research topics and emerging trends identified from STM. The field’s evolution shows a clear movement from an international perspective to a nationally-determined discussion on finance to the green transition.",15,2,967,967,"Maria, 2023, SUSTAINABILITY",58,"Maria, Mariana Rêis;Ballini, Rosângela;Souza, Roney Fraga","Maria, Mariana Rêis;Ballini, Rosângela;Souza, Roney Fraga",Universidade Estadual de Campinas (UNICAMP);Universidade Federal de Mato Grosso,https://openalex.org/W229097380;https://openalex.org/W1841824908;https://openalex.org/W1880262756;https://openalex.org/W1907286193;https://openalex.org/W1964959681;https://openalex.org/W1970859146;https://openalex.org/W1976092690;https://openalex.org/W1997712852;https://openalex.org/W2001973399;https://openalex.org/W2009284938;https://openalex.org/W2017987256;https://openalex.org/W2019302301;https://openalex.org/W2026686386;https://openalex.org/W2029863173;https://openalex.org/W2032556264;https://openalex.org/W2035713188;https://openalex.org/W2040546518;https://openalex.org/W2045398038;https://openalex.org/W2048624956;https://openalex.org/W2053499293;https://openalex.org/W2055800293;https://openalex.org/W2055908663;https://openalex.org/W2056541886;https://openalex.org/W2060336209;https://openalex.org/W2069787981;https://openalex.org/W2070779353;https://openalex.org/W2077579219;https://openalex.org/W2083084326;https://openalex.org/W2096885696;https://openalex.org/W2097854309;https://openalex.org/W2104834906;https://openalex.org/W2109890836;https://openalex.org/W2125662152;https://openalex.org/W2131681506;https://openalex.org/W2150220236;https://openalex.org/W2152785505;https://openalex.org/W2158290178;https://openalex.org/W2159855843;https://openalex.org/W2174670974;https://openalex.org/W2174706414;https://openalex.org/W2179362017;https://openalex.org/W2223092947;https://openalex.org/W2259853171;https://openalex.org/W2287244756;https://openalex.org/W2306119308;https://openalex.org/W2330768275;https://openalex.org/W2347125256;https://openalex.org/W2508555133;https://openalex.org/W2555074168;https://openalex.org/W2561644322;https://openalex.org/W2568810957;https://openalex.org/W2586827268;https://openalex.org/W2607002712;https://openalex.org/W2617775694;https://openalex.org/W2755950973;https://openalex.org/W2756564215;https://openalex.org/W2774944491;https://openalex.org/W2781571166;https://openalex.org/W2789511191;https://openalex.org/W2792963110;https://openalex.org/W2796870799;https://openalex.org/W2804677512;https://openalex.org/W2806486862;https://openalex.org/W2884657803;https://openalex.org/W2896699713;https://openalex.org/W2898756147;https://openalex.org/W2906323621;https://openalex.org/W2915161772;https://openalex.org/W2916047775;https://openalex.org/W2940636459;https://openalex.org/W2946881517;https://openalex.org/W2953977783;https://openalex.org/W2963824633;https://openalex.org/W2978802814;https://openalex.org/W3003671649;https://openalex.org/W3003900694;https://openalex.org/W3099768174;https://openalex.org/W3106437531;https://openalex.org/W3154067860;https://openalex.org/W3207458606;https://openalex.org/W3210595485;https://openalex.org/W4226061782;https://openalex.org/W4241563384;https://openalex.org/W4255497883;https://openalex.org/W4283078861;https://openalex.org/W4384347680;https://openalex.org/W6608852248;https://openalex.org/W6639619044;https://openalex.org/W6735852552;https://openalex.org/W6744394771,Field (mathematics);Bibliometrics;Investment (military);Sample (material);Finance;Artificial intelligence;Computer science;Data science;Economics;Political science;Politics;Data mining;Chemistry,"Energy, Environment, Economic Growth;Sustainable Finance and Green Bonds;Climate Change Policy and Economics"
-OPENALEX,https://openalex.org/W3193560119,https://doi.org/10.1155/2021/9739219,https://pubmed.ncbi.nlm.nih.gov/34426765,Literature Review on the Applications of Machine Learning and Blockchain Technology in Smart Healthcare Industry: A Bibliometric Analysis,JOURNAL OF HEALTHCARE ENGINEERING,JOURNAL OF HEALTHCARE ENGINEERING,2021,review,en,Jilin University,"The emergence of machine learning (ML) and blockchain (BC) technology has greatly enriched the functions and services of healthcare, giving birth to the new field of ""smart healthcare."" This study aims to review the application of ML and BC technology in the smart medical industry by Web of Science (WOS) using bibliometric visualization. Through our research, we identify the countries with the greatest output, the major research subjects, funding funds, and the research hotspots in this field. We also find out the key themes and future research areas in application of ML and BC technology in healthcare area. We reveal the different aspects of research under the two technologies and how they relate to each other around five themes.",2021,,1,11,"Li, 2021, JOURNAL OF HEALTHCARE ENGINEERING",48,"Li, Yang;Shan, Biaoan;Li, Beiwei;Liu, Xiaoju;Pu, Yi","Li, Yang;Shan, Biaoan;Li, Beiwei;Liu, Xiaoju;Pu, Yi",Jilin University,https://openalex.org/W1425868093;https://openalex.org/W1748004804;https://openalex.org/W2076587863;https://openalex.org/W2134295053;https://openalex.org/W2165022267;https://openalex.org/W2777105798;https://openalex.org/W2782540689;https://openalex.org/W2793933216;https://openalex.org/W2887500871;https://openalex.org/W2902123208;https://openalex.org/W2945874621;https://openalex.org/W2946228717;https://openalex.org/W2951578881;https://openalex.org/W2966450377;https://openalex.org/W2997208348;https://openalex.org/W2998720941;https://openalex.org/W3017174021;https://openalex.org/W3023711887;https://openalex.org/W3024489700;https://openalex.org/W3035227021;https://openalex.org/W3038010422;https://openalex.org/W3038946282;https://openalex.org/W3042243359;https://openalex.org/W3046507381;https://openalex.org/W3080705207;https://openalex.org/W3089981751;https://openalex.org/W3093982442;https://openalex.org/W3095916600;https://openalex.org/W3112894576;https://openalex.org/W3118261252;https://openalex.org/W3119252589;https://openalex.org/W3166799498;https://openalex.org/W3168933251;https://openalex.org/W3170352655;https://openalex.org/W4233812631;https://openalex.org/W4236962467;https://openalex.org/W4249909568,Healthcare industry;Field (mathematics);Health care;Blockchain;Data science;Knowledge management;Bibliometrics;Web of science;Visualization;Computer science;Business;Engineering management;MEDLINE;Engineering;World Wide Web;Artificial intelligence;Political science;Economic growth;Economics;Computer security,Blockchain Technology Applications and Security;Artificial Intelligence in Healthcare;Big Data and Business Intelligence
-OPENALEX,https://openalex.org/W3125603166,https://doi.org/10.1016/j.jacc.2020.11.030,https://pubmed.ncbi.nlm.nih.gov/33478654,Machine Learning and the Future of Cardiovascular Care,JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY,JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY,2021,review,en,Beth Israel Deaconess Medical Center,"The role of physicians has always been to synthesize the data available to them to identify diagnostic patterns that guide treatment and follow response. Today, increasingly sophisticated machine learning algorithms may grow to support clinical experts in some of these tasks. Machine learning has the potential to benefit patients and cardiologists, but only if clinicians take an active role in bringing these new algorithms into practice. The aim of this review is to introduce clinicians who are not data science experts to key concepts in machine learning that will allow them to better understand the field and evaluate new literature and developments. The current published data in machine learning for cardiovascular disease is then summarized, using both a bibliometric survey, with code publicly available to enable similar analysis for any research topic of interest, and select case studies. Finally, several ways that clinicians can and must be involved in this emerging field are presented.",77,3,300,313,"Quer, 2021, JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY",326,"Quer, Giorgio;Arnaout, Ramy;Arnaout, Ramy;Henne, Michael;Arnaout, Rima;Arnaout, Rima","Quer, Giorgio;Arnaout, Ramy;Arnaout, Ramy;Henne, Michael;Arnaout, Rima;Arnaout, Rima","Twitter (United States);Scripps Research Institute;Beth Israel Deaconess Medical Center;Lahey Medical Center;University of California, San Francisco;Intel (United States)",https://openalex.org/W98627379;https://openalex.org/W1839682376;https://openalex.org/W2032133654;https://openalex.org/W2067843516;https://openalex.org/W2177870565;https://openalex.org/W2273849032;https://openalex.org/W2404901863;https://openalex.org/W2557738935;https://openalex.org/W2567103357;https://openalex.org/W2580957850;https://openalex.org/W2581082771;https://openalex.org/W2592007282;https://openalex.org/W2611001834;https://openalex.org/W2622817237;https://openalex.org/W2752747624;https://openalex.org/W2758343255;https://openalex.org/W2758348074;https://openalex.org/W2784094750;https://openalex.org/W2807593075;https://openalex.org/W2807941755;https://openalex.org/W2810809154;https://openalex.org/W2811374795;https://openalex.org/W2887680499;https://openalex.org/W2887691119;https://openalex.org/W2890189169;https://openalex.org/W2896568293;https://openalex.org/W2899921784;https://openalex.org/W2901226889;https://openalex.org/W2901383458;https://openalex.org/W2902644322;https://openalex.org/W2913238351;https://openalex.org/W2915232829;https://openalex.org/W2917055433;https://openalex.org/W2921335396;https://openalex.org/W2929359750;https://openalex.org/W2934399013;https://openalex.org/W2937671098;https://openalex.org/W2941006887;https://openalex.org/W2945147429;https://openalex.org/W2945976633;https://openalex.org/W2948522751;https://openalex.org/W2955653438;https://openalex.org/W2957824372;https://openalex.org/W2961396908;https://openalex.org/W2962231994;https://openalex.org/W2962734274;https://openalex.org/W2963048752;https://openalex.org/W2963428668;https://openalex.org/W2963823661;https://openalex.org/W2965520043;https://openalex.org/W2966312603;https://openalex.org/W2967681529;https://openalex.org/W2971458492;https://openalex.org/W2973091513;https://openalex.org/W2979640443;https://openalex.org/W2979691446;https://openalex.org/W2981624778;https://openalex.org/W2981963245;https://openalex.org/W2986869417;https://openalex.org/W2991287954;https://openalex.org/W2995412382;https://openalex.org/W2995441112;https://openalex.org/W2997769210;https://openalex.org/W2999575735;https://openalex.org/W2999600747;https://openalex.org/W3000122014;https://openalex.org/W3000524228;https://openalex.org/W3000630830;https://openalex.org/W3001639610;https://openalex.org/W3004327895;https://openalex.org/W3005210932;https://openalex.org/W3005949594;https://openalex.org/W3006475810;https://openalex.org/W3008000699;https://openalex.org/W3016237870;https://openalex.org/W3037340117;https://openalex.org/W3042530487;https://openalex.org/W3083342460;https://openalex.org/W3083804794;https://openalex.org/W3103215654;https://openalex.org/W3104734507;https://openalex.org/W3161999894;https://openalex.org/W6756016900;https://openalex.org/W6758691785;https://openalex.org/W6768954387;https://openalex.org/W6769117056;https://openalex.org/W6769264027;https://openalex.org/W6771213972;https://openalex.org/W6780197325,Medicine;Machine learning;Artificial intelligence;Field (mathematics);Key (lock);Clinical Practice;Data science;Computer science;Nursing,Artificial Intelligence in Healthcare and Education;Artificial Intelligence in Healthcare;Machine Learning in Healthcare
-OPENALEX,https://openalex.org/W4297574821,https://doi.org/10.53964/jmge.2022004,,Machine Learning Theory in Building Energy Modeling and Optimization: A Bibliometric Analysis,JOURNAL OF MODERN GREEN ENERGY,JOURNAL OF MODERN GREEN ENERGY,2022,article,en,Iran University of Science and Technology,"In recent decades, the machine learning theory has been developed in the field of artificial intelligence (AI), as it excludes all shortcomings of manpower, performs complex calculations without rest, and provides prediction benefits for projects. Machine learning models and algorithms extract natural models from the data set, which offers increased problem insight, better decisions, and more accurate predictions. Machine learning has a variety of methods, including supervised, unsupervised, and reinforcement learning, and has been used for building energy modeling in recent years. In this review paper, machine learning in building energy modeling was examined to demonstrate the publications in this area and the relationship between these topics. This paper investigated machine learning methods for building energy modeling using bibliometric analysis and data mining. Therefore, the objective of this research was to give insight into the status of machine learning uses for energy systems in the construction industry. Scientometric software was used for analysis. Deep learning is also a cutting-edge topic of machine learning in 2018 onwards, so a brief explanation in this research was provided to explore a proper connection between machine learning, deep learning, and construction energy modeling.",,,,,"Ghoshchi, 2022, JOURNAL OF MODERN GREEN ENERGY",30,"Ghoshchi, Amir;Zahedi, Rahim;Pour, Zahra Moradi;Ahmadi, Abolfazl;Yang, Z;Becerik-Gerber, B;Young, A;Majchrzak, A;Kane, G;Khazaee, M;Zahedi, R;Faryadras, R;Theissler, A;Prez-Velzquez, J;Kettelgerdes, M;Seligman, B;Tuljapurkar, S;Rehkopf, D;Deb, C;Dai, Z;Schlueter, A;Zou, S;Chen, X;Xu, D;Dhiman, P;Ma, J;Navarro, C;Geyer, P;Singaravel, S;Zahedi, R;Eskandarpanah, R;Akbari, M;Walker, S;Khan, W;Katic, K;Huang, Y;Yuan, Y;Chen, H;Field, M;Hardcastle, N;Jameson, M;Moosavian, S;Borzuei, D;Zahedi, R;Ikeda, S;Nagai, T;Zahedi, R;Seraji, Man;Borzuei, D;Shapi, Mkm;Ramli, N;Awalin, L;Orlov, A;Rovnyagin, M;Aminova, A;Hadri, S;Naitmalek, Y;Najib, M;Nutkiewicz, A;Yang, Z;Jain, R;Gao, T;Lu, W;Paudel, D;Boogaard, H;De, Wit;Zivkovic, M;Bacanin, N;Venkatachalam, K;Ghannam, R;Techtmann, S;Taneja, M;Byabazaire, J;Jalodia, N;Antonopoulos, I;Robu, V;Couraud, B;Naganathan, H;Chong, W;Chen, X;Seyedzadeh, S;Rahimian, F;Glesk, I;Deng, H;Fannon, D;Eckelman, M;Donthu, N;Kumar, S;Mukherjee, D;Daneshgar, S;Zahedi, R;Song, X;Liu, X;Liu, F;Mpanya, D;Celik, T;Klug, E;Walther, J;Spanier, D;Panten, N","Ghoshchi, Amir;Zahedi, Rahim;Pour, Zahra Moradi;Ahmadi, Abolfazl;Yang, Z;Becerik-Gerber, B;Young, A;Majchrzak, A;Kane, G;Khazaee, M;Zahedi, R;Faryadras, R;Theissler, A;Prez-Velzquez, J;Kettelgerdes, M;Seligman, B;Tuljapurkar, S;Rehkopf, D;Deb, C;Dai, Z;Schlueter, A;Zou, S;Chen, X;Xu, D;Dhiman, P;Ma, J;Navarro, C;Geyer, P;Singaravel, S;Zahedi, R;Eskandarpanah, R;Akbari, M;Walker, S;Khan, W;Katic, K;Huang, Y;Yuan, Y;Chen, H;Field, M;Hardcastle, N;Jameson, M;Moosavian, S;Borzuei, D;Zahedi, R;Ikeda, S;Nagai, T;Zahedi, R;Seraji, Man;Borzuei, D;Shapi, Mkm;Ramli, N;Awalin, L;Orlov, A;Rovnyagin, M;Aminova, A;Hadri, S;Naitmalek, Y;Najib, M;Nutkiewicz, A;Yang, Z;Jain, R;Gao, T;Lu, W;Paudel, D;Boogaard, H;De, Wit;Zivkovic, M;Bacanin, N;Venkatachalam, K;Ghannam, R;Techtmann, S;Taneja, M;Byabazaire, J;Jalodia, N;Antonopoulos, I;Robu, V;Couraud, B;Naganathan, H;Chong, W;Chen, X;Seyedzadeh, S;Rahimian, F;Glesk, I;Deng, H;Fannon, D;Eckelman, M;Donthu, N;Kumar, S;Mukherjee, D;Daneshgar, S;Zahedi, R;Song, X;Liu, X;Liu, F;Mpanya, D;Celik, T;Klug, E;Walther, J;Spanier, D;Panten, N","Iran University of Science and Technology;Islamic Azad University, Lahijan Branch",https://openalex.org/W2019497540;https://openalex.org/W2028263411;https://openalex.org/W2519520824;https://openalex.org/W2770256320;https://openalex.org/W2773309836;https://openalex.org/W2790197011;https://openalex.org/W2884490557;https://openalex.org/W2894665398;https://openalex.org/W2922060155;https://openalex.org/W2943926435;https://openalex.org/W2990246451;https://openalex.org/W2996674504;https://openalex.org/W3011254899;https://openalex.org/W3017089791;https://openalex.org/W3034272367;https://openalex.org/W3096533084;https://openalex.org/W3112881537;https://openalex.org/W3112965332;https://openalex.org/W3113216760;https://openalex.org/W3114266307;https://openalex.org/W3122216490;https://openalex.org/W3131673466;https://openalex.org/W3132263092;https://openalex.org/W3135241484;https://openalex.org/W3145877980;https://openalex.org/W3156185991;https://openalex.org/W3158869325;https://openalex.org/W3160856016;https://openalex.org/W3161176628;https://openalex.org/W3167963175;https://openalex.org/W3172336096;https://openalex.org/W3173574091;https://openalex.org/W3174451482;https://openalex.org/W3177598680;https://openalex.org/W3182416595;https://openalex.org/W3185104581;https://openalex.org/W3191084496;https://openalex.org/W3216584722;https://openalex.org/W4205517422;https://openalex.org/W4205696178;https://openalex.org/W4224280770;https://openalex.org/W4224282610;https://openalex.org/W4225371958;https://openalex.org/W4281556077;https://openalex.org/W4281635626;https://openalex.org/W4282593973;https://openalex.org/W4297574821,Computer science;Energy analysis;Energy (signal processing);Artificial intelligence;Mathematics;Statistics,Energy Load and Power Forecasting;BIM and Construction Integration;Building Energy and Comfort Optimization
-OPENALEX,https://openalex.org/W4221027537,https://doi.org/10.1016/j.avsg.2022.03.019,https://pubmed.ncbi.nlm.nih.gov/35339595,A Systematic Review and Bibliometric Analysis of Applications of Artificial Intelligence and Machine Learning in Vascular Surgery,ANNALS OF VASCULAR SURGERY,ANNALS OF VASCULAR SURGERY,2022,review,en,University of Toronto,,85,,395,405,"Javidan, 2022, ANNALS OF VASCULAR SURGERY",58,"Javidan, Arshia P.;Li, Allen;Lee, Michael H.;Forbes, Thomas L.;Naji, Faysal","Javidan, Arshia P.;Li, Allen;Lee, Michael H.;Forbes, Thomas L.;Naji, Faysal",University of Toronto;University of Ottawa;University Health Network;McMaster University,https://openalex.org/W1794427698;https://openalex.org/W2177870565;https://openalex.org/W2299177375;https://openalex.org/W2600022899;https://openalex.org/W2745975212;https://openalex.org/W2763556273;https://openalex.org/W2788948370;https://openalex.org/W2809487627;https://openalex.org/W2905434820;https://openalex.org/W2912581524;https://openalex.org/W2919749284;https://openalex.org/W2921613140;https://openalex.org/W2996280595;https://openalex.org/W2997139801;https://openalex.org/W3006863342;https://openalex.org/W3007824786;https://openalex.org/W3013286749;https://openalex.org/W3034855613;https://openalex.org/W3078591268;https://openalex.org/W3137507105;https://openalex.org/W3170220376;https://openalex.org/W4205142556;https://openalex.org/W4225927996;https://openalex.org/W4294214983;https://openalex.org/W6735060455,Medicine;MEDLINE;Systematic review;Data extraction;Artificial intelligence;Vascular surgery;Meta-analysis;Medical physics;Surgery;Machine learning;Internal medicine;Computer science;Cardiac surgery,Artificial Intelligence in Healthcare and Education;Aortic aneurysm repair treatments;Retinal Imaging and Analysis
-OPENALEX,https://openalex.org/W3127175100,https://doi.org/10.1016/j.nanoen.2021.105844,,Towards smart cities powered by nanogenerators: Bibliometric and machine learning–based analysis,NANO ENERGY,NANO ENERGY,2021,article,en,Ferdowsi University of Mashhad,,83,,105844,105844,"Alagumalai, 2021, NANO ENERGY",44,"Alagumalai, Avinash;Mahian, Omid;Aghbashlo, Mortaza;Tabatabaei, Meisam;Wongwises, Somchai;Wang, Zhong Lin","Alagumalai, Avinash;Mahian, Omid;Aghbashlo, Mortaza;Tabatabaei, Meisam;Wongwises, Somchai;Wang, Zhong Lin",Ferdowsi University of Mashhad;Xi'an Jiaotong University;University of Tehran;Universiti Malaysia Terengganu;Henan Agricultural University;Agricultural Research & Education Organization;Biofuel Research Team;Agricultural Biotechnology Research Institute of Iran;National Science and Technology Development Agency;King Mongkut's University of Technology Thonburi;Georgia Institute of Technology,https://openalex.org/W1000605845;https://openalex.org/W1847624877;https://openalex.org/W1907979007;https://openalex.org/W1993378267;https://openalex.org/W2030260688;https://openalex.org/W2033607182;https://openalex.org/W2034834428;https://openalex.org/W2035759727;https://openalex.org/W2057494508;https://openalex.org/W2058554868;https://openalex.org/W2071521202;https://openalex.org/W2089766509;https://openalex.org/W2119097715;https://openalex.org/W2128525789;https://openalex.org/W2137815847;https://openalex.org/W2138818244;https://openalex.org/W2139143604;https://openalex.org/W2148352577;https://openalex.org/W2154707832;https://openalex.org/W2168100296;https://openalex.org/W2169336864;https://openalex.org/W2173762328;https://openalex.org/W2187910500;https://openalex.org/W2208352419;https://openalex.org/W2256965551;https://openalex.org/W2260415155;https://openalex.org/W2324179347;https://openalex.org/W2330492786;https://openalex.org/W2374529644;https://openalex.org/W2460650176;https://openalex.org/W2511968349;https://openalex.org/W2545851061;https://openalex.org/W2567728595;https://openalex.org/W2587626792;https://openalex.org/W2608936365;https://openalex.org/W2609860550;https://openalex.org/W2619763057;https://openalex.org/W2747997171;https://openalex.org/W2765862589;https://openalex.org/W2774777389;https://openalex.org/W2787690100;https://openalex.org/W2805868613;https://openalex.org/W2806443020;https://openalex.org/W2809436897;https://openalex.org/W2886833982;https://openalex.org/W2888447045;https://openalex.org/W2889412116;https://openalex.org/W2891606852;https://openalex.org/W2899809307;https://openalex.org/W2902768082;https://openalex.org/W2903206839;https://openalex.org/W2904186519;https://openalex.org/W2905714363;https://openalex.org/W2911976862;https://openalex.org/W2913629519;https://openalex.org/W2916871013;https://openalex.org/W2925162090;https://openalex.org/W2942620360;https://openalex.org/W2950487704;https://openalex.org/W2954696814;https://openalex.org/W2966483170;https://openalex.org/W2971176495;https://openalex.org/W2971490091;https://openalex.org/W2973529224;https://openalex.org/W2980812360;https://openalex.org/W2982154542;https://openalex.org/W2982928344;https://openalex.org/W2983288256;https://openalex.org/W2987660520;https://openalex.org/W2993029694;https://openalex.org/W2996080512;https://openalex.org/W2996606005;https://openalex.org/W3000781336;https://openalex.org/W3014311671;https://openalex.org/W3014518310;https://openalex.org/W3016910571;https://openalex.org/W3021341867;https://openalex.org/W3025199540;https://openalex.org/W3025880578;https://openalex.org/W3028262887;https://openalex.org/W3029513340;https://openalex.org/W3033153603;https://openalex.org/W3036291007;https://openalex.org/W3038627939;https://openalex.org/W3047027222;https://openalex.org/W3064821821;https://openalex.org/W3073035795;https://openalex.org/W3080108887;https://openalex.org/W3086246078;https://openalex.org/W3087670689;https://openalex.org/W3091654459;https://openalex.org/W3095254819;https://openalex.org/W4206890174;https://openalex.org/W6648463437;https://openalex.org/W6679365433;https://openalex.org/W6688598988;https://openalex.org/W6701126092;https://openalex.org/W6708692093;https://openalex.org/W6731685877;https://openalex.org/W6737112589;https://openalex.org/W6751810562;https://openalex.org/W6755682719;https://openalex.org/W6757290667;https://openalex.org/W6757687850;https://openalex.org/W6759296805;https://openalex.org/W6761140886;https://openalex.org/W6762102947;https://openalex.org/W6769580648;https://openalex.org/W6771952073;https://openalex.org/W6776146294;https://openalex.org/W6776779739;https://openalex.org/W6778506474;https://openalex.org/W6779045260,Nanogenerator;Triboelectric effect;Commercialization;Electricity;Field (mathematics);Energy harvesting;Engineering physics;Nanotechnology;Mechanical engineering;Materials science;Manufacturing engineering;Electrical engineering;Energy (signal processing);Engineering;Piezoelectricity;Business;Marketing,Advanced Sensor and Energy Harvesting Materials;Innovative Energy Harvesting Technologies;Tactile and Sensory Interactions
-OPENALEX,https://openalex.org/W3085940513,https://doi.org/10.1016/j.jbusres.2020.08.019,,Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review,JOURNAL OF BUSINESS RESEARCH,JOURNAL OF BUSINESS RESEARCH,2020,article,en,Parthenope University of Naples,,121,,283,314,"Vaio, 2020, JOURNAL OF BUSINESS RESEARCH",1019,"Vaio, Assunta Di;Palladino, Rosa;Hassan, Rohail;Escobar, Octavio","Vaio, Assunta Di;Palladino, Rosa;Hassan, Rohail;Escobar, Octavio",Parthenope University of Naples;Northern University of Malaysia;EM Normandie Business School;Métis-Lab,https://openalex.org/W81110436;https://openalex.org/W581665006;https://openalex.org/W972842902;https://openalex.org/W974480840;https://openalex.org/W1539987097;https://openalex.org/W1588162766;https://openalex.org/W1588485088;https://openalex.org/W1751943488;https://openalex.org/W1900880735;https://openalex.org/W1904625514;https://openalex.org/W1965987765;https://openalex.org/W1966481002;https://openalex.org/W1982795239;https://openalex.org/W1986298738;https://openalex.org/W1987539444;https://openalex.org/W1988221687;https://openalex.org/W1988415499;https://openalex.org/W1995861785;https://openalex.org/W2000319709;https://openalex.org/W2001771035;https://openalex.org/W2006282771;https://openalex.org/W2014647052;https://openalex.org/W2021655427;https://openalex.org/W2051768456;https://openalex.org/W2061977616;https://openalex.org/W2062827704;https://openalex.org/W2066575459;https://openalex.org/W2078008877;https://openalex.org/W2080013315;https://openalex.org/W2081797436;https://openalex.org/W2091860924;https://openalex.org/W2094532658;https://openalex.org/W2099913992;https://openalex.org/W2113847349;https://openalex.org/W2117871237;https://openalex.org/W2120931247;https://openalex.org/W2122266543;https://openalex.org/W2126549064;https://openalex.org/W2128011508;https://openalex.org/W2130716482;https://openalex.org/W2136071264;https://openalex.org/W2140699752;https://openalex.org/W2145526289;https://openalex.org/W2149631558;https://openalex.org/W2154482656;https://openalex.org/W2154944074;https://openalex.org/W2164314939;https://openalex.org/W2231928120;https://openalex.org/W2279438569;https://openalex.org/W2285118877;https://openalex.org/W2346237356;https://openalex.org/W2398631725;https://openalex.org/W2432612777;https://openalex.org/W2437319536;https://openalex.org/W2474335672;https://openalex.org/W2487200295;https://openalex.org/W2530278673;https://openalex.org/W2553227351;https://openalex.org/W2566818341;https://openalex.org/W2576200532;https://openalex.org/W2602096487;https://openalex.org/W2612430550;https://openalex.org/W2625223610;https://openalex.org/W2724896332;https://openalex.org/W2735575534;https://openalex.org/W2735789100;https://openalex.org/W2747760276;https://openalex.org/W2755950973;https://openalex.org/W2757967610;https://openalex.org/W2760327112;https://openalex.org/W2761948312;https://openalex.org/W2770256834;https://openalex.org/W2770567712;https://openalex.org/W2783182376;https://openalex.org/W2799281036;https://openalex.org/W2800705907;https://openalex.org/W2887423052;https://openalex.org/W2888604880;https://openalex.org/W2891212971;https://openalex.org/W2891325873;https://openalex.org/W2897312458;https://openalex.org/W2899856450;https://openalex.org/W2901497880;https://openalex.org/W2902851216;https://openalex.org/W2902873908;https://openalex.org/W2903202722;https://openalex.org/W2903682365;https://openalex.org/W2905604475;https://openalex.org/W2905862165;https://openalex.org/W2908475580;https://openalex.org/W2914612929;https://openalex.org/W2915530405;https://openalex.org/W2921510663;https://openalex.org/W2924621562;https://openalex.org/W2934302500;https://openalex.org/W2941705808;https://openalex.org/W2944031241;https://openalex.org/W2945920492;https://openalex.org/W2953974881;https://openalex.org/W2956448821;https://openalex.org/W2963849010;https://openalex.org/W2964167195;https://openalex.org/W2965744628;https://openalex.org/W2969752936;https://openalex.org/W2973318118;https://openalex.org/W2980616108;https://openalex.org/W2992586577;https://openalex.org/W3000603264;https://openalex.org/W3013019408;https://openalex.org/W3013311152;https://openalex.org/W3047665533;https://openalex.org/W3121463823;https://openalex.org/W3122250801;https://openalex.org/W3124536214;https://openalex.org/W3124627828;https://openalex.org/W3125505924;https://openalex.org/W3125707221;https://openalex.org/W3153432393;https://openalex.org/W3198048885;https://openalex.org/W4210824092;https://openalex.org/W4213277800;https://openalex.org/W4380354141;https://openalex.org/W6679299497;https://openalex.org/W6684180213;https://openalex.org/W6731341516;https://openalex.org/W6771196742;https://openalex.org/W6793892045,Perspective (graphical);Sustainable development;Management science;Business intelligence;Process management;Knowledge management;Business;Computer science;Economics;Artificial intelligence;Political science,Business and Economic Development;Economic and Technological Innovation;Big Data and Business Intelligence
-OPENALEX,https://openalex.org/W4396733572,https://doi.org/10.1016/j.jlp.2024.105343,,"Artificial Intelligence for safety and reliability: A descriptive, bibliometric and interpretative review on machine learning",JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES,JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES,2024,article,en,Norwegian University of Science and Technology,"This research provides a structured review of studies that utilize Artificial Intelligence for safety and reliability. In particular, it focuses on Machine Learning techniques to perform fault detection and diagnosis, anomaly detection, system prognosis, reliability analysis, and risk assessment of engineering-related systems across the industry. Relevant studies were identified through clear research questions, screened, and assessed for eligibility. Explicit inclusion and exclusion criteria were defined to verify the suitability of the records. The analysis encompasses a descriptive, bibliometric, and interpretative review. The descriptive analysis details how different ML approaches are adapted and implemented across various domains of safety and reliability. The bibliometric analysis provides in-depth and comprehensive statistics, covering aspects such as data types used, preprocessing steps undertaken, and categories of ML algorithms employed. The interpretative analysis offers a critical and forward-looking perspective on the current state of the field. A total of 308 papers were analyzed, mostly adopting supervised learning frameworks (81%) and addressing fault detection and diagnosis (57%) in more than 16 different industrial fields. The outcome of this study reflects the rapid development of this cross-cutting and interdisciplinary research field and highlights the potential for future improvement in the data-driven operational safety of industrial plants. Challenges and limitations have been highlighted and discussed, including data availability and label scarcity, data quality, trust and explainability, and interdisciplinary collaboration. Additionally, suggestions on potential solutions to overcome existing limitations and outline future directions are provided.",90,,105343,105343,"Tamascelli, 2024, JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES",37,"Tamascelli, Nicola;Campari, Alessandro;Parhizkar, Tarannom;Paltrinieri, Nicola","Tamascelli, Nicola;Campari, Alessandro;Parhizkar, Tarannom;Paltrinieri, Nicola","Norwegian University of Science and Technology;University of Bologna;University of California, Los Angeles",https://openalex.org/W1592847587;https://openalex.org/W1978677160;https://openalex.org/W1992129230;https://openalex.org/W1995386176;https://openalex.org/W1997304546;https://openalex.org/W2009637664;https://openalex.org/W2014220254;https://openalex.org/W2016864600;https://openalex.org/W2018664114;https://openalex.org/W2031757691;https://openalex.org/W2039125545;https://openalex.org/W2051196068;https://openalex.org/W2063867591;https://openalex.org/W2066363274;https://openalex.org/W2085019962;https://openalex.org/W2089903857;https://openalex.org/W2122646361;https://openalex.org/W2124000105;https://openalex.org/W2137570937;https://openalex.org/W2146194630;https://openalex.org/W2148143831;https://openalex.org/W2150220236;https://openalex.org/W2169347809;https://openalex.org/W2205836349;https://openalex.org/W2239104709;https://openalex.org/W2564037921;https://openalex.org/W2594352094;https://openalex.org/W2606959532;https://openalex.org/W2738091946;https://openalex.org/W2772084711;https://openalex.org/W2773549135;https://openalex.org/W2792098970;https://openalex.org/W2797844224;https://openalex.org/W2799712295;https://openalex.org/W2809047955;https://openalex.org/W2897805291;https://openalex.org/W2897966647;https://openalex.org/W2901772421;https://openalex.org/W2910142614;https://openalex.org/W2912629470;https://openalex.org/W2913289332;https://openalex.org/W2920083100;https://openalex.org/W2921789962;https://openalex.org/W2922577842;https://openalex.org/W2922856096;https://openalex.org/W2948678706;https://openalex.org/W2949341078;https://openalex.org/W2960995627;https://openalex.org/W2981915020;https://openalex.org/W2984353870;https://openalex.org/W2990555792;https://openalex.org/W2991860176;https://openalex.org/W2994863453;https://openalex.org/W2998389585;https://openalex.org/W3014057330;https://openalex.org/W3023741150;https://openalex.org/W3035318737;https://openalex.org/W3035330751;https://openalex.org/W3038822267;https://openalex.org/W3094253667;https://openalex.org/W3095487005;https://openalex.org/W3098015864;https://openalex.org/W3103035501;https://openalex.org/W3105804795;https://openalex.org/W3117629641;https://openalex.org/W3126272279;https://openalex.org/W3138953622;https://openalex.org/W3162098247;https://openalex.org/W3162930711;https://openalex.org/W3164952570;https://openalex.org/W3171724330;https://openalex.org/W3185756013;https://openalex.org/W3198406420;https://openalex.org/W3200985993;https://openalex.org/W3207396718;https://openalex.org/W3207646706;https://openalex.org/W3210180182;https://openalex.org/W3213560988;https://openalex.org/W3215414868;https://openalex.org/W4205666304;https://openalex.org/W4206544469;https://openalex.org/W4220957411;https://openalex.org/W4223475678;https://openalex.org/W4224055057;https://openalex.org/W4226404024;https://openalex.org/W4231400111;https://openalex.org/W4231649899;https://openalex.org/W4236777627;https://openalex.org/W4250716257;https://openalex.org/W4251691207;https://openalex.org/W4256669726;https://openalex.org/W4285159403;https://openalex.org/W4285238901;https://openalex.org/W4285256586;https://openalex.org/W4289516096;https://openalex.org/W4292313839;https://openalex.org/W4298558181;https://openalex.org/W4300990358;https://openalex.org/W4304761972;https://openalex.org/W4306920664;https://openalex.org/W4308033457;https://openalex.org/W4308307412;https://openalex.org/W4309337291;https://openalex.org/W4310029360;https://openalex.org/W4318586239;https://openalex.org/W4361012985;https://openalex.org/W4362669277;https://openalex.org/W4386986818;https://openalex.org/W4387806421;https://openalex.org/W4390005078;https://openalex.org/W6634113578;https://openalex.org/W6672986919;https://openalex.org/W6679002737;https://openalex.org/W6690156651;https://openalex.org/W6708991347;https://openalex.org/W6728146022;https://openalex.org/W6744322657;https://openalex.org/W6752891516;https://openalex.org/W6755603292;https://openalex.org/W6756708519;https://openalex.org/W6759355091;https://openalex.org/W6760255084;https://openalex.org/W6760570153;https://openalex.org/W6787731716;https://openalex.org/W6792572463;https://openalex.org/W6802173281;https://openalex.org/W6803381961;https://openalex.org/W6804747582;https://openalex.org/W6813297198;https://openalex.org/W6822589861;https://openalex.org/W6825983812;https://openalex.org/W6838655090;https://openalex.org/W6844316357;https://openalex.org/W6856464586;https://openalex.org/W6860180176,Reliability (semiconductor);Field (mathematics);Computer science;Descriptive statistics;Data science;Quality (philosophy);Scarcity;Fault tree analysis;Management science;Artificial intelligence;Risk analysis (engineering);Engineering;Reliability engineering;Mathematics,Occupational Health and Safety Research;Quality and Safety in Healthcare;Risk and Safety Analysis
-OPENALEX,https://openalex.org/W3115894432,https://doi.org/10.1007/s00521-020-05626-8,https://pubmed.ncbi.nlm.nih.gov/33564213,A review on COVID-19 forecasting models,NEURAL COMPUTING AND APPLICATIONS,NEURAL COMPUTING AND APPLICATIONS,2021,review,en,Universiti Putra Malaysia,,35,33,23671,23681,"Rahimi, 2021, NEURAL COMPUTING AND APPLICATIONS",252,"Rahimi, Iman;Fang, Chen;Gandomi, Amir H.","Rahimi, Iman;Fang, Chen;Gandomi, Amir H.",Universiti Putra Malaysia;University of Technology Sydney,https://openalex.org/W42525004;https://openalex.org/W150292108;https://openalex.org/W187904391;https://openalex.org/W888141997;https://openalex.org/W1488166531;https://openalex.org/W1489537150;https://openalex.org/W1570814949;https://openalex.org/W1985028263;https://openalex.org/W1991038590;https://openalex.org/W2002944058;https://openalex.org/W2004617458;https://openalex.org/W2019282798;https://openalex.org/W2032841931;https://openalex.org/W2039568841;https://openalex.org/W2041490648;https://openalex.org/W2076063813;https://openalex.org/W2094534930;https://openalex.org/W2141125852;https://openalex.org/W2144301074;https://openalex.org/W2163922914;https://openalex.org/W2166901389;https://openalex.org/W2171103562;https://openalex.org/W2316616412;https://openalex.org/W2339500526;https://openalex.org/W2402695066;https://openalex.org/W2483251205;https://openalex.org/W2541920591;https://openalex.org/W2596233835;https://openalex.org/W2605411705;https://openalex.org/W2618530766;https://openalex.org/W2747599906;https://openalex.org/W2783781251;https://openalex.org/W2797841405;https://openalex.org/W2896745687;https://openalex.org/W2914422414;https://openalex.org/W2919115771;https://openalex.org/W2982545415;https://openalex.org/W2995301563;https://openalex.org/W2995566743;https://openalex.org/W2996056133;https://openalex.org/W3006028741;https://openalex.org/W3007602081;https://openalex.org/W3008573283;https://openalex.org/W3009333463;https://openalex.org/W3009916383;https://openalex.org/W3011771926;https://openalex.org/W3013056994;https://openalex.org/W3013649595;https://openalex.org/W3014804276;https://openalex.org/W3015305373;https://openalex.org/W3015380512;https://openalex.org/W3016038616;https://openalex.org/W3016049706;https://openalex.org/W3016236951;https://openalex.org/W3016393085;https://openalex.org/W3017051018;https://openalex.org/W3018069106;https://openalex.org/W3018219276;https://openalex.org/W3019529372;https://openalex.org/W3022122691;https://openalex.org/W3022714712;https://openalex.org/W3022787740;https://openalex.org/W3023006744;https://openalex.org/W3023277104;https://openalex.org/W3024647574;https://openalex.org/W3024773523;https://openalex.org/W3024906488;https://openalex.org/W3024990888;https://openalex.org/W3026147544;https://openalex.org/W3026389350;https://openalex.org/W3026481086;https://openalex.org/W3029558740;https://openalex.org/W3030109869;https://openalex.org/W3031229471;https://openalex.org/W3031465195;https://openalex.org/W3032502637;https://openalex.org/W3034944671;https://openalex.org/W3035619533;https://openalex.org/W3038060658;https://openalex.org/W3038075184;https://openalex.org/W3043265968;https://openalex.org/W3043618167;https://openalex.org/W3093554370;https://openalex.org/W3106321705;https://openalex.org/W3112102355;https://openalex.org/W3114631459;https://openalex.org/W3125492565;https://openalex.org/W4205230814;https://openalex.org/W4205406083;https://openalex.org/W4206700125;https://openalex.org/W4236605548;https://openalex.org/W4238753141;https://openalex.org/W4242841269;https://openalex.org/W4292156591;https://openalex.org/W4399522163,Coronavirus disease 2019 (COVID-19);Scopus;Computer science;Web of science;Computational Science and Engineering;Section (typography);Work (physics);Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2);Machine learning;2019-20 coronavirus outbreak;Artificial intelligence;Subject (documents);Data science;Outbreak;Library science;MEDLINE;Engineering,COVID-19 diagnosis using AI;COVID-19 epidemiological studies;Anomaly Detection Techniques and Applications
-OPENALEX,https://openalex.org/W4398150100,https://doi.org/10.18178/ijiet.2024.14.5.2095,,The Information Age for Education via Artificial Intelligence and Machine Learning: A Bibliometric and Systematic Literature Analysis,INTERNATIONAL JOURNAL OF INFORMATION AND EDUCATION TECHNOLOGY,INTERNATIONAL JOURNAL OF INFORMATION AND EDUCATION TECHNOLOGY,2024,article,en,Newcastle University Medicine Malaysia,"The integration of Artificial Intelligence (AI) and Machine Learning (ML) in education is a rapidly evolving field, yet the long-term implications and actual impacts on student learning outcomes require more in-depth study. Address this gap, our study offers a novel approach combining bibliometric analysis and a Systematic Literature Review (SLR), guided by the PRISMA methodology. The first phase, a comprehensive bibliometric analysis, identified key nations, educational institutions, journals, keywords, and influential authors in the realm of AI/ML in educational settings. This phase provided a macro-level understanding of the field’s landscape, showcasing the global and interdisciplinary nature of AI/ML research in education. The subsequent phase involved a meticulous SLR of 22 select scholarly articles. This in-depth review sheds light on the current applications, emerging trends, challenges, and future directions of AI and ML in education. The findings from this dual-method approach offer a comprehensive roadmap for educators, researchers, and policymakers, underscoring the transformative potential of AI and ML in the educational sector. The review’s extensive article collection provides a deep dive into the diverse and significant impact of AI in education, highlighting its role in areas such as predicting academic success, enhancing e-learning experiences, and preparing future generations for AI’s integration in various fields like healthcare. This study not only underscores the revolutionary potential of AI in reshaping educational landscapes but also serves as a guiding framework for effectively deploying AI and ML technologies in education.",14,5,700,711,"Abuhassna, 2024, INTERNATIONAL JOURNAL OF INFORMATION AND EDUCATION TECHNOLOGY",39,"Abuhassna, Hassan","Abuhassna, Hassan",Newcastle University Medicine Malaysia,https://openalex.org/W182272962;https://openalex.org/W2117655204;https://openalex.org/W2133586213;https://openalex.org/W2770717476;https://openalex.org/W2892786865;https://openalex.org/W2959063074;https://openalex.org/W2997565628;https://openalex.org/W3000599514;https://openalex.org/W3047161823;https://openalex.org/W3087987726;https://openalex.org/W3090783394;https://openalex.org/W3094595104;https://openalex.org/W3197804775;https://openalex.org/W3199263016;https://openalex.org/W3217334161;https://openalex.org/W4212852473;https://openalex.org/W4212853713;https://openalex.org/W4223926312;https://openalex.org/W4225377410;https://openalex.org/W4229067029;https://openalex.org/W4229442974;https://openalex.org/W4236476849;https://openalex.org/W4280607716;https://openalex.org/W4280651999;https://openalex.org/W4283077296;https://openalex.org/W4304589352;https://openalex.org/W4306741917;https://openalex.org/W4307871426;https://openalex.org/W4310153968;https://openalex.org/W4312090823,Computer science;Artificial intelligence;Data science;Psychology;Machine learning;Mathematics education,Online Learning and Analytics;Artificial Intelligence in Healthcare and Education;COVID-19 diagnosis using AI
-OPENALEX,https://openalex.org/W4402323428,https://doi.org/10.14254/1795-6889.2024.20-2.5,,Artificial intelligence and machine learning in combating illegal financial operations: Bibliometric analysis,HUMAN TECHNOLOGY,HUMAN TECHNOLOGY,2024,article,en,Silesian University of Technology,"Money launderers and corrupt entities refine methods to evade detection, making artificial intelligence (AI) and machine learning (ML) essential for countering these threats. AI automates identity verification using diverse data sources, including government databases and social media, analysing client data more effectively than traditional methods. This study uses bibliometric analysis to examine AI and ML in anti-money laundering and anti-corruption efforts. A sample of 746 documents from 477 sources from Scopus shows a 14.33% annual growth rate and an average document age of 3.51 years, highlighting the field's actuality and rapid development. The research indicates significant international collaboration in documents. The main clusters of keywords relate to the implementation of AI and ML in (1) avoiding fraud and cybersecurity, (2) AML compliance, (3) promotion of transparency in combating corruption, etc. Addressing ethical concerns, privacy, and bias is crucial for the fair and effective use of AI and ML in this area.",20,2,325,360,"Lyeonov, 2024, HUMAN TECHNOLOGY",36,"Lyeonov, Serhiy;Drašković, Veselin;Kubaščíková, Zuzana;Fenyves, Veronaika","Lyeonov, Serhiy;Drašković, Veselin;Kubaščíková, Zuzana;Fenyves, Veronaika",Silesian University of Technology;Społeczna Akademia Nauk;Bratislava University of Economics and Business;University of Debrecen,https://openalex.org/W1992953801;https://openalex.org/W2031111227;https://openalex.org/W2040255780;https://openalex.org/W2045049630;https://openalex.org/W2085573882;https://openalex.org/W2096870307;https://openalex.org/W2132651096;https://openalex.org/W2135455887;https://openalex.org/W2610250061;https://openalex.org/W2755950973;https://openalex.org/W2772947247;https://openalex.org/W2785637175;https://openalex.org/W2788185337;https://openalex.org/W2898514850;https://openalex.org/W2962831337;https://openalex.org/W3001272657;https://openalex.org/W3006240935;https://openalex.org/W3137875885;https://openalex.org/W3169553587;https://openalex.org/W4205650440;https://openalex.org/W4205663358;https://openalex.org/W4211068006;https://openalex.org/W4307765021;https://openalex.org/W4313489356;https://openalex.org/W4362465742;https://openalex.org/W4378473099;https://openalex.org/W4381547385;https://openalex.org/W4381571476;https://openalex.org/W4381678433;https://openalex.org/W4383562916;https://openalex.org/W4383889888;https://openalex.org/W4384567368;https://openalex.org/W4384567389;https://openalex.org/W4387149956;https://openalex.org/W4387521525;https://openalex.org/W4387521707;https://openalex.org/W4387812465;https://openalex.org/W4389918938;https://openalex.org/W4390108766;https://openalex.org/W4390166642;https://openalex.org/W4390672665;https://openalex.org/W4390844260;https://openalex.org/W4390921997;https://openalex.org/W4390937518;https://openalex.org/W4390939370;https://openalex.org/W4390939386;https://openalex.org/W4390939405;https://openalex.org/W4391261872;https://openalex.org/W4391736322;https://openalex.org/W4394857854;https://openalex.org/W4394860286;https://openalex.org/W4394860292;https://openalex.org/W4394864040;https://openalex.org/W4399264176;https://openalex.org/W4400134253;https://openalex.org/W4400310660;https://openalex.org/W4400313119;https://openalex.org/W4400673915;https://openalex.org/W4400674293;https://openalex.org/W4400674450;https://openalex.org/W4400674643;https://openalex.org/W4400675440;https://openalex.org/W4401027169;https://openalex.org/W4401916452;https://openalex.org/W4401918272;https://openalex.org/W4402959877,Computer science;Artificial intelligence;Data science;Business;Knowledge management,"Banking, Crisis Management, COVID-19 Impact;Business and Economic Development;Economic, Social, and Public Health Issues in Russia and Globally"
-OPENALEX,https://openalex.org/W4392031682,https://doi.org/10.1016/j.egyr.2024.02.036,,A bibliometric analysis of machine learning techniques in photovoltaic cells and solar energy (2014–2022),ENERGY REPORTS,ENERGY REPORTS,2024,article,en,Qassim University,"Solar energy presents a promising solution to replace fossil-based energy sources, mitigating global warming and climate change. However, solar energy faces socio-economic, environmental, and technical challenges. Computational tools like machine learning offer solutions to these technical challenges. Despite numerous studies, there's a lack of comprehensive research on ML applications in Photovoltaics and Solar Energy. This study conducts a critical analysis of ML applications in Photovoltaics and Solar Energy research using publication trends and bibliometric analysis, employing the PRISMA approach on Scopus database. Results reveal a high publication output, citations, and international collaboration. Notable researchers include G. E. Georghiou and Haibo Ma, with the Ministry of Education (China) being a prolific affiliation. China emerges as the most active nation due to funding programs like the National Natural Science Foundation and the National Key Research and Development Program. This research contributes in terms of providing an analysis of publication patterns from 2014 to 2022, including topic categories and important metrics, at the levels of country, institution, and funding organisation. Analysing author-keyword data to aggregate publishing themes and identify the most influential journals. Enhancing comprehension of hotspots and focal points in machine learning applications in Photovoltaics and Solar Energy research. This research also aims to discuss the role of Cognitive Computing in cancer/tumor and oncological research, emphasising the potential for significant advancements and the obstacles that need to be overcome in order to fully utilise its advantages. Future studies on the topic could include extensive research into the cybersecurity of Photovoltaics and solar energy systems particularly in the wake of numerous malware, phishing, and other intrusion attacks on the energy and grid infrastructure worldwide.",11,,2768,2779,"Zaïdi, 2024, ENERGY REPORTS",42,"Zaïdi, Abdelhamid","Zaïdi, Abdelhamid",Qassim University,https://openalex.org/W1743187317;https://openalex.org/W1983797158;https://openalex.org/W2026804118;https://openalex.org/W2029297700;https://openalex.org/W2127451718;https://openalex.org/W2132618171;https://openalex.org/W2135455887;https://openalex.org/W2160808585;https://openalex.org/W2171702311;https://openalex.org/W2259944928;https://openalex.org/W2286152107;https://openalex.org/W2287933588;https://openalex.org/W2297092368;https://openalex.org/W2474191477;https://openalex.org/W2576683119;https://openalex.org/W2587299461;https://openalex.org/W2752052391;https://openalex.org/W2757642744;https://openalex.org/W2768163011;https://openalex.org/W2780722608;https://openalex.org/W2787944342;https://openalex.org/W2790021805;https://openalex.org/W2794614147;https://openalex.org/W2799753020;https://openalex.org/W2810220676;https://openalex.org/W2884258597;https://openalex.org/W2898907833;https://openalex.org/W2912623183;https://openalex.org/W2924357249;https://openalex.org/W2925145055;https://openalex.org/W2946494228;https://openalex.org/W2955588401;https://openalex.org/W2961960358;https://openalex.org/W2969489994;https://openalex.org/W2978713836;https://openalex.org/W2983566047;https://openalex.org/W2988073060;https://openalex.org/W2988203096;https://openalex.org/W2989592648;https://openalex.org/W2990450011;https://openalex.org/W3000632091;https://openalex.org/W3005415948;https://openalex.org/W3006448130;https://openalex.org/W3010274200;https://openalex.org/W3016260214;https://openalex.org/W3022321166;https://openalex.org/W3026907991;https://openalex.org/W3048884198;https://openalex.org/W3080199112;https://openalex.org/W3097763105;https://openalex.org/W3110377484;https://openalex.org/W3111879052;https://openalex.org/W3124856069;https://openalex.org/W3125019846;https://openalex.org/W3132544139;https://openalex.org/W3133181227;https://openalex.org/W3138852234;https://openalex.org/W3140968591;https://openalex.org/W3150904570;https://openalex.org/W3160856016;https://openalex.org/W3191690765;https://openalex.org/W3214240043;https://openalex.org/W3214910795;https://openalex.org/W4200277584;https://openalex.org/W4200434445;https://openalex.org/W4205605524;https://openalex.org/W4206935801;https://openalex.org/W4213455927;https://openalex.org/W4220726206;https://openalex.org/W4220972044;https://openalex.org/W4224052816;https://openalex.org/W4228996833;https://openalex.org/W4231515310;https://openalex.org/W4237152566;https://openalex.org/W4240818896;https://openalex.org/W4244082399;https://openalex.org/W4245805152;https://openalex.org/W4281917964;https://openalex.org/W4283588236;https://openalex.org/W4285122660;https://openalex.org/W4297478379;https://openalex.org/W4299421480;https://openalex.org/W4308200999;https://openalex.org/W4311098650;https://openalex.org/W4311273571;https://openalex.org/W4321120950;https://openalex.org/W4321164476;https://openalex.org/W4360604152;https://openalex.org/W4362666995;https://openalex.org/W4366588036;https://openalex.org/W4379057423;https://openalex.org/W4382542164;https://openalex.org/W4383682762;https://openalex.org/W4384523309;https://openalex.org/W4384944264;https://openalex.org/W4385413292;https://openalex.org/W4386803046;https://openalex.org/W4386859003;https://openalex.org/W4387259010;https://openalex.org/W4387401057;https://openalex.org/W4388665898;https://openalex.org/W6753371401;https://openalex.org/W6762625936;https://openalex.org/W6768602574;https://openalex.org/W6768905283;https://openalex.org/W6769300415;https://openalex.org/W6783001559;https://openalex.org/W6786726123;https://openalex.org/W6790681345;https://openalex.org/W6793970244;https://openalex.org/W6800363861;https://openalex.org/W6810864097;https://openalex.org/W6811357256;https://openalex.org/W6842497516;https://openalex.org/W6843664783;https://openalex.org/W6847109510;https://openalex.org/W6849879430;https://openalex.org/W6851579319;https://openalex.org/W6855493788;https://openalex.org/W6861427902;https://openalex.org/W6910792018,Photovoltaics;Computer science;Data science;Scopus;Photovoltaic system;Solar energy;Political science;Engineering;Electrical engineering,Solar Radiation and Photovoltaics;Photovoltaic System Optimization Techniques;Energy Load and Power Forecasting
-OPENALEX,https://openalex.org/W4362576983,https://doi.org/10.1016/j.wneu.2023.03.115,https://pubmed.ncbi.nlm.nih.gov/37019303,Automated Brain Tumor Detection Using Machine Learning: A Bibliometric Review,WORLD NEUROSURGERY,WORLD NEUROSURGERY,2023,review,en,Malaysia University of Science and Technology,,175,,57,68,"Hossain, 2023, WORLD NEUROSURGERY",28,"Hossain, Rajan;Ibrahim, Roliana;Hashim, Haslina","Hossain, Rajan;Ibrahim, Roliana;Hashim, Haslina",Malaysia University of Science and Technology,https://openalex.org/W1970398577;https://openalex.org/W1985820976;https://openalex.org/W2344469150;https://openalex.org/W2905017682;https://openalex.org/W2917364154;https://openalex.org/W2955805844;https://openalex.org/W3036656090;https://openalex.org/W3037825799;https://openalex.org/W3043717094;https://openalex.org/W3101028869;https://openalex.org/W3111465317;https://openalex.org/W3160856016;https://openalex.org/W3165128717;https://openalex.org/W3207478520;https://openalex.org/W4210792946;https://openalex.org/W4226371181;https://openalex.org/W4238243588;https://openalex.org/W4286209802;https://openalex.org/W4291017261;https://openalex.org/W4292560495;https://openalex.org/W4293545361;https://openalex.org/W4293547155;https://openalex.org/W4295753330;https://openalex.org/W4301600675;https://openalex.org/W4307951239;https://openalex.org/W4310059423;https://openalex.org/W4311186062;https://openalex.org/W4312129879;https://openalex.org/W6719649792;https://openalex.org/W6780213641;https://openalex.org/W6781605770;https://openalex.org/W6795024988;https://openalex.org/W6811353288;https://openalex.org/W6841167871;https://openalex.org/W6842620793;https://openalex.org/W6847334649,Scopus;Medicine;Artificial intelligence;Bibliometrics;Machine learning;Glioma;Citation;Citation analysis;Brain tumor;Convolutional neural network;Web of science;MEDLINE;Library science;Medical physics;Meta-analysis;Computer science;Pathology;Political science,Brain Tumor Detection and Classification;COVID-19 diagnosis using AI;Radiomics and Machine Learning in Medical Imaging
-OPENALEX,https://openalex.org/W2889666927,https://doi.org/10.1007/s13042-018-0875-9,,A bibliometric overview of International Journal of Machine Learning and Cybernetics between 2010 and 2017,INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,2018,article,en,Sichuan University,,10,9,2375,2387,"Xu, 2018, INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS",34,"Xu, Zeshui;Yu, Dejian;Wang, Xizhao","Xu, Zeshui;Yu, Dejian;Wang, Xizhao",Sichuan University;Nanjing Audit University;Shenzhen University,https://openalex.org/W1559665635;https://openalex.org/W1675042025;https://openalex.org/W1888811121;https://openalex.org/W1966546225;https://openalex.org/W1969392438;https://openalex.org/W1971386719;https://openalex.org/W1980867644;https://openalex.org/W1984149720;https://openalex.org/W1984558542;https://openalex.org/W1990014583;https://openalex.org/W1993717606;https://openalex.org/W1994425726;https://openalex.org/W2009550727;https://openalex.org/W2010684265;https://openalex.org/W2014677380;https://openalex.org/W2016419002;https://openalex.org/W2017314269;https://openalex.org/W2027090091;https://openalex.org/W2043976122;https://openalex.org/W2046904226;https://openalex.org/W2046958243;https://openalex.org/W2047237187;https://openalex.org/W2069315453;https://openalex.org/W2069613886;https://openalex.org/W2071978572;https://openalex.org/W2072897447;https://openalex.org/W2074669169;https://openalex.org/W2077611589;https://openalex.org/W2077812306;https://openalex.org/W2079288973;https://openalex.org/W2083793682;https://openalex.org/W2087762516;https://openalex.org/W2093040750;https://openalex.org/W2122040390;https://openalex.org/W2128438887;https://openalex.org/W2135021377;https://openalex.org/W2144452238;https://openalex.org/W2150220236;https://openalex.org/W2154568261;https://openalex.org/W2163572752;https://openalex.org/W2218209912;https://openalex.org/W2263682169;https://openalex.org/W2275696275;https://openalex.org/W2540365088;https://openalex.org/W2563961554;https://openalex.org/W2589264714;https://openalex.org/W2606989030;https://openalex.org/W2744510879;https://openalex.org/W2751427740;https://openalex.org/W2761863472;https://openalex.org/W2768163011;https://openalex.org/W2772164149;https://openalex.org/W2781801925;https://openalex.org/W2794391233;https://openalex.org/W2889541841;https://openalex.org/W2950146322;https://openalex.org/W2963453445;https://openalex.org/W3098217728;https://openalex.org/W4238591974,Cybernetics;Citation;Computer science;Visualization;Research Object;Computational intelligence;Data science;Object (grammar);Quality (philosophy);Bibliometrics;Scopus;Library science;Artificial intelligence;Sociology;Regional science;Political science,Advanced Graph Neural Networks;Explainable Artificial Intelligence (XAI);Advanced Technologies in Various Fields
-OPENALEX,https://openalex.org/W4321354435,https://doi.org/10.1016/j.desal.2023.116482,,"A deep dive into membrane distillation literature with data analysis, bibliometric methods, and machine learning",DESALINATION,DESALINATION,2023,article,en,IMDEA Water,,553,,116482,116482,"Aytaç, 2023, DESALINATION",35,"Aytaç, Ersin;Khayet, M.","Aytaç, Ersin;Khayet, M.",Bülent Ecevit University;Universidad Complutense de Madrid;IMDEA Water,https://openalex.org/W1190915821;https://openalex.org/W1964580509;https://openalex.org/W1968011757;https://openalex.org/W1968034217;https://openalex.org/W2003947504;https://openalex.org/W2005776172;https://openalex.org/W2012507281;https://openalex.org/W2023951204;https://openalex.org/W2024422358;https://openalex.org/W2027413832;https://openalex.org/W2029391160;https://openalex.org/W2032027057;https://openalex.org/W2036440733;https://openalex.org/W2037856029;https://openalex.org/W2039979955;https://openalex.org/W2043616978;https://openalex.org/W2045402873;https://openalex.org/W2048494531;https://openalex.org/W2048661822;https://openalex.org/W2049247912;https://openalex.org/W2051639998;https://openalex.org/W2079970656;https://openalex.org/W2093035301;https://openalex.org/W2106721372;https://openalex.org/W2118061493;https://openalex.org/W2182559116;https://openalex.org/W2275696275;https://openalex.org/W2317869140;https://openalex.org/W2742453473;https://openalex.org/W2755950973;https://openalex.org/W2775683773;https://openalex.org/W2796448519;https://openalex.org/W2806236333;https://openalex.org/W2901960243;https://openalex.org/W2911132744;https://openalex.org/W2916013340;https://openalex.org/W2927879055;https://openalex.org/W2939821513;https://openalex.org/W3007038395;https://openalex.org/W3015364947;https://openalex.org/W3017384336;https://openalex.org/W3025046786;https://openalex.org/W3025325252;https://openalex.org/W3026965422;https://openalex.org/W3027942295;https://openalex.org/W3040040993;https://openalex.org/W3045495327;https://openalex.org/W3048290301;https://openalex.org/W3081195637;https://openalex.org/W3091961186;https://openalex.org/W3116936901;https://openalex.org/W3132895219;https://openalex.org/W3132900198;https://openalex.org/W3134570971;https://openalex.org/W3152730236;https://openalex.org/W3157428252;https://openalex.org/W3162153788;https://openalex.org/W3163210612;https://openalex.org/W3172184706;https://openalex.org/W3177036938;https://openalex.org/W3182035566;https://openalex.org/W3183633869;https://openalex.org/W3186377071;https://openalex.org/W3193328551;https://openalex.org/W3208821509;https://openalex.org/W3217529127;https://openalex.org/W4200234570;https://openalex.org/W4200247834;https://openalex.org/W4200300469;https://openalex.org/W4205627535;https://openalex.org/W4206088862;https://openalex.org/W4206185584;https://openalex.org/W4206419394;https://openalex.org/W4206785894;https://openalex.org/W4210475852;https://openalex.org/W4210864411;https://openalex.org/W4211223593;https://openalex.org/W4213162685;https://openalex.org/W4220917274;https://openalex.org/W4221028339;https://openalex.org/W4237504296;https://openalex.org/W4238669430;https://openalex.org/W4238996481;https://openalex.org/W4249580866;https://openalex.org/W4255031334;https://openalex.org/W4281260109;https://openalex.org/W4283019527;https://openalex.org/W4293730547;https://openalex.org/W4294622242;https://openalex.org/W4295008071;https://openalex.org/W4312448015;https://openalex.org/W6610221500;https://openalex.org/W6684366623;https://openalex.org/W6686433439;https://openalex.org/W6695147765;https://openalex.org/W6758259173;https://openalex.org/W6759809052;https://openalex.org/W6791300346;https://openalex.org/W6804581477;https://openalex.org/W6807871031;https://openalex.org/W6808269762;https://openalex.org/W6810286219,Membrane distillation;Desalination;Distillation;Computer science;Process (computing);Data science;Management science;Process engineering;Operations research;Chemistry;Engineering;Membrane;Chromatography,Membrane Separation Technologies;Solar-Powered Water Purification Methods;Membrane-based Ion Separation Techniques
-OPENALEX,https://openalex.org/W4221040532,https://doi.org/10.1016/j.eswa.2022.117000,,Machine learning and soft computing applications in textile and clothing supply chain: Bibliometric and network analyses to delineate future research agenda,EXPERT SYSTEMS WITH APPLICATIONS,EXPERT SYSTEMS WITH APPLICATIONS,2022,article,en,Indian Institute of Technology Delhi,,200,,117000,117000,"Arora, 2022, EXPERT SYSTEMS WITH APPLICATIONS",51,"Arora, Sanchi;Majumdar, Abhijit","Arora, Sanchi;Majumdar, Abhijit",Indian Institute of Technology Delhi,https://openalex.org/W58954717;https://openalex.org/W1562788212;https://openalex.org/W1965746216;https://openalex.org/W1967576324;https://openalex.org/W1968469154;https://openalex.org/W1968475341;https://openalex.org/W1968529367;https://openalex.org/W1970034291;https://openalex.org/W1970927811;https://openalex.org/W1971153583;https://openalex.org/W1975792549;https://openalex.org/W1976713707;https://openalex.org/W1977704857;https://openalex.org/W1981350336;https://openalex.org/W1986759740;https://openalex.org/W1989910766;https://openalex.org/W1993782638;https://openalex.org/W1998489527;https://openalex.org/W2001691783;https://openalex.org/W2007492518;https://openalex.org/W2008254025;https://openalex.org/W2011516823;https://openalex.org/W2011870943;https://openalex.org/W2012082498;https://openalex.org/W2016311778;https://openalex.org/W2018175331;https://openalex.org/W2018345613;https://openalex.org/W2020406507;https://openalex.org/W2023748160;https://openalex.org/W2026156619;https://openalex.org/W2030731788;https://openalex.org/W2031842291;https://openalex.org/W2032454107;https://openalex.org/W2033252229;https://openalex.org/W2033594126;https://openalex.org/W2034680966;https://openalex.org/W2035249336;https://openalex.org/W2035622977;https://openalex.org/W2036183396;https://openalex.org/W2039001958;https://openalex.org/W2040133609;https://openalex.org/W2040906208;https://openalex.org/W2046442262;https://openalex.org/W2048471306;https://openalex.org/W2052684391;https://openalex.org/W2053058963;https://openalex.org/W2060315726;https://openalex.org/W2060331378;https://openalex.org/W2061922306;https://openalex.org/W2061968279;https://openalex.org/W2062456035;https://openalex.org/W2062856725;https://openalex.org/W2066636486;https://openalex.org/W2068169989;https://openalex.org/W2070383764;https://openalex.org/W2070772100;https://openalex.org/W2073041749;https://openalex.org/W2073988312;https://openalex.org/W2074262003;https://openalex.org/W2075727088;https://openalex.org/W2076604763;https://openalex.org/W2080937899;https://openalex.org/W2091864141;https://openalex.org/W2093973217;https://openalex.org/W2114226901;https://openalex.org/W2118221227;https://openalex.org/W2129363794;https://openalex.org/W2129847851;https://openalex.org/W2131681506;https://openalex.org/W2133607644;https://openalex.org/W2133683819;https://openalex.org/W2134566505;https://openalex.org/W2134950073;https://openalex.org/W2141428366;https://openalex.org/W2150755904;https://openalex.org/W2152530798;https://openalex.org/W2153799454;https://openalex.org/W2163187547;https://openalex.org/W2174890733;https://openalex.org/W2193792017;https://openalex.org/W2203091106;https://openalex.org/W2282374200;https://openalex.org/W2428547613;https://openalex.org/W2522395253;https://openalex.org/W2528812466;https://openalex.org/W2533491448;https://openalex.org/W2546519383;https://openalex.org/W2550023227;https://openalex.org/W2559996032;https://openalex.org/W2568069995;https://openalex.org/W2568552722;https://openalex.org/W2574751020;https://openalex.org/W2586088018;https://openalex.org/W2592092867;https://openalex.org/W2593110037;https://openalex.org/W2593850823;https://openalex.org/W2606606758;https://openalex.org/W2615511951;https://openalex.org/W2615512215;https://openalex.org/W2743521732;https://openalex.org/W2744003934;https://openalex.org/W2764031900;https://openalex.org/W2772761593;https://openalex.org/W2784180941;https://openalex.org/W2788814106;https://openalex.org/W2789184060;https://openalex.org/W2793174271;https://openalex.org/W2793359360;https://openalex.org/W2795647708;https://openalex.org/W2796765303;https://openalex.org/W2807684636;https://openalex.org/W2810276845;https://openalex.org/W2889059162;https://openalex.org/W2891979815;https://openalex.org/W2901499640;https://openalex.org/W2907705461;https://openalex.org/W2919549704;https://openalex.org/W2940036667;https://openalex.org/W2944603060;https://openalex.org/W2954923731;https://openalex.org/W2959048647;https://openalex.org/W2967267206;https://openalex.org/W2971138471;https://openalex.org/W2972258942;https://openalex.org/W3001077607;https://openalex.org/W3006688225;https://openalex.org/W3019427697;https://openalex.org/W3021925331;https://openalex.org/W3024726348;https://openalex.org/W3029349200;https://openalex.org/W3029619601;https://openalex.org/W3037646074;https://openalex.org/W3038952158;https://openalex.org/W3041912729;https://openalex.org/W3043469888;https://openalex.org/W3089819260;https://openalex.org/W3091737531;https://openalex.org/W3092139698;https://openalex.org/W3099768174;https://openalex.org/W3101964319;https://openalex.org/W3107752708;https://openalex.org/W3111547024;https://openalex.org/W3125707221;https://openalex.org/W3131345956;https://openalex.org/W4210949841;https://openalex.org/W4211007335;https://openalex.org/W6653506936;https://openalex.org/W6727672186;https://openalex.org/W6728963153;https://openalex.org/W6734081584;https://openalex.org/W6745728535;https://openalex.org/W6748880627;https://openalex.org/W6755112764;https://openalex.org/W6767986271;https://openalex.org/W6783810191;https://openalex.org/W6791095613;https://openalex.org/W6797249196,Clothing;Supply chain;Computer science;Quality (philosophy);Soft computing;Textile;Fast fashion;Control (management);Fuzzy logic;Manufacturing engineering;Artificial intelligence;Data science;Business;Engineering;Marketing,Textile materials and evaluations;Industrial Vision Systems and Defect Detection;Color perception and design
-OPENALEX,https://openalex.org/W4391360895,https://doi.org/10.1007/s10462-023-10628-8,,Exploring the trend of recognizing apple leaf disease detection through machine learning: a comprehensive analysis using bibliometric techniques,ARTIFICIAL INTELLIGENCE REVIEW,ARTIFICIAL INTELLIGENCE REVIEW,2024,article,en,Chitkara University,"Abstract This study’s foremost objectives were to scrutinize how unexpected weather affects agricultural output and to assess how well AI-based machine learning and deep leaning algorithms work for spotting apple leaf diseases. The researchers carried out a bibliometric study to obtain understanding of the current research trends, citation patterns, ownership and partnership arrangements, publishing patterns, and other parameters related to early identification of apple illnesses. Comprehensive interdisciplinary scientific maps are limited because syndrome recognition is not restricted to any solitary arena of research, despite the fact that there have been many studies on the identification of apple diseases. By employing a scientometric technique and 109 publications from the Scopus database published between 2011 and 2022, this study attempted to assess the condition of the research area and combine knowledge frameworks. To find important journals, authors, nations, articles, and topics, the study used the automated processes of VOSviewer and Biblioshiny software. Patterns and trends were discovered using citation counts, social network analysis, and citation and co-citation studies.",57,2,,,"Bonkra, 2024, ARTIFICIAL INTELLIGENCE REVIEW",44,"Bonkra, Anupam;Pathak, Sunil;Kaur, Amandeep;Shah, Mohd Asif","Bonkra, Anupam;Pathak, Sunil;Kaur, Amandeep;Shah, Mohd Asif",Chandigarh University;Punjab Engineering College;Chitkara University;Amhara Agricultural Research Institute;Kebri Dehar University,https://openalex.org/W581488446;https://openalex.org/W1535753778;https://openalex.org/W1678171433;https://openalex.org/W1972012119;https://openalex.org/W1974141360;https://openalex.org/W1974552298;https://openalex.org/W1978305786;https://openalex.org/W1983865151;https://openalex.org/W1985473486;https://openalex.org/W1989369420;https://openalex.org/W2003007706;https://openalex.org/W2056848809;https://openalex.org/W2131375706;https://openalex.org/W2150220236;https://openalex.org/W2163803148;https://openalex.org/W2590209697;https://openalex.org/W2755950973;https://openalex.org/W2769636271;https://openalex.org/W2774944751;https://openalex.org/W2776705292;https://openalex.org/W2892258254;https://openalex.org/W2934580386;https://openalex.org/W2940775598;https://openalex.org/W2941288374;https://openalex.org/W2944599236;https://openalex.org/W2973152666;https://openalex.org/W3011791478;https://openalex.org/W3028000264;https://openalex.org/W3037845067;https://openalex.org/W3042621236;https://openalex.org/W3082606970;https://openalex.org/W3086962397;https://openalex.org/W3117722799;https://openalex.org/W3119842544;https://openalex.org/W3130490319;https://openalex.org/W3133307794;https://openalex.org/W3133429097;https://openalex.org/W3135999592;https://openalex.org/W3158760582;https://openalex.org/W3158764639;https://openalex.org/W3160856016;https://openalex.org/W3163885127;https://openalex.org/W3166574792;https://openalex.org/W3174385379;https://openalex.org/W3183606774;https://openalex.org/W3187050136;https://openalex.org/W3189818995;https://openalex.org/W3205260081;https://openalex.org/W3215484246;https://openalex.org/W4210660549;https://openalex.org/W4249894953;https://openalex.org/W4285115839;https://openalex.org/W4285328170;https://openalex.org/W4285815338;https://openalex.org/W4293009360;https://openalex.org/W4296143681;https://openalex.org/W4309355882;https://openalex.org/W4312787172;https://openalex.org/W4313160571;https://openalex.org/W4320496104;https://openalex.org/W4320728788;https://openalex.org/W4322577828;https://openalex.org/W4362496509;https://openalex.org/W4362496522;https://openalex.org/W4362500913,Computer science;Machine learning;Artificial intelligence;Pattern recognition (psychology),Plant Pathogens and Fungal Diseases;Phytoplasmas and Hemiptera pathogens;Plant Physiology and Cultivation Studies
-OPENALEX,https://openalex.org/W4386803046,https://doi.org/10.32479/ijeep.14832,,New Insights into the Emerging Trends Research of Machine and Deep Learning Applications in Energy Storage: A Bibliometric Analysis and Publication Trends,INTERNATIONAL JOURNAL OF ENERGY ECONOMICS AND POLICY,INTERNATIONAL JOURNAL OF ENERGY ECONOMICS AND POLICY,2023,article,en,Istanbul Commerce University,"The publication trends and bibliometric analysis of the research landscape on the applications of machine and deep learning in energy storage (MDLES) research were examined in this study based on published documents in the Elsevier Scopus database between 2012 and 2022. The PRISMA technique employed to identify, screen, and filter related publications on MDLES research recovered 969 documents comprising articles, conference papers, and reviews published in English. The results showed that the publications count on the topic increased from 3 to 385 (or a 12,733.3% increase) along with citations between 2012 and 2022. The high publications and citations rate was ascribed to the MDLES research impact, co-authorships/collaborations, as well as the source title/journals’ reputation, multidisciplinary nature, and research funding. The top/most prolific researcher, institution, country, and funding body on MDLES research are; is Yan Xu, Tsinghua University, China, and the National Natural Science Foundation of China, respectively. Keywords occurrence analysis revealed three clusters or hotspots based on machine learning, digital storage, and Energy Storage. Further analysis of the research landscape showed that MDLES research is currently and largely focused on the application of machine/deep learning for predicting, operating, and optimising energy storage as well as the design of energy storage materials for renewable energy technologies such as wind, and PV solar. However, future research will presumably include a focus on advanced energy materials development, operational systems monitoring and control as well as techno-economic analysis to address challenges associated with energy efficiency analysis, costing of renewable energy electricity pricing, trading, and revenue prediction",13,5,303,314,"Ajibade, 2023, INTERNATIONAL JOURNAL OF ENERGY ECONOMICS AND POLICY",39,"Ajibade, Samuel-Soma M.;Zaïdi, Abdelhamid;Luhayb, Asamh Saleh M. Al;Adediran, Anthonia Oluwatosin;Voumik, Liton Chandra;Rabbi, Fazle","Ajibade, Samuel-Soma M.;Zaïdi, Abdelhamid;Luhayb, Asamh Saleh M. Al;Adediran, Anthonia Oluwatosin;Voumik, Liton Chandra;Rabbi, Fazle",Istanbul Commerce University;Qassim University;Universidade Federal de Uberlândia;Noakhali Science and Technology University,https://openalex.org/W618969766;https://openalex.org/W1588786163;https://openalex.org/W1592409232;https://openalex.org/W1769221173;https://openalex.org/W1872649730;https://openalex.org/W1917633107;https://openalex.org/W1982396829;https://openalex.org/W1984703120;https://openalex.org/W1987027200;https://openalex.org/W2011133221;https://openalex.org/W2057480616;https://openalex.org/W2058391473;https://openalex.org/W2086496065;https://openalex.org/W2091154441;https://openalex.org/W2093664903;https://openalex.org/W2166692234;https://openalex.org/W2202159449;https://openalex.org/W2235853075;https://openalex.org/W2295405103;https://openalex.org/W2318201131;https://openalex.org/W2343280481;https://openalex.org/W2344174278;https://openalex.org/W2505251935;https://openalex.org/W2604611197;https://openalex.org/W2606577704;https://openalex.org/W2741358105;https://openalex.org/W2766786289;https://openalex.org/W2771505708;https://openalex.org/W2780553247;https://openalex.org/W2785929784;https://openalex.org/W2800156975;https://openalex.org/W2804990541;https://openalex.org/W2884320687;https://openalex.org/W2885578090;https://openalex.org/W2888142130;https://openalex.org/W2891483647;https://openalex.org/W2901225969;https://openalex.org/W2910849319;https://openalex.org/W2921149492;https://openalex.org/W2931197960;https://openalex.org/W2935877504;https://openalex.org/W2941054058;https://openalex.org/W2944678844;https://openalex.org/W2945288028;https://openalex.org/W2963691557;https://openalex.org/W2967729973;https://openalex.org/W2981470893;https://openalex.org/W2989671254;https://openalex.org/W2990466689;https://openalex.org/W3000731708;https://openalex.org/W3006269673;https://openalex.org/W3007407721;https://openalex.org/W3007550778;https://openalex.org/W3009652674;https://openalex.org/W3015704027;https://openalex.org/W3021912390;https://openalex.org/W3023640102;https://openalex.org/W3024350433;https://openalex.org/W3034026285;https://openalex.org/W3037631072;https://openalex.org/W3039342821;https://openalex.org/W3040330580;https://openalex.org/W3041101137;https://openalex.org/W3044303740;https://openalex.org/W3045302506;https://openalex.org/W3080311931;https://openalex.org/W3087769098;https://openalex.org/W3113216760;https://openalex.org/W3122735851;https://openalex.org/W3124296095;https://openalex.org/W3133541835;https://openalex.org/W3138654112;https://openalex.org/W3149578452;https://openalex.org/W3150580950;https://openalex.org/W3156040358;https://openalex.org/W3158361850;https://openalex.org/W3187418191;https://openalex.org/W3203259196;https://openalex.org/W4200235241;https://openalex.org/W4200308385;https://openalex.org/W4205561506;https://openalex.org/W4206784551;https://openalex.org/W4206936355;https://openalex.org/W4210379075;https://openalex.org/W4211018499;https://openalex.org/W4223517519;https://openalex.org/W4226262670;https://openalex.org/W4230046549;https://openalex.org/W4230248672;https://openalex.org/W4230648463;https://openalex.org/W4230798432;https://openalex.org/W4236660208;https://openalex.org/W4237117882;https://openalex.org/W4252775465;https://openalex.org/W4253848338;https://openalex.org/W4281557767;https://openalex.org/W4281721786;https://openalex.org/W4284959951;https://openalex.org/W4292072448;https://openalex.org/W4294688964;https://openalex.org/W4297478379;https://openalex.org/W4306252271;https://openalex.org/W4309205153;https://openalex.org/W4311098650;https://openalex.org/W4320063314;https://openalex.org/W4320063358;https://openalex.org/W4366588036;https://openalex.org/W4381549141;https://openalex.org/W4404193242;https://openalex.org/W7008203420,Renewable energy;Scopus;Computer science;Bibliometrics;Data science;Library science;Engineering;Political science,Hybrid Renewable Energy Systems;Energy Load and Power Forecasting;Microgrid Control and Optimization
-OPENALEX,https://openalex.org/W4408900440,https://doi.org/10.3389/fdgth.2025.1557467,https://pubmed.ncbi.nlm.nih.gov/40212895,Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis,FRONTIERS IN DIGITAL HEALTH,FRONTIERS IN DIGITAL HEALTH,2025,review,en,Anglia Ruskin University,"Background: Type 2 Diabetes Mellitus (T2DM) remains a critical global health challenge, necessitating robust predictive models to enable early detection and personalized interventions. This study presents a comprehensive bibliometric and systematic review of 33 years (1991-2024) of research on machine learning (ML) and artificial intelligence (AI) applications in T2DM prediction. It highlights the growing complexity of the field and identifies key trends, methodologies, and research gaps. Methods: A systematic methodology guided the literature selection process, starting with keyword identification using Term Frequency-Inverse Document Frequency (TF-IDF) and expert input. Based on these refined keywords, literature was systematically selected using PRISMA guidelines, resulting in a dataset of 2,351 articles from Web of Science and Scopus databases. Bibliometric analysis was performed on the entire selected dataset using tools such as VOSviewer and Bibliometrix, enabling thematic clustering, co-citation analysis, and network visualization. To assess the most impactful literature, a dual-criteria methodology combining relevance and impact scores was applied. Articles were qualitatively assessed on their alignment with T2DM prediction using a four-point relevance scale and quantitatively evaluated based on citation metrics normalized within subject, journal, and publication year. Articles scoring above a predefined threshold were selected for detailed review. The selected literature spans four time periods: 1991-2000, 2001-2010, 2011-2020, and 2021-2024. Results: The bibliometric findings reveal exponential growth in publications since 2010, with the USA and UK leading contributions, followed by emerging players like Singapore and India. Key thematic clusters include foundational ML techniques, epidemiological forecasting, predictive modelling, and clinical applications. Ensemble methods (e.g., Random Forest, Gradient Boosting) and deep learning models (e.g., Convolutional Neural Networks) dominate recent advancements. Literature analysis reveals that, early studies primarily used demographic and clinical variables, while recent efforts integrate genetic, lifestyle, and environmental predictors. Additionally, literature analysis highlights advances in integrating real-world datasets, emerging trends like federated learning, and explainability tools such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). Conclusion: Future work should address gaps in generalizability, interdisciplinary T2DM prediction research, and psychosocial integration, while also focusing on clinically actionable solutions and real-world applicability to combat the growing diabetes epidemic effectively.",7,,1557467,1557467,"Kiran, 2025, FRONTIERS IN DIGITAL HEALTH",43,"Kiran, Mahreen;Xie, Ying;Anjum, Nasreen;Ball, Graham;Pierścionek, Barbara;Russell, Duncan","Kiran, Mahreen;Xie, Ying;Anjum, Nasreen;Ball, Graham;Pierścionek, Barbara;Russell, Duncan",Anglia Ruskin University;Cranfield University;University of Portsmouth;Digital Catapult,https://openalex.org/W129507607;https://openalex.org/W582134693;https://openalex.org/W807187018;https://openalex.org/W1563923811;https://openalex.org/W1618905105;https://openalex.org/W1833120343;https://openalex.org/W1848590964;https://openalex.org/W1963870857;https://openalex.org/W1977328750;https://openalex.org/W1989022033;https://openalex.org/W2012942264;https://openalex.org/W2014057135;https://openalex.org/W2020267609;https://openalex.org/W2022118522;https://openalex.org/W2026841079;https://openalex.org/W2060261318;https://openalex.org/W2065692273;https://openalex.org/W2066698760;https://openalex.org/W2068485445;https://openalex.org/W2094675826;https://openalex.org/W2097453405;https://openalex.org/W2103556204;https://openalex.org/W2108632167;https://openalex.org/W2110694570;https://openalex.org/W2117290533;https://openalex.org/W2118414527;https://openalex.org/W2122381408;https://openalex.org/W2125775672;https://openalex.org/W2131414141;https://openalex.org/W2132091485;https://openalex.org/W2148143831;https://openalex.org/W2150220236;https://openalex.org/W2157784421;https://openalex.org/W2158822657;https://openalex.org/W2191755155;https://openalex.org/W2198899446;https://openalex.org/W2236731268;https://openalex.org/W2239135493;https://openalex.org/W2282821441;https://openalex.org/W2340262115;https://openalex.org/W2379581788;https://openalex.org/W2484845775;https://openalex.org/W2531360867;https://openalex.org/W2544538455;https://openalex.org/W2554536751;https://openalex.org/W2569214105;https://openalex.org/W2570760970;https://openalex.org/W2577749910;https://openalex.org/W2611138580;https://openalex.org/W2612292012;https://openalex.org/W2622382573;https://openalex.org/W2626967530;https://openalex.org/W2738681903;https://openalex.org/W2775450699;https://openalex.org/W2791659097;https://openalex.org/W2793071066;https://openalex.org/W2796441011;https://openalex.org/W2798790543;https://openalex.org/W2800094831;https://openalex.org/W2805782847;https://openalex.org/W2806075129;https://openalex.org/W2883730939;https://openalex.org/W2900329012;https://openalex.org/W2904931021;https://openalex.org/W2905097366;https://openalex.org/W2906295032;https://openalex.org/W2908201961;https://openalex.org/W2914383481;https://openalex.org/W2914959816;https://openalex.org/W2921196390;https://openalex.org/W2927351257;https://openalex.org/W2946074361;https://openalex.org/W2947730823;https://openalex.org/W2950722229;https://openalex.org/W2962862931;https://openalex.org/W2965372631;https://openalex.org/W2971160393;https://openalex.org/W2975495759;https://openalex.org/W2981121978;https://openalex.org/W2981731882;https://openalex.org/W2986446268;https://openalex.org/W2989180667;https://openalex.org/W2992144222;https://openalex.org/W2997476292;https://openalex.org/W2997591727;https://openalex.org/W2999365564;https://openalex.org/W3008233702;https://openalex.org/W3011403448;https://openalex.org/W3011491737;https://openalex.org/W3017209650;https://openalex.org/W3020776760;https://openalex.org/W3021463136;https://openalex.org/W3024571102;https://openalex.org/W3043363778;https://openalex.org/W3045445851;https://openalex.org/W3047812492;https://openalex.org/W3090072574;https://openalex.org/W3096850376;https://openalex.org/W3106920072;https://openalex.org/W3111667594;https://openalex.org/W3120624594;https://openalex.org/W3125292757;https://openalex.org/W3125841495;https://openalex.org/W3126599133;https://openalex.org/W3130449168;https://openalex.org/W3134138028;https://openalex.org/W3135225825;https://openalex.org/W3135503315;https://openalex.org/W3135521497;https://openalex.org/W3135573238;https://openalex.org/W3137532457;https://openalex.org/W3152731513;https://openalex.org/W3153116130;https://openalex.org/W3159186882;https://openalex.org/W3159274100;https://openalex.org/W3164294986;https://openalex.org/W3165845449;https://openalex.org/W3165907317;https://openalex.org/W3171397873;https://openalex.org/W3172328600;https://openalex.org/W3174030102;https://openalex.org/W3174053279;https://openalex.org/W3176463933;https://openalex.org/W3178109510;https://openalex.org/W3179092643;https://openalex.org/W3180846682;https://openalex.org/W3184080322;https://openalex.org/W3193202819;https://openalex.org/W3196511944;https://openalex.org/W3197676009;https://openalex.org/W3202323106;https://openalex.org/W3202796611;https://openalex.org/W3208700431;https://openalex.org/W3214100756;https://openalex.org/W3216633527;https://openalex.org/W3216815601;https://openalex.org/W4200115535;https://openalex.org/W4200255742;https://openalex.org/W4200265911;https://openalex.org/W4200282090;https://openalex.org/W4200485674;https://openalex.org/W4206956476;https://openalex.org/W4206980706;https://openalex.org/W4211253314;https://openalex.org/W4220709985;https://openalex.org/W4220755962;https://openalex.org/W4220974901;https://openalex.org/W4220999455;https://openalex.org/W4221125739;https://openalex.org/W4223485277;https://openalex.org/W4223893747;https://ope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science;Relevance (law);Systematic review;Bibliometrics;Data science;Citation;Cluster analysis;Artificial intelligence;Machine learning;Data mining;Information retrieval;MEDLINE;Library science,Artificial Intelligence in Healthcare;Machine Learning in Healthcare;Artificial Intelligence in Healthcare and Education
-OPENALEX,https://openalex.org/W3135775894,https://doi.org/10.1177/00368504211029777,https://pubmed.ncbi.nlm.nih.gov/35220816,Machine learning on small size samples: A synthetic knowledge synthesis,SCIENCE PROGRESS,SCIENCE PROGRESS,2022,article,en,University of Maribor,"Machine Learning is an increasingly important technology dealing with the growing complexity of the digitalised world. Despite the fact, that we live in a 'Big data' world where, almost 'everything' is digitally stored, there are many real-world situations, where researchers are still faced with small data samples. The present bibliometric knowledge synthesis study aims to answer the research question 'What is the small data problem in machine learning and how it is solved?' The analysis a positive trend in the number of research publications and substantial growth of the research community, indicating that the research field is reaching maturity. Most productive countries are China, United States and United Kingdom. Despite notable international cooperation, the regional concentration of research literature production in economically more developed countries was observed. Thematic analysis identified four research themes. The themes are concerned with to dimension reduction in complex big data analysis, data augmentation techniques in deep learning, data mining and statistical learning on small datasets.",105,1,368504211029777,368504211029777,"Kokol, 2022, SCIENCE PROGRESS",243,"Kokol, Peter;Kokol, Marko;Zagoranski, Sašo","Kokol, Peter;Kokol, Marko;Zagoranski, Sašo",University of Maribor,https://openalex.org/W150292108;https://openalex.org/W246829850;https://openalex.org/W415541256;https://openalex.org/W1901616594;https://openalex.org/W2005772800;https://openalex.org/W2016919366;https://openalex.org/W2055499166;https://openalex.org/W2078019847;https://openalex.org/W2100642106;https://openalex.org/W2150220236;https://openalex.org/W2154243653;https://openalex.org/W2205558186;https://openalex.org/W2417429787;https://openalex.org/W2474204052;https://openalex.org/W2493157521;https://openalex.org/W2558111825;https://openalex.org/W2569330741;https://openalex.org/W2586821431;https://openalex.org/W2597959056;https://openalex.org/W2605315194;https://openalex.org/W2625386759;https://openalex.org/W2641342067;https://openalex.org/W2750988938;https://openalex.org/W2753564313;https://openalex.org/W2770456481;https://openalex.org/W2794386528;https://openalex.org/W2800722845;https://openalex.org/W2801285981;https://openalex.org/W2803793592;https://openalex.org/W2809254203;https://openalex.org/W2884564258;https://openalex.org/W2902390267;https://openalex.org/W2916091221;https://openalex.org/W2936715908;https://openalex.org/W2941930405;https://openalex.org/W2945252059;https://openalex.org/W2956096659;https://openalex.org/W2962177523;https://openalex.org/W2962823337;https://openalex.org/W2963560899;https://openalex.org/W2966207284;https://openalex.org/W2966741169;https://openalex.org/W2969658393;https://openalex.org/W2974836096;https://openalex.org/W2975573752;https://openalex.org/W2980823878;https://openalex.org/W2981679558;https://openalex.org/W2981704932;https://openalex.org/W2983030060;https://openalex.org/W2995942064;https://openalex.org/W3000524228;https://openalex.org/W3002188287;https://openalex.org/W3004754245;https://openalex.org/W3006767019;https://openalex.org/W3007628380;https://openalex.org/W3008647172;https://openalex.org/W3010414725;https://openalex.org/W3014387504;https://openalex.org/W3014524176;https://openalex.org/W3017138949;https://openalex.org/W3024240210;https://openalex.org/W3034252585;https://openalex.org/W3037097018;https://openalex.org/W3039041742;https://openalex.org/W3088291765;https://openalex.org/W3091133721;https://openalex.org/W3092028662;https://openalex.org/W3106321705;https://openalex.org/W3121116615;https://openalex.org/W3126053921;https://openalex.org/W3130198311;https://openalex.org/W3138953784;https://openalex.org/W3140905632;https://openalex.org/W4230653117;https://openalex.org/W4231235517;https://openalex.org/W4233723955;https://openalex.org/W4239768083;https://openalex.org/W4244516567;https://openalex.org/W4255030514,Big data;Data science;Maturity (psychological);Field (mathematics);China;Computer science;Dimension (graph theory);Thematic analysis;Artificial intelligence;Political science;Sociology;Data mining;Social science;Mathematics;Qualitative research,Big Data and Business Intelligence;Big Data Technologies and Applications;Artificial Intelligence in Healthcare
-OPENALEX,https://openalex.org/W4292600288,https://doi.org/10.1016/j.autcon.2022.104532,,Machine learning techniques applied to construction: A hybrid bibliometric analysis of advances and future directions,AUTOMATION IN CONSTRUCTION,AUTOMATION IN CONSTRUCTION,2022,article,en,Pontificia Universidad Católica de Valparaíso,,142,,104532,104532,"García, 2022, AUTOMATION IN CONSTRUCTION",80,"García, José;Villavicencio, Gabriel;Altimiras, Francisco;Crawford, Broderick;Soto, Ricardo;Minatogawa, Vinicius;Franco, Matheus;Martínez-Muñoz, David;Yepes, Víctor","García, José;Villavicencio, Gabriel;Altimiras, Francisco;Crawford, Broderick;Soto, Ricardo;Minatogawa, Vinicius;Franco, Matheus;Martínez-Muñoz, David;Yepes, Víctor",Pontificia Universidad Católica de Valparaíso,https://openalex.org/W1158935686;https://openalex.org/W1842075455;https://openalex.org/W1902027874;https://openalex.org/W2002844166;https://openalex.org/W2008306839;https://openalex.org/W2134731454;https://openalex.org/W2135455887;https://openalex.org/W2195180028;https://openalex.org/W2321654184;https://openalex.org/W2424728784;https://openalex.org/W2494645788;https://openalex.org/W2507244352;https://openalex.org/W2537411327;https://openalex.org/W2590209538;https://openalex.org/W2625378322;https://openalex.org/W2748643398;https://openalex.org/W2755950973;https://openalex.org/W2757455114;https://openalex.org/W2786672974;https://openalex.org/W2787948291;https://openalex.org/W2795876296;https://openalex.org/W2796105695;https://openalex.org/W2796506861;https://openalex.org/W2800343216;https://openalex.org/W2800346298;https://openalex.org/W2806229851;https://openalex.org/W2809438835;https://openalex.org/W2809503103;https://openalex.org/W2814406141;https://openalex.org/W2886369963;https://openalex.org/W2890028683;https://openalex.org/W2890167402;https://openalex.org/W2892086004;https://openalex.org/W2894336343;https://openalex.org/W2896457183;https://openalex.org/W2898234019;https://openalex.org/W2909580866;https://openalex.org/W2912530595;https://openalex.org/W2915074154;https://openalex.org/W2921796625;https://openalex.org/W2936891363;https://openalex.org/W2940384555;https://openalex.org/W2941147690;https://openalex.org/W2943692341;https://openalex.org/W2944114041;https://openalex.org/W2946640301;https://openalex.org/W2955558066;https://openalex.org/W2966746360;https://openalex.org/W2968561194;https://openalex.org/W2972445383;https://openalex.org/W2978627518;https://openalex.org/W2983902176;https://openalex.org/W2985669209;https://openalex.org/W2990574233;https://openalex.org/W2990784565;https://openalex.org/W2995753587;https://openalex.org/W3004315419;https://openalex.org/W3012222204;https://openalex.org/W3015152352;https://openalex.org/W3015671042;https://openalex.org/W3026226589;https://openalex.org/W3030588023;https://openalex.org/W3038415525;https://openalex.org/W3122614502;https://openalex.org/W3131952439;https://openalex.org/W3153764057;https://openalex.org/W3158648516;https://openalex.org/W3163259028;https://openalex.org/W3170735608;https://openalex.org/W3173025278;https://openalex.org/W3183876120;https://openalex.org/W3194473882;https://openalex.org/W3198355034;https://openalex.org/W3202282233;https://openalex.org/W3215757687;https://openalex.org/W3216986367;https://openalex.org/W4200521387;https://openalex.org/W4210273991;https://openalex.org/W4210312791;https://openalex.org/W4210403381;https://openalex.org/W4221142221;https://openalex.org/W4231510805;https://openalex.org/W4231863044;https://openalex.org/W4233164864;https://openalex.org/W4241104219;https://openalex.org/W6606062967;https://openalex.org/W6639619044;https://openalex.org/W6640399235;https://openalex.org/W6679396105;https://openalex.org/W6713214251;https://openalex.org/W6720450344;https://openalex.org/W6724009921;https://openalex.org/W6729054752;https://openalex.org/W6738893313;https://openalex.org/W6748489449;https://openalex.org/W6752256386;https://openalex.org/W6753397543;https://openalex.org/W6754692489;https://openalex.org/W6761880298;https://openalex.org/W6766646568;https://openalex.org/W6767616814;https://openalex.org/W6775744832;https://openalex.org/W6788784772;https://openalex.org/W6796932044;https://openalex.org/W6798663949;https://openalex.org/W6800665057;https://openalex.org/W6804393915;https://openalex.org/W6807102060,Computer science;Engineering;Data science;Machine learning;Artificial intelligence,Infrastructure Maintenance and Monitoring;Occupational Health and Safety Research;BIM and Construction Integration
-OPENALEX,https://openalex.org/W2765743217,https://doi.org/10.1007/978-981-10-5523-2_20,,A Bibliometric Analysis of Recent Research on Machine Learning for Cyber Security,LECTURE NOTES IN NETWORKS AND SYSTEMS,LECTURE NOTES IN NETWORKS AND SYSTEMS,2017,book-chapter,en,Seva Mandir,,,,213,226,"Makawana, 2017, LECTURE NOTES IN NETWORKS AND SYSTEMS",22,"Makawana, Pooja R.;Jhaveri, Rutvij H.","Makawana, Pooja R.;Jhaveri, Rutvij H.",Seva Mandir,https://openalex.org/W856269280;https://openalex.org/W1983551905;https://openalex.org/W2016441490;https://openalex.org/W2107879036;https://openalex.org/W2186054980;https://openalex.org/W2246154150,Globe;Computer science;The Internet;Data science;Cyber threats;Information security;Computer security;Artificial intelligence;World Wide Web,Network Security and Intrusion Detection;Information and Cyber Security;Advanced Malware Detection Techniques
-OPENALEX,https://openalex.org/W4319441392,https://doi.org/10.1016/j.compbiomed.2023.106638,https://pubmed.ncbi.nlm.nih.gov/36764155,Machine learning in antibacterial discovery and development: A bibliometric and network analysis of research hotspots and trends,COMPUTERS IN BIOLOGY AND MEDICINE,COMPUTERS IN BIOLOGY AND MEDICINE,2023,article,en,University of the Basque Country,,155,,106638,106638,"Diéguez‐Santana, 2023, COMPUTERS IN BIOLOGY AND MEDICINE",25,"Diéguez‐Santana, Karel;González‐Díaz, Humberto","Diéguez‐Santana, Karel;González‐Díaz, Humberto",University of the Basque Country;Universidad Regional Amazónica IKIAM;Ikerbasque,https://openalex.org/W1021000864;https://openalex.org/W1583410561;https://openalex.org/W1950993160;https://openalex.org/W1986157690;https://openalex.org/W2029088861;https://openalex.org/W2037521840;https://openalex.org/W2039457532;https://openalex.org/W2081413516;https://openalex.org/W2085102470;https://openalex.org/W2086249877;https://openalex.org/W2093543476;https://openalex.org/W2112411768;https://openalex.org/W2129115651;https://openalex.org/W2143283561;https://openalex.org/W2150220236;https://openalex.org/W2150258411;https://openalex.org/W2150962198;https://openalex.org/W2155997698;https://openalex.org/W2163646378;https://openalex.org/W2170482677;https://openalex.org/W2171490806;https://openalex.org/W2191867853;https://openalex.org/W2273267066;https://openalex.org/W2345209196;https://openalex.org/W2550329658;https://openalex.org/W2567231876;https://openalex.org/W2583907533;https://openalex.org/W2693176153;https://openalex.org/W2734366854;https://openalex.org/W2754494334;https://openalex.org/W2755950973;https://openalex.org/W2767711842;https://openalex.org/W2775714759;https://openalex.org/W2793710025;https://openalex.org/W2896298459;https://openalex.org/W2898861515;https://openalex.org/W2901930347;https://openalex.org/W2921107389;https://openalex.org/W2937307539;https://openalex.org/W2944680032;https://openalex.org/W2954214838;https://openalex.org/W2959938226;https://openalex.org/W2963454409;https://openalex.org/W2979610116;https://openalex.org/W2985684656;https://openalex.org/W2989739077;https://openalex.org/W2990450011;https://openalex.org/W3007309629;https://openalex.org/W3036139765;https://openalex.org/W3036656090;https://openalex.org/W3037654077;https://openalex.org/W3094089973;https://openalex.org/W3107222016;https://openalex.org/W3131345956;https://openalex.org/W3139613220;https://openalex.org/W3163901847;https://openalex.org/W3181204626;https://openalex.org/W3184778096;https://openalex.org/W3193226555;https://openalex.org/W3207944298;https://openalex.org/W4205739101;https://openalex.org/W4207009226;https://openalex.org/W4281675596;https://openalex.org/W6670944780;https://openalex.org/W6672146451;https://openalex.org/W6705035250;https://openalex.org/W6744394771;https://openalex.org/W6762168938;https://openalex.org/W6799240867,Cheminformatics;Linkage (software);Computer science;Data science;Big data;Field (mathematics);Scopus;Productivity;Artificial intelligence;Data mining;Bioinformatics;MEDLINE;Chemistry;Mathematics,Computational Drug Discovery Methods;vaccines and immunoinformatics approaches;Machine Learning in Materials Science
-OPENALEX,https://openalex.org/W3176619972,https://doi.org/10.1016/j.xkme.2021.04.012,https://pubmed.ncbi.nlm.nih.gov/34693256,Machine Learning Applications in Nephrology: A Bibliometric Analysis Comparing Kidney Studies to Other Medicine Subspecialities,KIDNEY MEDICINE,KIDNEY MEDICINE,2021,article,en,Boston University,"RATIONALE & OBJECTIVES: Artificial intelligence driven by machine learning algorithms is being increasingly employed for early detection, disease diagnosis, and clinical management. We explored the use of machine learning-driven advancements in kidney research compared with other organ-specific fields. STUDY DESIGN: Cross-sectional bibliometric analysis. SETTING & PARTICIPANTS: ISI Web of Science database was queried using specific Medical Subject Headings (MeSH) terms about the organ system, journal International Standard Serial Number, and research methodology. In parallel, we screened the National Institutes of Health (NIH) RePORTER website to explore funded grants that proposed the use of machine learning as a methodology. PREDICTORS: Number of publications using machine learning as a research method. OUTCOME: Articles were characterized by research methodology among 5 organ systems (brain, heart, kidney, liver, and lung). Grants funded by NIH for machine learning were characterized by study sections. ANALYTICAL APPROACH: Percentages of articles using machine learning and other research methodologies were compared among 5 organ systems. RESULTS: Machine learning-based articles that are focused on the kidney accounted for 3.2% of the total relevant articles from the 5 organ systems. Specifically, brain research published over 19-fold higher number of articles than kidney research. As compared with machine learning, conventional statistical approaches such as the Cox proportional hazard model were used 9-fold higher in articles related to kidney research. In general, a lower utilization of machine learning-based approaches was observed in organ-specific specialty journals than the broad interdisciplinary journals. The digestive disease, kidney, and urology study sections funded 122 applications proposing machine learning-based approaches compared to 265 applications from the neurology, neuropsychology, and neuropathology study sections. LIMITATIONS: Observational study. CONCLUSIONS: Our analysis suggests lowest use of machine learning as a research tool among kidney researchers compared with other organ-specific researchers, underscoring a need to better inform the kidney research community about this emerging data analytic tool.",3,5,762,767,"Verma, 2021, KIDNEY MEDICINE",29,"Verma, Ashish;Chitalia, Vipul C.;Waikar, Sushrut S.;Kolachalama, Vijaya B.","Verma, Ashish;Chitalia, Vipul C.;Waikar, Sushrut S.;Kolachalama, Vijaya B.",Boston University;Brigham and Women's Hospital;Boston Medical Center;VA Boston Healthcare System,https://openalex.org/W1987380823;https://openalex.org/W2081385953;https://openalex.org/W2090443364;https://openalex.org/W2097432501;https://openalex.org/W2112481616;https://openalex.org/W2148983669;https://openalex.org/W2160134719;https://openalex.org/W2163853417;https://openalex.org/W2165019590;https://openalex.org/W2613326680;https://openalex.org/W2655689996;https://openalex.org/W2783839600;https://openalex.org/W2794885170;https://openalex.org/W2889976627;https://openalex.org/W2899995215;https://openalex.org/W2905483812;https://openalex.org/W2905810301;https://openalex.org/W2919089713;https://openalex.org/W2952003460;https://openalex.org/W2952527443;https://openalex.org/W2964696298;https://openalex.org/W2969528126;https://openalex.org/W2971487518;https://openalex.org/W2972214324;https://openalex.org/W3014372210;https://openalex.org/W3015113267;https://openalex.org/W3080446999;https://openalex.org/W3087585143,Machine learning;Artificial intelligence;Computer science;Specialty;Medicine;Medical physics;Pathology,Artificial Intelligence in Healthcare and Education;AI in cancer detection;Artificial Intelligence in Healthcare
-OPENALEX,https://openalex.org/W4364365712,https://doi.org/10.1002/cai2.68,https://pubmed.ncbi.nlm.nih.gov/38089405,A bibliometric analysis of worldwide cancer research using machine learning methods,CANCER INNOVATION,CANCER INNOVATION,2023,review,en,South China Normal University,"Abstract With the progress and development of computer technology, applying machine learning methods to cancer research has become an important research field. To analyze the most recent research status and trends, main research topics, topic evolutions, research collaborations, and potential directions of this research field, this study conducts a bibliometric analysis on 6206 research articles worldwide collected from PubMed between 2011 and 2021 concerning cancer research using machine learning methods. Python is used as a tool for bibliometric analysis, Gephi is used for social network analysis, and the Latent Dirichlet Allocation model is used for topic modeling. The trend analysis of articles not only reflects the innovative research at the intersection of machine learning and cancer but also demonstrates its vigorous development and increasing impacts. In terms of journals, Nature Communications is the most influential journal and Scientific Reports is the most prolific one. The United States and Harvard University have contributed the most to cancer research using machine learning methods. As for the research topic, “Support Vector Machine,” “classification,” and “deep learning” have been the core focuses of the research field. Findings are helpful for scholars and related practitioners to better understand the development status and trends of cancer research using machine learning methods, as well as to have a deeper understanding of research hotspots.",2,3,219,232,"Lin, 2023, CANCER INNOVATION",19,"Lin, Lianghong;Liang, Likeng;Wang, Maojie;Huang, Runyue;Gong, Mengchun;Song, Guangjun;Hao, Tianyong","Lin, Lianghong;Liang, Likeng;Wang, Maojie;Huang, Runyue;Gong, Mengchun;Song, Guangjun;Hao, Tianyong",South China Normal University;Guangzhou University of Chinese Medicine;Guangdong Provincial Hospital of Traditional Chinese Medicine;Southern Medical University;San’an Optoelectronics (China),https://openalex.org/W1548482530;https://openalex.org/W1588989507;https://openalex.org/W1992492534;https://openalex.org/W2012162805;https://openalex.org/W2027641169;https://openalex.org/W2059515884;https://openalex.org/W2076811409;https://openalex.org/W2094053777;https://openalex.org/W2121197635;https://openalex.org/W2167482691;https://openalex.org/W2525784261;https://openalex.org/W2588022491;https://openalex.org/W2593573916;https://openalex.org/W2593949166;https://openalex.org/W2664267452;https://openalex.org/W2750098299;https://openalex.org/W2790313915;https://openalex.org/W2798286858;https://openalex.org/W2800670487;https://openalex.org/W2887382745;https://openalex.org/W2888732444;https://openalex.org/W2937265313;https://openalex.org/W2945395591;https://openalex.org/W2962686197;https://openalex.org/W2968176395;https://openalex.org/W3003683721;https://openalex.org/W3014848624;https://openalex.org/W3020635402;https://openalex.org/W3025370095;https://openalex.org/W3029684717;https://openalex.org/W3034410199;https://openalex.org/W3037389001;https://openalex.org/W3124449840;https://openalex.org/W3126487024;https://openalex.org/W3133796486;https://openalex.org/W3136098559;https://openalex.org/W3168716724;https://openalex.org/W3170170443;https://openalex.org/W3208563410;https://openalex.org/W4225315041;https://openalex.org/W4364365712,Latent Dirichlet allocation;Artificial intelligence;Computer science;Topic model;Field (mathematics);Data science;Bibliometrics;Machine learning;Library science;Mathematics,Radiomics and Machine Learning in Medical Imaging;AI in cancer detection;Artificial Intelligence in Healthcare and Education
-OPENALEX,https://openalex.org/W4366588036,https://doi.org/10.3390/cleantechnol5020026,,Machine Learning Applications in Renewable Energy (MLARE) Research: A Publication Trend and Bibliometric Analysis Study (2012–2021),CLEAN TECHNOLOGIES,CLEAN TECHNOLOGIES,2023,article,en,İstanbul Gelişim Üniversitesi,"This study examines the research climate on machine learning applications in renewable energy (MLARE). Therefore, the publication trends (PT) and bibliometric analysis (BA) on MLARE research published and indexed in the Elsevier Scopus database between 2012 and 2021 were examined. The PT was adopted to deduce the major stakeholders, top-cited publications, and funding organizations on MLARE, whereas BA elucidated critical insights into the research landscape, scientific developments, and technological growth. The PT revealed 1218 published documents comprising 46.9% articles, 39.7% conference papers, and 6.0% reviews on the topic. Subject area analysis revealed MLARE research spans the areas of science, technology, engineering, and mathematics among others, which indicates it is a broad, multidisciplinary, and impactful research topic. The most prolific researcher, affiliations, country, and funder are Ravinesh C. Deo, National Renewable Energy Laboratory, United States, and the National Natural Science Foundation of China, respectively. The most prominent journals on the top are Applied Energy and Energies, which indicates that journal reputation and open access are critical considerations for the author’s choice of publication outlet. The high productivity of the major stakeholders in MLARE is due to collaborations and research funding support. The keyword co-occurrence analysis identified four (4) clusters or thematic areas on MLARE, which broadly describe the systems, technologies, tools/technologies, and socio-technical dynamics of MLARE research. Overall, the study showed that ML is critical to the prediction, operation, and optimization of renewable energy technologies (RET) along with the design and development of RE-related materials.",5,2,497,517,"Ajibade, 2023, CLEAN TECHNOLOGIES",40,"Ajibade, Samuel-Soma M.;Bekun, Festus Víctor;Adedoyin, Festus Fatai;Gyamfi, Bright Akwasi;Adediran, Anthonia Oluwatosin","Ajibade, Samuel-Soma M.;Bekun, Festus Víctor;Adedoyin, Festus Fatai;Gyamfi, Bright Akwasi;Adediran, Anthonia Oluwatosin","Istanbul Commerce University;İstanbul Gelişim Üniversitesi;Lebanese American University;Bournemouth University;Sir Padampat Singhania University;The Federal Polytechnic, Ado-Ekiti",https://openalex.org/W1495476169;https://openalex.org/W1977177161;https://openalex.org/W1979480754;https://openalex.org/W1982617890;https://openalex.org/W2001518080;https://openalex.org/W2034901725;https://openalex.org/W2045940255;https://openalex.org/W2093822345;https://openalex.org/W2106488040;https://openalex.org/W2142332398;https://openalex.org/W2160808585;https://openalex.org/W2199008649;https://openalex.org/W2289343141;https://openalex.org/W2315977129;https://openalex.org/W2336998050;https://openalex.org/W2525448601;https://openalex.org/W2563954806;https://openalex.org/W2580254850;https://openalex.org/W2742692373;https://openalex.org/W2763128055;https://openalex.org/W2799581641;https://openalex.org/W2799753020;https://openalex.org/W2810753849;https://openalex.org/W2821843609;https://openalex.org/W2891810618;https://openalex.org/W2891859208;https://openalex.org/W2893898383;https://openalex.org/W2898544197;https://openalex.org/W2903560887;https://openalex.org/W2905035404;https://openalex.org/W2909202499;https://openalex.org/W2911256795;https://openalex.org/W2915043045;https://openalex.org/W2920988109;https://openalex.org/W2922019030;https://openalex.org/W2943162653;https://openalex.org/W2953936648;https://openalex.org/W2960560113;https://openalex.org/W2987201163;https://openalex.org/W3001937224;https://openalex.org/W3019827462;https://openalex.org/W3023538869;https://openalex.org/W3081125651;https://openalex.org/W3081707209;https://openalex.org/W3091939691;https://openalex.org/W3092179490;https://openalex.org/W3093695208;https://openalex.org/W3094843299;https://openalex.org/W3101604855;https://openalex.org/W3123725380;https://openalex.org/W3124856069;https://openalex.org/W3135734453;https://openalex.org/W3191690765;https://openalex.org/W3194540065;https://openalex.org/W4200575230;https://openalex.org/W4250542689;https://openalex.org/W4281388464;https://openalex.org/W4287510462;https://openalex.org/W4292072448;https://openalex.org/W4294892280;https://openalex.org/W4297478379;https://openalex.org/W4400058028;https://openalex.org/W6638465966;https://openalex.org/W6650557320;https://openalex.org/W6776366498;https://openalex.org/W6804853111;https://openalex.org/W6843664783,Scopus;Bibliometrics;Library science;Renewable energy;Multidisciplinary approach;Thematic analysis;Citation;Subject (documents);Political science;Reputation;Data science;Regional science;Computer science;Engineering;Social science;Sociology;Qualitative research;MEDLINE,Solar Radiation and Photovoltaics;Energy and Environment Impacts;Air Quality Monitoring and Forecasting
-OPENALEX,https://openalex.org/W4387675817,https://doi.org/10.1016/j.rineng.2023.101518,,A bibliometric analysis of the application of machine learning methods in the petroleum industry,RESULTS IN ENGINEERING,RESULTS IN ENGINEERING,2023,article,en,University of Tabriz,"With the emerge of Artificial Intelligence and Machin learning systems, the petroleum industry has witnessed a significant progress in its different disciplines to optimize decision making, time and costs. Despite the widespread application of using machine learning methods in the petroleum industry, a little attention has been devoted to build a framework to bring the main currents and researches on the topic. The current research is aimed at covering this gap through further analysis of complementary sources of bibliographic information, assessing 3163 bibliometric studies published in Web of Science (WOS) database. The descriptive statistics show that this field has an exponential growth in the last five years, such that more than 62 % of identified articles were published between 2018 and 2022. CHINA, IRAN and US are the pioneer countries with the highest number of publications on the application of artificial intelligence and machine learning in the upstream sector of the petroleum industry. The most influential journal in this field is ‘JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING’ (with 416 articles) (the current journal title is Geoenergy Science and Engineering) and the most productive author is SALAHELDIN ELKATATNY (with 54 articles) in WOS database. Also, the co-occurrence word analysis show that most of the artificial intelligence and machine learning applications in the upstream sector of the petroleum industry was the prediction and optimization in the field of ‘porosity’, ‘well logs’ and ‘permeability’. This paper contributes to the body of knowledge by providing a comprehensive overview of the application of artificial intelligence and machine learning in the upstream petroleum industry.",20,,101518,101518,"Sadeqi-Arani, 2023, RESULTS IN ENGINEERING",21,"Sadeqi-Arani, Zahra;Kadkhodaie, Ali","Sadeqi-Arani, Zahra;Kadkhodaie, Ali",University of Kashan;University of Tabriz,https://openalex.org/W1537982310;https://openalex.org/W1968313366;https://openalex.org/W1968716244;https://openalex.org/W1983797158;https://openalex.org/W1994743230;https://openalex.org/W2017680781;https://openalex.org/W2021245834;https://openalex.org/W2031226906;https://openalex.org/W2038929774;https://openalex.org/W2039027543;https://openalex.org/W2048243493;https://openalex.org/W2050380864;https://openalex.org/W2054837066;https://openalex.org/W2059298705;https://openalex.org/W2078792643;https://openalex.org/W2087570443;https://openalex.org/W2101710913;https://openalex.org/W2128601505;https://openalex.org/W2138366127;https://openalex.org/W2150220236;https://openalex.org/W2152840078;https://openalex.org/W2159496790;https://openalex.org/W2174848150;https://openalex.org/W2182559116;https://openalex.org/W2283043143;https://openalex.org/W2461512211;https://openalex.org/W2616526449;https://openalex.org/W2914046321;https://openalex.org/W2953737906;https://openalex.org/W2990803190;https://openalex.org/W3033703056;https://openalex.org/W3081451599;https://openalex.org/W3116936901;https://openalex.org/W3123421385;https://openalex.org/W3123497531;https://openalex.org/W3125707221;https://openalex.org/W3131830380;https://openalex.org/W3160856016;https://openalex.org/W3161066282;https://openalex.org/W3165988956;https://openalex.org/W3168695438;https://openalex.org/W3187000892;https://openalex.org/W3195680644;https://openalex.org/W3196322538;https://openalex.org/W4213189685;https://openalex.org/W4221125746;https://openalex.org/W4224248705;https://openalex.org/W4224293566;https://openalex.org/W4281661043;https://openalex.org/W4283074959;https://openalex.org/W4283124298;https://openalex.org/W4316654911;https://openalex.org/W4318939167;https://openalex.org/W4360998646;https://openalex.org/W4385383343;https://openalex.org/W4387073182;https://openalex.org/W6632339523;https://openalex.org/W6679259992;https://openalex.org/W6686433439;https://openalex.org/W6799334232;https://openalex.org/W6850884974;https://openalex.org/W6856736576,Artificial intelligence;Field (mathematics);Computer science;Petroleum industry;Petroleum;Upstream (networking);Web of science;Machine learning;Data science;Engineering;Political science;Mathematics;Geology,Reservoir Engineering and Simulation Methods;Drilling and Well Engineering;Atmospheric and Environmental Gas Dynamics
-OPENALEX,https://openalex.org/W3045713571,https://doi.org/10.1177/1756284820934594,https://pubmed.ncbi.nlm.nih.gov/32782478,"A bibliometric analysis of 23,492 publications on rectal cancer by machine learning: basic medical research is needed",THERAPEUTIC ADVANCES IN GASTROENTEROLOGY,THERAPEUTIC ADVANCES IN GASTROENTEROLOGY,2020,article,en,Central South University,"BACKGROUND AND AIMS: The aim of this study was to analyse the landscape of publications on rectal cancer (RC) over the past 25 years by machine learning and semantic analysis. METHODS: Publications indexed in PubMed under the Medical Subject Headings (MeSH) term 'Rectal Neoplasms' from 1994 to 2018 were downloaded in September 2019. R and Python were used to extract publication date, MeSH terms and abstract from the metadata of each publication for bibliometric assessment. Latent Dirichlet allocation was applied to analyse the text from the articles' abstracts to identify more specific research topics. Louvain algorithm was used to establish a topic network resulting in identifying the relationship between the topics. RESULTS: A total of 23,492 papers published were identified and analysed in this study. The changes of research focus were analysed by the changing of MeSH terms. Studied contents extracted from the publications were divided into five areas, including surgical intervention, radiotherapy and chemotherapy intervention, clinical case management, epidemiology and cancer risk as well as prognosis studies. CONCLUSIONS: The number of publications indexed on RC has expanded rapidly over the past 25 years. Studies on RC have mainly focused on five areas. However, studies on basic research, postoperative quality of life and cost-effective research were relatively lacking. It is predicted that basic research, inflammation and some other research fields might become the potential hotspots in the future.",13,,1756284820934594,1756284820934594,"Wang, 2020, THERAPEUTIC ADVANCES IN GASTROENTEROLOGY",35,"Wang, Kangtao;Feng, Chenzhe;Li, Ming;Pei, Qian;Li, Yuqiang;Zhu, Hong;Song, Xiangping;Pei, Haiping;Tan, Fengbo","Wang, Kangtao;Feng, Chenzhe;Li, Ming;Pei, Qian;Li, Yuqiang;Zhu, Hong;Song, Xiangping;Pei, Haiping;Tan, Fengbo",Central South University;Xiangya Hospital Central South University;Chinese Academy of Medical Sciences & Peking Union Medical College;Peking Union Medical College Hospital;Universität Hamburg;University Medical Center Hamburg-Eppendorf,https://openalex.org/W141482010;https://openalex.org/W1218787212;https://openalex.org/W1650984530;https://openalex.org/W1849119602;https://openalex.org/W1880262756;https://openalex.org/W1971250649;https://openalex.org/W1979044015;https://openalex.org/W2001932471;https://openalex.org/W2009781085;https://openalex.org/W2015567271;https://openalex.org/W2028695285;https://openalex.org/W2060363577;https://openalex.org/W2071880161;https://openalex.org/W2089643849;https://openalex.org/W2111120558;https://openalex.org/W2148323067;https://openalex.org/W2155492044;https://openalex.org/W2160042758;https://openalex.org/W2165323775;https://openalex.org/W2165579757;https://openalex.org/W2238912933;https://openalex.org/W2336985479;https://openalex.org/W2395569962;https://openalex.org/W2512102151;https://openalex.org/W2552603742;https://openalex.org/W2564110073;https://openalex.org/W2600614137;https://openalex.org/W2612183966;https://openalex.org/W2706644294;https://openalex.org/W2781654669;https://openalex.org/W2792712441;https://openalex.org/W2794587414;https://openalex.org/W2810322781;https://openalex.org/W2921846932;https://openalex.org/W2927453907;https://openalex.org/W2948897437;https://openalex.org/W2963060404;https://openalex.org/W2979579431;https://openalex.org/W2999417355;https://openalex.org/W3000607909;https://openalex.org/W3012305074;https://openalex.org/W4244315786,Latent Dirichlet allocation;Medicine;Bibliometrics;Metadata;Colorectal cancer;Medical research;Subject (documents);Topic model;Computer science;Cancer;Library science;Information retrieval;Pathology;Internal medicine;World Wide Web,scientometrics and bibliometrics research;Colorectal Cancer Surgical Treatments;Meta-analysis and systematic reviews
-OPENALEX,https://openalex.org/W4391099316,https://doi.org/10.3390/info15010065,,Machine Learning and Blockchain: A Bibliometric Study on Security and Privacy,INFORMATION,INFORMATION,2024,article,en,Universidad Señor de Sipán,"Machine learning and blockchain technology are fast-developing fields with implications for multiple sectors. Both have attracted a lot of interest and show promise in security, IoT, 5G/6G networks, artificial intelligence, and more. However, challenges remain in the scientific literature, so the aim is to investigate research trends around the use of machine learning in blockchain. A bibliometric analysis is proposed based on the PRISMA-2020 parameters in the Scopus and Web of Science databases. An objective analysis of the most productive and highly cited authors, journals, and countries is conducted. Additionally, a thorough analysis of keyword validity and importance is performed, along with a review of the most significant topics by year of publication. Co-occurrence networks are generated to identify the most crucial research clusters in the field. Finally, a research agenda is proposed to highlight future topics with great potential. This study reveals a growing interest in machine learning and blockchain. Topics are evolving towards IoT and smart contracts. Emerging keywords include cloud computing, intrusion detection, and distributed learning. The United States, Australia, and India are leading the research. The research proposes an agenda to explore new applications and foster collaboration between researchers and countries in this interdisciplinary field.",15,1,65,65,"Valencia-Arías, 2024, INFORMATION",17,"Valencia-Arías, Alejandro;González-Ruíz, Juan David;Flores, Lilian Verde;Vega-Mori, Luis;Rodríguez-Correa, Paula Andrea;Santos, Gustavo Sánchez","Valencia-Arías, Alejandro;González-Ruíz, Juan David;Flores, Lilian Verde;Vega-Mori, Luis;Rodríguez-Correa, Paula Andrea;Santos, Gustavo Sánchez",Universidad Señor de Sipán;Universidad Nacional de Colombia;Universidad Ricardo Palma;Institución Universitaria Escolme,https://openalex.org/W2163539724;https://openalex.org/W2884850778;https://openalex.org/W2899063614;https://openalex.org/W2899559633;https://openalex.org/W2907683311;https://openalex.org/W2939989211;https://openalex.org/W2944852501;https://openalex.org/W2951694401;https://openalex.org/W2962621836;https://openalex.org/W2974429275;https://openalex.org/W2993463367;https://openalex.org/W3009735711;https://openalex.org/W3016342701;https://openalex.org/W3026150618;https://openalex.org/W3031802354;https://openalex.org/W3087214020;https://openalex.org/W3088273379;https://openalex.org/W3091851474;https://openalex.org/W3097704033;https://openalex.org/W3108615370;https://openalex.org/W3111635317;https://openalex.org/W3118615836;https://openalex.org/W3126615894;https://openalex.org/W3131823290;https://openalex.org/W3135725526;https://openalex.org/W3157876841;https://openalex.org/W3157894430;https://openalex.org/W3167619068;https://openalex.org/W3171918319;https://openalex.org/W3172817039;https://openalex.org/W3182418041;https://openalex.org/W3191116886;https://openalex.org/W3192184597;https://openalex.org/W3192414357;https://openalex.org/W3193560119;https://openalex.org/W3194026771;https://openalex.org/W3195473500;https://openalex.org/W3195539663;https://openalex.org/W3201827372;https://openalex.org/W3215181416;https://openalex.org/W3215514448;https://openalex.org/W4200152289;https://openalex.org/W4207038251;https://openalex.org/W4210698639;https://openalex.org/W4220835468;https://openalex.org/W4220993330;https://openalex.org/W4224230842;https://openalex.org/W4229008987;https://openalex.org/W4282032734;https://openalex.org/W4296250495;https://openalex.org/W4313307346;https://openalex.org/W4313583479;https://openalex.org/W4317038528;https://openalex.org/W4324053439;https://openalex.org/W6753458459;https://openalex.org/W6794788827;https://openalex.org/W6800042234,Blockchain;Computer science;Scopus;Field (mathematics);Data science;Cloud computing;Web of science;Internet of Things;Artificial intelligence;Computer security;Political science,Blockchain Technology Applications and Security;Cybercrime and Law Enforcement Studies
-OPENALEX,https://openalex.org/W4399434684,https://doi.org/10.2174/1570159x22999240531160344,https://pubmed.ncbi.nlm.nih.gov/38847379,Brain Disorder Detection and Diagnosis using Machine Learning and DeepLearning – A Bibliometric Analysis,CURRENT NEUROPHARMACOLOGY,CURRENT NEUROPHARMACOLOGY,2024,review,en,Vellore Institute of Technology University,"BACKGROUND AND OBJECTIVE: Brain disorders are one of the major global mortality issues, and their early detection is crucial for healing. Machine learning, specifically deep learning, is a technology that is increasingly being used to detect and diagnose brain disorders. Our objective is to provide a quantitative bibliometric analysis of the field to inform researchers about trends that can inform their Research directions in the future. METHODS: We carried out a bibliometric analysis to create an overview of brain disorder detection and diagnosis using machine learning and deep learning. Our bibliometric analysis includes 1550 articles gathered from the Scopus database on automated brain disorder detection and diagnosis using machine learning and deep learning published from 2015 to May 2023. A thorough bibliometric análisis is carried out with the help of Biblioshiny and the VOSviewer platform. Citation analysis and various measures of collaboration are analyzed in the study. RESULTS: According to a study, maximum research is reported in 2022, with a consistent rise from preceding years. The majority of the authors referenced have concentrated on multiclass classification and innovative convolutional neural network models that are effective in this field. A keyword analysis revealed that among the several brain disorder types, Alzheimer's, autism, and Parkinson's disease had received the greatest attention. In terms of both authors and institutes, the USA, China, and India are among the most collaborating countries. We built a future research agenda based on our findings to help progress research on machine learning and deep learning for brain disorder detection and diagnosis. CONCLUSION: In summary, our quantitative bibliometric analysis provides useful insights about trends in the field and points them to potential directions in applying machine learning and deep learning for brain disorder detection and diagnosis..",22,13,2191,2216,"Chaki, 2024, CURRENT NEUROPHARMACOLOGY",16,"Chaki, Jyotismita;Deshpande, Gopikrishna","Chaki, Jyotismita;Deshpande, Gopikrishna",Vellore Institute of Technology University;Advanced Imaging Research (United States);Indian Institute of Science Bangalore;Indian Institute of Technology Hyderabad;National Institute of Mental Health and Neurosciences;Auburn University;Capital Normal University,https://openalex.org/W26772505;https://openalex.org/W47135330;https://openalex.org/W1457602677;https://openalex.org/W1970262118;https://openalex.org/W1983797158;https://openalex.org/W2075210662;https://openalex.org/W2120109270;https://openalex.org/W2260678261;https://openalex.org/W2296627753;https://openalex.org/W2300209210;https://openalex.org/W2310177520;https://openalex.org/W2569531558;https://openalex.org/W2618530766;https://openalex.org/W2706159764;https://openalex.org/W2752558629;https://openalex.org/W2762517398;https://openalex.org/W2773214121;https://openalex.org/W2773893004;https://openalex.org/W2792703138;https://openalex.org/W2805418444;https://openalex.org/W2805494981;https://openalex.org/W2898969413;https://openalex.org/W2905104432;https://openalex.org/W2905641806;https://openalex.org/W2906155095;https://openalex.org/W2909533222;https://openalex.org/W2916013340;https://openalex.org/W2919115771;https://openalex.org/W2939845202;https://openalex.org/W2947607756;https://openalex.org/W2969955811;https://openalex.org/W2972471455;https://openalex.org/W2985355520;https://openalex.org/W3003515831;https://openalex.org/W3022315685;https://openalex.org/W3023210783;https://openalex.org/W3033801901;https://openalex.org/W3048339221;https://openalex.org/W3082595202;https://openalex.org/W3088547738;https://openalex.org/W3092024150;https://openalex.org/W3092527447;https://openalex.org/W3095114157;https://openalex.org/W3112489665;https://openalex.org/W3127908559;https://openalex.org/W3157670290;https://openalex.org/W3160856016;https://openalex.org/W3169635392;https://openalex.org/W3183308789;https://openalex.org/W3183848791;https://openalex.org/W4205294987;https://openalex.org/W4205448186;https://openalex.org/W4205941964;https://openalex.org/W4220675637;https://openalex.org/W4226041160;https://openalex.org/W4234552385;https://openalex.org/W4239252183;https://openalex.org/W4254530907;https://openalex.org/W4293662955;https://openalex.org/W4311621367;https://openalex.org/W4318678031;https://openalex.org/W4321459679;https://openalex.org/W4323349538;https://openalex.org/W4327978535;https://openalex.org/W4382176573,Artificial intelligence;Deep learning;Machine learning;Computer science;Data science,Brain Tumor Detection and Classification;COVID-19 diagnosis using AI;Artificial Intelligence in Healthcare
-OPENALEX,https://openalex.org/W4391160762,https://doi.org/10.1007/s10994-023-06467-x,,Hybrid approaches to optimization and machine learning methods: a systematic literature review,MACHINE LEARNING,MACHINE LEARNING,2024,article,en,Polytechnic Institute of Bragança,"Abstract Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.",113,7,4055,4097,"Azevedo, 2024, MACHINE LEARNING",236,"Azevedo, Beatriz Flamia;Rocha, Ana Maria A. C.;Pereira, Ana I.","Azevedo, Beatriz Flamia;Rocha, Ana Maria A. C.;Pereira, Ana I.",Polytechnic Institute of Bragança;Research Centre in Digitalization and Intelligent Robotics;University of Minho,https://openalex.org/W67623166;https://openalex.org/W334120727;https://openalex.org/W368469426;https://openalex.org/W632575780;https://openalex.org/W1494581921;https://openalex.org/W1550411348;https://openalex.org/W1587157779;https://openalex.org/W1595159159;https://openalex.org/W1639032689;https://openalex.org/W1659842140;https://openalex.org/W1723619723;https://openalex.org/W1748133846;https://openalex.org/W1977042441;https://openalex.org/W2008499862;https://openalex.org/W2010334716;https://openalex.org/W2018450565;https://openalex.org/W2024060531;https://openalex.org/W2073616444;https://openalex.org/W2084792706;https://openalex.org/W2097571405;https://openalex.org/W2113741278;https://openalex.org/W2126554879;https://openalex.org/W2140112578;https://openalex.org/W2140190241;https://openalex.org/W2148423395;https://openalex.org/W2151554678;https://openalex.org/W2151653296;https://openalex.org/W2152195021;https://openalex.org/W2154241802;https://openalex.org/W2154943049;https://openalex.org/W2157104063;https://openalex.org/W2165489473;https://openalex.org/W2201487387;https://openalex.org/W2277678953;https://openalex.org/W2287814884;https://openalex.org/W2301363727;https://openalex.org/W2317440965;https://openalex.org/W2418499371;https://openalex.org/W2527766424;https://openalex.org/W2613854265;https://openalex.org/W2619205994;https://openalex.org/W2735074495;https://openalex.org/W2746471721;https://openalex.org/W2755950973;https://openalex.org/W2783445774;https://openalex.org/W2789574636;https://openalex.org/W2791030877;https://openalex.org/W2800658400;https://openalex.org/W2804299858;https://openalex.org/W2808717296;https://openalex.org/W2885026880;https://openalex.org/W2885938377;https://openalex.org/W2889949445;https://openalex.org/W2892074118;https://openalex.org/W2901478555;https://openalex.org/W2904262369;https://openalex.org/W2913441098;https://openalex.org/W2913511179;https://openalex.org/W2913923809;https://openalex.org/W2915062141;https://openalex.org/W2931821931;https://openalex.org/W2938187055;https://openalex.org/W2941951094;https://openalex.org/W2945366039;https://openalex.org/W2950652464;https://openalex.org/W2953670995;https://openalex.org/W2954243979;https://openalex.org/W2955282193;https://openalex.org/W2957480063;https://openalex.org/W2958120908;https://openalex.org/W2958591219;https://openalex.org/W2959384841;https://openalex.org/W2966794404;https://openalex.org/W2967148691;https://openalex.org/W2967541662;https://openalex.org/W2968337214;https://openalex.org/W2970312737;https://openalex.org/W2971754179;https://openalex.org/W2971935510;https://openalex.org/W2981630869;https://openalex.org/W2983960985;https://openalex.org/W2991117816;https://openalex.org/W2991842529;https://openalex.org/W3006321375;https://openalex.org/W3006500846;https://openalex.org/W3007031763;https://openalex.org/W3007555823;https://openalex.org/W3007924065;https://openalex.org/W3008869496;https://openalex.org/W3009083808;https://openalex.org/W3015376070;https://openalex.org/W3015712154;https://openalex.org/W3017381157;https://openalex.org/W3021213627;https://openalex.org/W3021324494;https://openalex.org/W3021611044;https://openalex.org/W3023540311;https://openalex.org/W3030453392;https://openalex.org/W3036663969;https://openalex.org/W3038588646;https://openalex.org/W3041931127;https://openalex.org/W3043954412;https://openalex.org/W3046364220;https://openalex.org/W3049194088;https://openalex.org/W3089669567;https://openalex.org/W3091866490;https://openalex.org/W3092376343;https://openalex.org/W3093015498;https://openalex.org/W3093846425;https://openalex.org/W3094370609;https://openalex.org/W3094415104;https://openalex.org/W3107851601;https://openalex.org/W3109065714;https://openalex.org/W3112700758;https://openalex.org/W3114197462;https://openalex.org/W3117323734;https://openalex.org/W3117693614;https://openalex.org/W3118057467;https://openalex.org/W3118408813;https://openalex.org/W3119896356;https://openalex.org/W3120225493;https://openalex.org/W3120254517;https://openalex.org/W3121507283;https://openalex.org/W3122939402;https://openalex.org/W3126831744;https://openalex.org/W3127236931;https://openalex.org/W3131478027;https://openalex.org/W3137708129;https://openalex.org/W3144543375;https://openalex.org/W3149452572;https://openalex.org/W3151861686;https://openalex.org/W3154169920;https://openalex.org/W3155052204;https://openalex.org/W3155218548;https://openalex.org/W3156827694;https://openalex.org/W3156852085;https://openalex.org/W3159068915;https://openalex.org/W3180086944;https://openalex.org/W3181846079;https://openalex.org/W3186072580;https://openalex.org/W3186521061;https://openalex.org/W3197886029;https://openalex.org/W3200955720;https://openalex.org/W3201722279;https://openalex.org/W3203676317;https://openalex.org/W3210186723;https://openalex.org/W3210212385;https://openalex.org/W3214709316;https://openalex.org/W4200251857;https://openalex.org/W4200411312;https://openalex.org/W4205129187;https://openalex.org/W4205431832;https://openalex.org/W4206289809;https://openalex.org/W4211189042;https://openalex.org/W4230109820;https://openalex.org/W4232545478;https://openalex.org/W4235174477;https://openalex.org/W4236362309;https://openalex.org/W4239972939;https://openalex.org/W4245306669;https://openalex.org/W4250042253;https://openalex.org/W4250589301;https://openalex.org/W4253572765;https://openalex.org/W4283203948;https://openalex.org/W4289601225;https://openalex.org/W4293775970;https://openalex.org/W4300995828;https://openalex.org/W4308933352;https://openalex.org/W4311198169;https://openalex.org/W4366779255;https://openalex.org/W4367056742;https://openalex.org/W4380320036;https://openalex.org/W4380792490;https://openalex.org/W4389474287;https://openalex.org/W4392204353;https://openalex.org/W6629510986;https://openalex.org/W6633218642;https://openalex.org/W6636726260;https://openalex.org/W6680704940;https://openalex.org/W6989336298,Computer science;Artificial intelligence;Machine learning,Metaheuristic Optimization Algorithms Research;Vehicle Routing Optimization Methods;Advanced Multi-Objective Optimization Algorithms
-OPENALEX,https://openalex.org/W3117942735,https://doi.org/10.1016/j.autcon.2020.103490,,Risk assessment and management of excavation system based on fuzzy set theory and machine learning methods,AUTOMATION IN CONSTRUCTION,AUTOMATION IN CONSTRUCTION,2020,article,en,Shantou University,,122,,103490,103490,"Lin, 2020, AUTOMATION IN CONSTRUCTION",256,"Lin, Song-Shun;Shen, Shui‐Long;Zhou, Annan;Xu, Ye‐Shuang","Lin, Song-Shun;Shen, Shui‐Long;Zhou, Annan;Xu, Ye‐Shuang",Shanghai Jiao Tong University;Shantou University;RMIT University,https://openalex.org/W13108582;https://openalex.org/W1498436455;https://openalex.org/W1528620860;https://openalex.org/W1563088657;https://openalex.org/W1668569279;https://openalex.org/W1968275355;https://openalex.org/W1975481270;https://openalex.org/W1978894336;https://openalex.org/W1980452149;https://openalex.org/W1980564456;https://openalex.org/W1980841949;https://openalex.org/W1980973394;https://openalex.org/W1983210776;https://openalex.org/W2011702380;https://openalex.org/W2013932252;https://openalex.org/W2016179164;https://openalex.org/W2026410682;https://openalex.org/W2031703450;https://openalex.org/W2035025114;https://openalex.org/W2042102899;https://openalex.org/W2067627945;https://openalex.org/W2081849111;https://openalex.org/W2094259335;https://openalex.org/W2097879961;https://openalex.org/W2100548091;https://openalex.org/W2116321937;https://openalex.org/W2119821739;https://openalex.org/W2122145224;https://openalex.org/W2123754130;https://openalex.org/W2125068387;https://openalex.org/W2130016085;https://openalex.org/W2147228094;https://openalex.org/W2150220236;https://openalex.org/W2153635508;https://openalex.org/W2178939760;https://openalex.org/W2292359941;https://openalex.org/W2318485605;https://openalex.org/W2360069731;https://openalex.org/W2365060897;https://openalex.org/W2393686830;https://openalex.org/W2477128123;https://openalex.org/W2506768821;https://openalex.org/W2512479836;https://openalex.org/W2581855983;https://openalex.org/W2582146055;https://openalex.org/W2731637685;https://openalex.org/W2739589480;https://openalex.org/W2754789423;https://openalex.org/W2755885017;https://openalex.org/W2761171919;https://openalex.org/W2768163011;https://openalex.org/W2789555074;https://openalex.org/W2790304257;https://openalex.org/W2794572842;https://openalex.org/W2796224854;https://openalex.org/W2798149936;https://openalex.org/W2804432451;https://openalex.org/W2884762388;https://openalex.org/W2892755442;https://openalex.org/W2896286460;https://openalex.org/W2900292137;https://openalex.org/W2905944349;https://openalex.org/W2911964244;https://openalex.org/W2921922207;https://openalex.org/W2951787506;https://openalex.org/W2954289684;https://openalex.org/W2954397328;https://openalex.org/W2954621195;https://openalex.org/W2954869684;https://openalex.org/W2959787411;https://openalex.org/W2963929932;https://openalex.org/W2965614847;https://openalex.org/W2970263100;https://openalex.org/W2991653145;https://openalex.org/W2993667365;https://openalex.org/W2996045208;https://openalex.org/W2996806689;https://openalex.org/W3000372520;https://openalex.org/W3004047739;https://openalex.org/W3004765871;https://openalex.org/W3006637824;https://openalex.org/W3007454630;https://openalex.org/W3008829399;https://openalex.org/W3014784156;https://openalex.org/W3014913272;https://openalex.org/W3081821516;https://openalex.org/W3087472426;https://openalex.org/W3120421331;https://openalex.org/W3213390689;https://openalex.org/W4239510810;https://openalex.org/W6659258946;https://openalex.org/W6697397043;https://openalex.org/W6706664090;https://openalex.org/W6750624659;https://openalex.org/W6771592987;https://openalex.org/W6773626131,Excavation;Automatic summarization;Warning system;Engineering;Risk management;Risk assessment;Fuzzy set;Fuzzy logic;Computer science;Construction engineering;Risk analysis (engineering);Artificial intelligence;Computer security,Advanced Decision-Making Techniques;Evaluation and Optimization Models;Evaluation Methods in Various Fields
-OPENALEX,https://openalex.org/W3035953096,https://doi.org/10.1080/0194262x.2020.1776193,,Bibliometric Survey of Quantum Machine Learning,SCIENCE & TECHNOLOGY LIBRARIES,SCIENCE & TECHNOLOGY LIBRARIES,2020,article,en,Symbiosis International University,"Quantum Machine Learning (QML) is one of the core research fields in the larger paradigm of Quantum Computing (also known alternatively as Quantum Information). In recent years, researchers have taken deep interest in QML, given the potential time and cost advantages that solutions to real-life problems using QML algorithms provide, in comparison to their classical (or digital) machine learning equivalents. This is still a very new and exciting area of research with new algorithms and their uses being developed almost every other day. Deep research interest in this area has picked up only in the past 5–6 years. Given the background, this paper focuses on studying Scopus and Web of Science databases for the past 6 years (2014–2019) to identify various publication trends in the areas of Quantum Machine Learning. The authors have done an in-depth study of the Scopus and Web of Science publication data pertaining to this area and have come up with interesting insights. The survey covers 276 publications in Scopus and 154 publications in Web of Science. From the Scopus database, it is found that there has been a consistent growth in the number of publications in this period. Four research areas, namely, Physics, Astronomy, Computer Science, and Mathematics, have contributed 68.1% of the research publications. The USA leads the top 10 countries with nearly half (49.2%) of the research publications. A total of 148 patents have been published with 94 of these being published in the last four years (2016–2019). This essentially translates to one patent for every two publications. The Web of Science database, though bringing out 154 publications in the period, shows similar trends across the metrics. We have carried out a comparative study of some of the metrics in Scopus and Web of Science databases. Overall the study identifies the top 10 Institutions, authors, and research journals.",39,4,369,382,"Pande, 2020, SCIENCE & TECHNOLOGY LIBRARIES",34,"Pande, Mandaar B.;Mulay, Preeti","Pande, Mandaar B.;Mulay, Preeti",Symbiosis International University,https://openalex.org/W118877790;https://openalex.org/W199424061;https://openalex.org/W1492999010;https://openalex.org/W1568345435;https://openalex.org/W1619888535;https://openalex.org/W1988369744;https://openalex.org/W2006226307;https://openalex.org/W2009562587;https://openalex.org/W2012206667;https://openalex.org/W2040792108;https://openalex.org/W2067763535;https://openalex.org/W2084652510;https://openalex.org/W2103956991;https://openalex.org/W2148132004;https://openalex.org/W2168676717;https://openalex.org/W2257937122;https://openalex.org/W2559394418;https://openalex.org/W2781738013;https://openalex.org/W2788945937;https://openalex.org/W2792946961;https://openalex.org/W2796293949;https://openalex.org/W2798434869;https://openalex.org/W2890984812;https://openalex.org/W2892079374;https://openalex.org/W2963468826;https://openalex.org/W2974549418;https://openalex.org/W2981065735;https://openalex.org/W2982169647;https://openalex.org/W2990961515;https://openalex.org/W2995742898;https://openalex.org/W3011907935;https://openalex.org/W3023478445;https://openalex.org/W3100566623;https://openalex.org/W3100931082;https://openalex.org/W3101479050;https://openalex.org/W3101518480;https://openalex.org/W3111297213,Scopus;Web of science;Computer science;Artificial intelligence;Data science;Mathematics;Library science;Political science;MEDLINE,Quantum Computing Algorithms and Architecture;Quantum Information and Cryptography;Machine Learning in Materials Science
-OPENALEX,https://openalex.org/W4292572075,https://doi.org/10.1007/s13278-022-00916-6,https://pubmed.ncbi.nlm.nih.gov/35971409,Bibliometric analysis of the published literature on machine learning in economics and econometrics,SOCIAL NETWORK ANALYSIS AND MINING,SOCIAL NETWORK ANALYSIS AND MINING,2022,article,en,Dokuz Eylül University,,12,1,109,109,"Akay, 2022, SOCIAL NETWORK ANALYSIS AND MINING",16,"Akay, Ebru Çağlayan;Yılmaz, Naciye Tuba;GACAR, Burcu KOCARIK","Akay, Ebru Çağlayan;Yılmaz, Naciye Tuba;GACAR, Burcu KOCARIK",Marmara University;Dokuz Eylül University,https://openalex.org/W1021000864;https://openalex.org/W1492784227;https://openalex.org/W1691836324;https://openalex.org/W1743190515;https://openalex.org/W1965746216;https://openalex.org/W1984703120;https://openalex.org/W2018881137;https://openalex.org/W2021314860;https://openalex.org/W2025572017;https://openalex.org/W2026048037;https://openalex.org/W2027090774;https://openalex.org/W2047060174;https://openalex.org/W2071894795;https://openalex.org/W2072119404;https://openalex.org/W2090870577;https://openalex.org/W2105201700;https://openalex.org/W2108680868;https://openalex.org/W2114060717;https://openalex.org/W2118373411;https://openalex.org/W2134064007;https://openalex.org/W2141409967;https://openalex.org/W2155419203;https://openalex.org/W2329512751;https://openalex.org/W2344469150;https://openalex.org/W2353267094;https://openalex.org/W2416848540;https://openalex.org/W2512365341;https://openalex.org/W2522448907;https://openalex.org/W2563961554;https://openalex.org/W2584924584;https://openalex.org/W2592084954;https://openalex.org/W2605481522;https://openalex.org/W2610886376;https://openalex.org/W2751861288;https://openalex.org/W2759832051;https://openalex.org/W2765743217;https://openalex.org/W2770958024;https://openalex.org/W2772164149;https://openalex.org/W2786141192;https://openalex.org/W2804346410;https://openalex.org/W2810322781;https://openalex.org/W2885251002;https://openalex.org/W2898057422;https://openalex.org/W2908094560;https://openalex.org/W2921613140;https://openalex.org/W2924708206;https://openalex.org/W2942867818;https://openalex.org/W2950708169;https://openalex.org/W2953527948;https://openalex.org/W2963453445;https://openalex.org/W2964099165;https://openalex.org/W2979610116;https://openalex.org/W2985684656;https://openalex.org/W2990302163;https://openalex.org/W2998145162;https://openalex.org/W2999225196;https://openalex.org/W3010600010;https://openalex.org/W3013165905;https://openalex.org/W3024591130;https://openalex.org/W3032868513;https://openalex.org/W3037825799;https://openalex.org/W3044902155;https://openalex.org/W3045713571;https://openalex.org/W3046037449;https://openalex.org/W3122125470;https://openalex.org/W3125019846;https://openalex.org/W3125707221;https://openalex.org/W3127361825;https://openalex.org/W3145296828;https://openalex.org/W3150904570;https://openalex.org/W3153273683;https://openalex.org/W3160560894;https://openalex.org/W3160856016;https://openalex.org/W3161537902;https://openalex.org/W3193226555;https://openalex.org/W3214251695;https://openalex.org/W4224246713;https://openalex.org/W4226037378;https://openalex.org/W4240407251;https://openalex.org/W4250767237;https://openalex.org/W4285521953;https://openalex.org/W4293232152;https://openalex.org/W6963401302,Scopus;Field (mathematics);Artificial intelligence;Machine learning;Computer science;Variance (accounting);Bibliometrics;Web of science;Data science;Econometrics;Mathematics;Data mining;Political science;Economics,"Stock Market Forecasting Methods;Forecasting Techniques and Applications;Energy, Environment, Economic Growth"
-OPENALEX,https://openalex.org/W4393218542,https://doi.org/10.3390/su16072764,,Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach,SUSTAINABILITY,SUSTAINABILITY,2024,article,en,Bucharest University of Economic Studies,"Over the past years, machine learning and big data analysis have emerged, starting as a scientific and fictional domain, very interesting but difficult to test, and becoming one of the most powerful tools that is part of Industry 5.0 and has a significant impact on sustainable, resilient manufacturing. This has garnered increasing attention within scholarly circles due to its applicability in various domains. The scope of the article is to perform an exhaustive bibliometric analysis of existing papers that belong to machine learning and big data, pointing out the capability from a scientific point of view, explaining the usability of applications, and identifying which is the actual in a continually changing domain. In this context, the present paper aims to discuss the research landscape associated with the use of machine learning and big data analysis in Industry 5.0 in terms of themes, authors, citations, preferred journals, research networks, and collaborations. The initial part of the analysis focuses on the latest trends and how researchers lend a helping hand to change preconceptions about machine learning. The annual growth rate is 123.69%, which is considerable for such a short period, and it requires a comprehensive analysis to check the boom of articles in this domain. Further, the exploration investigates affiliated academic institutions, influential publications, journals, key contributors, and most delineative authors. To accomplish this, a dataset has been created containing researchers’ papers extracted from the ISI Web of Science database using keywords associated with machine learning and big data, starting in 2016 and ending in 2023. The paper incorporates graphs, which describe the most relevant authors, academic institutions, annual publications, country collaborations, and the most used words. The paper ends with a review of the globally most cited documents, describing the importance of machine learning and big data in Industry 5.0.",16,7,2764,2764,"Domenteanu, 2024, SUSTAINABILITY",26,"Domenteanu, Adrian;Cibu, Bianca;Delcea, Camelia","Domenteanu, Adrian;Cibu, Bianca;Delcea, Camelia",Bucharest University of Economic Studies,https://openalex.org/W2015846187;https://openalex.org/W2263682169;https://openalex.org/W2277805675;https://openalex.org/W2485363317;https://openalex.org/W2564810971;https://openalex.org/W2755950973;https://openalex.org/W2883370365;https://openalex.org/W2923180594;https://openalex.org/W2942942407;https://openalex.org/W2949084817;https://openalex.org/W2968770299;https://openalex.org/W2980029747;https://openalex.org/W2999855024;https://openalex.org/W3018002587;https://openalex.org/W3020966838;https://openalex.org/W3021951958;https://openalex.org/W3027944590;https://openalex.org/W3041439006;https://openalex.org/W3045792044;https://openalex.org/W3086866811;https://openalex.org/W3099009030;https://openalex.org/W3102990958;https://openalex.org/W3120521473;https://openalex.org/W3124153019;https://openalex.org/W3127908559;https://openalex.org/W3175077610;https://openalex.org/W3179183900;https://openalex.org/W3191438608;https://openalex.org/W3194459689;https://openalex.org/W3195580945;https://openalex.org/W3212055144;https://openalex.org/W3216637507;https://openalex.org/W4205335634;https://openalex.org/W4205584165;https://openalex.org/W4212802598;https://openalex.org/W4226099146;https://openalex.org/W4229063390;https://openalex.org/W4248701983;https://openalex.org/W4283079749;https://openalex.org/W4289109789;https://openalex.org/W4290098476;https://openalex.org/W4293217592;https://openalex.org/W4296143688;https://openalex.org/W4301419755;https://openalex.org/W4302009449;https://openalex.org/W4308467154;https://openalex.org/W4315783066;https://openalex.org/W4316037798;https://openalex.org/W4352978455;https://openalex.org/W4364302366;https://openalex.org/W4366198760;https://openalex.org/W4376130285;https://openalex.org/W4383226833;https://openalex.org/W4383501141;https://openalex.org/W4385299006;https://openalex.org/W4385759973;https://openalex.org/W4386013029;https://openalex.org/W4387856280;https://openalex.org/W4388109198;https://openalex.org/W4388723715;https://openalex.org/W4388731017;https://openalex.org/W4388761003;https://openalex.org/W4389049809;https://openalex.org/W4389068265;https://openalex.org/W4389686096;https://openalex.org/W4390944118;https://openalex.org/W4391034917;https://openalex.org/W4391309535;https://openalex.org/W4392518990;https://openalex.org/W6760792329;https://openalex.org/W6781077811;https://openalex.org/W6800583627,Perspective (graphical);Big data;Data science;Analytics;Bibliometrics;Data analysis;Computer science;Engineering;Data mining;Artificial intelligence,Big Data and Business Intelligence;Digital Transformation in Industry
-OPENALEX,https://openalex.org/W4396685483,https://doi.org/10.1007/s10639-024-12734-8,,Deciphering the impact of machine learning on education: Insights from a bibliometric analysis using bibliometrix R-package,EDUCATION AND INFORMATION TECHNOLOGIES,EDUCATION AND INFORMATION TECHNOLOGIES,2024,article,en,Beijing Foreign Studies University,,29,16,21995,22022,"Zhong, 2024, EDUCATION AND INFORMATION TECHNOLOGIES",21,"Zhong, Zilong;Guo, Hui;Qian, Kun","Zhong, Zilong;Guo, Hui;Qian, Kun",Beijing Foreign Studies University;Harbin Normal University;Chongqing University,https://openalex.org/W1970881937;https://openalex.org/W1993319826;https://openalex.org/W2029732300;https://openalex.org/W2038749650;https://openalex.org/W2089097786;https://openalex.org/W2156472837;https://openalex.org/W2755950973;https://openalex.org/W2793183907;https://openalex.org/W2945876440;https://openalex.org/W2964583491;https://openalex.org/W2967960129;https://openalex.org/W2977285514;https://openalex.org/W2983034992;https://openalex.org/W2983382509;https://openalex.org/W2985503058;https://openalex.org/W2989593976;https://openalex.org/W2998542624;https://openalex.org/W3001491100;https://openalex.org/W3004067956;https://openalex.org/W3045443865;https://openalex.org/W3045509030;https://openalex.org/W3047342523;https://openalex.org/W3083068801;https://openalex.org/W3092475292;https://openalex.org/W3097160820;https://openalex.org/W3100819463;https://openalex.org/W3103420222;https://openalex.org/W3106028942;https://openalex.org/W3119645277;https://openalex.org/W3132249579;https://openalex.org/W3135775894;https://openalex.org/W3140559152;https://openalex.org/W3155263273;https://openalex.org/W3160856016;https://openalex.org/W3164020442;https://openalex.org/W3193226555;https://openalex.org/W3197897963;https://openalex.org/W4200178763;https://openalex.org/W4200230543;https://openalex.org/W4223484919;https://openalex.org/W4223926312;https://openalex.org/W4224048112;https://openalex.org/W4250948876;https://openalex.org/W4280592718;https://openalex.org/W4283763160;https://openalex.org/W4289334535;https://openalex.org/W4295878168;https://openalex.org/W4296808957;https://openalex.org/W4308783088;https://openalex.org/W4311123195;https://openalex.org/W4312186458;https://openalex.org/W4319224963;https://openalex.org/W4321021705;https://openalex.org/W4367044714;https://openalex.org/W4386517498;https://openalex.org/W4389211185;https://openalex.org/W4391026063;https://openalex.org/W4391052267;https://openalex.org/W4391174753,Educational technology;Computer science;Science education;Mathematics education;Data science;Knowledge management;Psychology,Online Learning and Analytics;Big Data and Business Intelligence
-OPENALEX,https://openalex.org/W4303195184,https://doi.org/10.2174/9789815036060122010008,,A Bibliometric Analysis of Fault Prediction System using Machine Learning Techniques,BENTHAM SCIENCE PUBLISHERS EBOOKS,BENTHAM SCIENCE PUBLISHERS EBOOKS,2022,book-chapter,en,Chitkara University,"Fault prediction in software is an important aspect to be considered in software development because it ensures reliability and the quality of a software product. A high-quality software product consists of a few numbers of faults and failures. Software fault prediction (SFP) is crucial for the software quality assurance process as it examines the vulnerability of software products towards failures. Fault detection is a significant aspect of cost estimation in the initial stage, and hence, a fault predictor model is required to lower the expenses used during the development and maintenance phase. SFP is applied to identify the faulty modules of the software in order to complement the development as well as the testing process. Software metric based fault prediction reflects several aspects of the software. Several Machine Learning (ML) techniques have been implemented to eliminate faulty and unnecessary data from faulty modules. This chapter gives a brief introduction to SFP and includes a bibliometric analysis. The objective of the bibliometric analysis is to analyze research trends of ML techniques that are used for predicting software faults. This chapter uses the VOSviewer software and Biblioshiny tool to visually analyze 1623 papers fetched from the Scopus database for the past twenty years. It explores the distribution of publications over the years, top-rated publishers, contributing authors, funding agencies, cited papers and citations per paper. The collaboration of countries and cooccurrence analysis as well as over the year’s trend of author keywords are also explored. This chapter can be beneficial for young researchers to locate attractive and relevant research insights within SFP.",,,109,130,"Uppal, 2022, BENTHAM SCIENCE PUBLISHERS EBOOKS",23,"Uppal, Mudita;Gupta, Deepali;Mehta, Vaishali","Uppal, Mudita;Gupta, Deepali;Mehta, Vaishali",Chitkara University;Govind Ballabh Pant University of Agriculture and Technology,https://openalex.org/W631751048;https://openalex.org/W1493788687;https://openalex.org/W1838241330;https://openalex.org/W1980851144;https://openalex.org/W2007705030;https://openalex.org/W2028349769;https://openalex.org/W2034445489;https://openalex.org/W2043709414;https://openalex.org/W2045116160;https://openalex.org/W2053968218;https://openalex.org/W2095638516;https://openalex.org/W2099919734;https://openalex.org/W2150220236;https://openalex.org/W2160988203;https://openalex.org/W2305460223;https://openalex.org/W2562317638;https://openalex.org/W2616916909;https://openalex.org/W2755950973;https://openalex.org/W2766899299;https://openalex.org/W2783657687;https://openalex.org/W2889539774;https://openalex.org/W2902930463;https://openalex.org/W2921707507;https://openalex.org/W2966280563;https://openalex.org/W3008381189;https://openalex.org/W3009734373;https://openalex.org/W3014740133;https://openalex.org/W3117038359;https://openalex.org/W3139053720;https://openalex.org/W4235295935;https://openalex.org/W4248299818;https://openalex.org/W6759177930;https://openalex.org/W6831423407,Software quality;Computer science;Software;Software development;Reliability engineering;Software engineering;Software construction;Software quality analyst;Software metric;Software sizing;Quality (philosophy);Software quality assurance;Process (computing);Verification and validation;Software development process;Metric (unit);Fault (geology);Engineering;Operating system;Operations management,Software Engineering Research;Software Reliability and Analysis Research;Software System Performance and Reliability
-OPENALEX,https://openalex.org/W4319160636,https://doi.org/10.1016/j.eswa.2023.119640,,Financial applications of machine learning: A literature review,EXPERT SYSTEMS WITH APPLICATIONS,EXPERT SYSTEMS WITH APPLICATIONS,2023,review,en,Goa University,,219,,119640,119640,"Nazareth, 2023, EXPERT SYSTEMS WITH APPLICATIONS",186,"Nazareth, Noella;Reddy, Y.V.","Nazareth, Noella;Reddy, Y.V.",Goa University,https://openalex.org/W222543348;https://openalex.org/W1069790386;https://openalex.org/W1840208138;https://openalex.org/W1974938537;https://openalex.org/W1977627101;https://openalex.org/W2006680549;https://openalex.org/W2020848494;https://openalex.org/W2038443446;https://openalex.org/W2048801439;https://openalex.org/W2058417559;https://openalex.org/W2073754467;https://openalex.org/W2076143961;https://openalex.org/W2078115153;https://openalex.org/W2090637028;https://openalex.org/W2106895738;https://openalex.org/W2121970262;https://openalex.org/W2124532504;https://openalex.org/W2185628600;https://openalex.org/W2235716330;https://openalex.org/W2252909801;https://openalex.org/W2284153934;https://openalex.org/W2301106258;https://openalex.org/W2344279130;https://openalex.org/W2424889563;https://openalex.org/W2510651935;https://openalex.org/W2556544035;https://openalex.org/W2588836480;https://openalex.org/W2593842564;https://openalex.org/W2594142095;https://openalex.org/W2606916050;https://openalex.org/W2607162077;https://openalex.org/W2762466482;https://openalex.org/W2771814524;https://openalex.org/W2788057825;https://openalex.org/W2791306048;https://openalex.org/W2793037577;https://openalex.org/W2795111853;https://openalex.org/W2800942967;https://openalex.org/W2802832424;https://openalex.org/W2806777472;https://openalex.org/W2806948703;https://openalex.org/W2810154616;https://openalex.org/W2811103148;https://openalex.org/W2833425706;https://openalex.org/W2886249837;https://openalex.org/W2890297193;https://openalex.org/W2897494692;https://openalex.org/W2897596136;https://openalex.org/W2900743306;https://openalex.org/W2902408730;https://openalex.org/W2902534617;https://openalex.org/W2902640113;https://openalex.org/W2920934919;https://openalex.org/W2927690792;https://openalex.org/W2939367930;https://openalex.org/W2949202718;https://openalex.org/W2956885731;https://openalex.org/W2959801916;https://openalex.org/W2966861509;https://openalex.org/W2967723546;https://openalex.org/W2967732991;https://openalex.org/W2970527275;https://openalex.org/W2976611669;https://openalex.org/W2979358647;https://openalex.org/W2980996168;https://openalex.org/W2994537010;https://openalex.org/W2994949492;https://openalex.org/W3003538339;https://openalex.org/W3003975888;https://openalex.org/W3007883824;https://openalex.org/W3009416884;https://openalex.org/W3009457452;https://openalex.org/W3011495541;https://openalex.org/W3012235251;https://openalex.org/W3016298350;https://openalex.org/W3017051726;https://openalex.org/W3019427697;https://openalex.org/W3022746105;https://openalex.org/W3027003065;https://openalex.org/W3035669514;https://openalex.org/W3048267635;https://openalex.org/W3048630347;https://openalex.org/W3064683854;https://openalex.org/W3081572486;https://openalex.org/W3082130641;https://openalex.org/W3083080466;https://openalex.org/W3083125023;https://openalex.org/W3084045086;https://openalex.org/W3088545074;https://openalex.org/W3093186795;https://openalex.org/W3093310271;https://openalex.org/W3094452610;https://openalex.org/W3095388897;https://openalex.org/W3106063491;https://openalex.org/W3110826337;https://openalex.org/W3110845139;https://openalex.org/W3115503345;https://openalex.org/W3123937240;https://openalex.org/W3124134784;https://openalex.org/W3125049021;https://openalex.org/W3125139843;https://openalex.org/W3126678629;https://openalex.org/W3126720980;https://openalex.org/W3127150246;https://openalex.org/W3129863217;https://openalex.org/W3135241214;https://openalex.org/W3136959963;https://openalex.org/W3143493396;https://openalex.org/W3156409915;https://openalex.org/W3156601971;https://openalex.org/W3159148887;https://openalex.org/W3160228030;https://openalex.org/W3162950604;https://openalex.org/W3165926838;https://openalex.org/W3171095691;https://openalex.org/W3172498855;https://openalex.org/W3173768691;https://openalex.org/W3185522547;https://openalex.org/W3207578078;https://openalex.org/W3217626109;https://openalex.org/W4200391316;https://openalex.org/W4200410702;https://openalex.org/W4206123005;https://openalex.org/W4206178848;https://openalex.org/W4211068006;https://openalex.org/W4212791338;https://openalex.org/W4220704507;https://openalex.org/W4220827691;https://openalex.org/W4220945596;https://openalex.org/W4221053610;https://openalex.org/W4223531847;https://openalex.org/W4223569375;https://openalex.org/W4226061267;https://openalex.org/W4226469147;https://openalex.org/W4237835726;https://openalex.org/W4254724182;https://openalex.org/W4280620998;https://openalex.org/W4280640105;https://openalex.org/W4281383361;https://openalex.org/W4281569614;https://openalex.org/W4281703464;https://openalex.org/W4281756923;https://openalex.org/W4281792374;https://openalex.org/W4281975289;https://openalex.org/W4283763322;https://openalex.org/W4283774845;https://openalex.org/W4284988747;https://openalex.org/W4285169194;https://openalex.org/W6698327768;https://openalex.org/W6752621111;https://openalex.org/W6765470117;https://openalex.org/W6768701007;https://openalex.org/W6772064197;https://openalex.org/W6772875154;https://openalex.org/W6774700487;https://openalex.org/W6781688287;https://openalex.org/W6783468131;https://openalex.org/W6785318008;https://openalex.org/W6787093747;https://openalex.org/W6789930184;https://openalex.org/W6790404978;https://openalex.org/W6795086908;https://openalex.org/W6796798734;https://openalex.org/W6804660761;https://openalex.org/W6805299581;https://openalex.org/W6807091881;https://openalex.org/W6809781435;https://openalex.org/W6809970921;https://openalex.org/W6838741634;https://openalex.org/W6838752390;https://openalex.org/W6838851410;https://openalex.org/W6839396184;https://openalex.org/W6839802115,Computer science;Systematic review;Portfolio;Artificial intelligence;Machine learning;Bankruptcy;Finance;Economics,Stock Market Forecasting Methods;Financial Markets and Investment Strategies;Financial Distress and Bankruptcy Prediction
-OPENALEX,https://openalex.org/W4246652249,https://doi.org/10.12688/f1000research.15620.1,,Exploring machine learning: A bibliometric general approach using SciMAT,F1000RESEARCH,F1000RESEARCH,2018,preprint,en,University of Cauca," Background: Machine learning is becoming increasingly important for companies and the scientific community. In this study, we perform a bibliometric analysis on machine learning research, in order to provide an overview of the scientific work during the period 2007-2017 in this area and to show trends that could be the basis for future developments in the field. Methods: This study is carried out using the SciMAT tool based on results extracted from Scopus. This analysis shows the strategic diagrams of evolution and a set of thematic networks. The results provide information on broad tendencies of machine learning. Results: The results show that SciMAT is a useful tool to carry out a science mapping analysis, and emphasizes the premise that machine learning has boundless applications and will continue to be an interesting research field in the future. Conclusions: Some of the conclusions exposed in this study show that classification algorithms have been widely studied and represent a relevant tool for generating different machine learning applications. Nonetheless, regression algorithms are becoming increasingly important in the scientific community, allowing the generation of solutions to predict diseases, sales, and yields, for example. ",7,,1210,1210,"Rincon-Patino, 2018, F1000RESEARCH",16,"Rincon-Patino, Juan;Ramírez-González, Gustavo;Corrales, Juan Carlos","Rincon-Patino, Juan;Ramírez-González, Gustavo;Corrales, Juan Carlos",University of Cauca,https://openalex.org/W239165158;https://openalex.org/W1596324102;https://openalex.org/W1972785704;https://openalex.org/W1977150449;https://openalex.org/W1981886524;https://openalex.org/W2017680781;https://openalex.org/W2029088861;https://openalex.org/W2043347849;https://openalex.org/W2071659396;https://openalex.org/W2093602450;https://openalex.org/W2102203250;https://openalex.org/W2105822516;https://openalex.org/W2114046749;https://openalex.org/W2118020653;https://openalex.org/W2135455887;https://openalex.org/W2295897189;https://openalex.org/W2591652315;https://openalex.org/W2724945533;https://openalex.org/W2727482721;https://openalex.org/W4244340606,Field (mathematics);Artificial intelligence;Computer science;Mathematics,Text and Document Classification Technologies;Imbalanced Data Classification Techniques;Data Stream Mining Techniques
-OPENALEX,https://openalex.org/W4402635887,https://doi.org/10.1016/j.iswa.2024.200441,,Evolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysis,INTELLIGENT SYSTEMS WITH APPLICATIONS,INTELLIGENT SYSTEMS WITH APPLICATIONS,2024,article,en,National Open University of Nigeria,"• The current status is presented, along with the publication patterns from 1994 to 2023, as well as the topic area categories, which include the general analysis and fundamental features that are provided include the following: total publications (TP), and percentage total publications (%TP) for MDLHC research, several viewpoints on types and research directions, as well as significant indicators at the levels of countries, institutions, and funding organizations. Furthermore, this study presents the varying number of highest publications and citations within the past decade (1994–2023). • Analysing the collaborations at the level of countries, authorship and institutions, the corresponding networks are demonstrated by network visualisation map for co-authorship on MLHC research, network visualisation map for collaborating countries on MLHC research. • The themes of all publications and the top influential journals are aggregated based on the author-keywords analysis aimed to help researchers understand the hotspots and focus. • Ultimately, the study seeks to add impetus to the current discourse surrounding the growth of ML in HC, nurturing a greater comprehension of its transformative prospects as well as challenges that require tackling to harness its complete benefits. • According to all the analyses and visualisation maps, It is also envisaged that the insights garnered from the study will avail academics, politicians, and medical practitioners with critical insights that could stimulate pioneering research initiatives and novel collaborations This bibliometric research explores the global evolution of machine learning applications in medical and healthcare research for 3 decades (1994 to 2023). The study applies data mining techniques to a comprehensive dataset of published articles related to machine learning applications in the medical and healthcare sectors. The data extraction process includes the retrieval of relevant information from the source sources such as journals, books, and conference proceedings. An analysis of the extracted data is then conducted to identify the trends in the machine learning applications in medical and healthcare research. The Results revealed the publications published and indexed in the Scopus and PubMed database over the last 30 years. Bibliometric Analysis revealed that funding played a more significant role in publication productivity compared to collaboration (co-authorships), particularly at the country level. Hotspots analysis revealed three core research themes on MLHC research hence demonstrating the importance of machine learning applications to medical and healthcare research. Further, the study showed that the MLHC research landscape has largely focused on ML applications to tackle various issues ranging from chronic medical challenges (e.g., cardiological diseases) to patient data security. The findings of this research may be useful to policy makers and practitioners in the medical and healthcare sectors and to global research endeavours in the field. Future studies could include addressing issues such as growing ethical considerations, integration, and practical applications in wearable technology, IoT, and smart healthcare systems.",24,,200441,200441,"Ajibade, 2024, INTELLIGENT SYSTEMS WITH APPLICATIONS",22,"Ajibade, Samuel-Soma M.;Alhassan, Gloria Nnadwa;Zaïdi, Abdelhamid;Oki, Olukayode;Awotunde, Joseph Bamidele;Ogbuju, Emeka;Akintoye, Kayode A.","Ajibade, Samuel-Soma M.;Alhassan, Gloria Nnadwa;Zaïdi, Abdelhamid;Oki, Olukayode;Awotunde, Joseph Bamidele;Ogbuju, Emeka;Akintoye, Kayode A.","National Open University of Nigeria;Istanbul Commerce University;Sunway University;Western Caspian University;İstanbul Gelişim Üniversitesi;Qassim University;Walter Sisulu University;University of Ilorin;Federal University Lokoja;The Federal Polytechnic, Ado-Ekiti",https://openalex.org/W1968411139;https://openalex.org/W2029025845;https://openalex.org/W2163187547;https://openalex.org/W2263556835;https://openalex.org/W2536826723;https://openalex.org/W2593193389;https://openalex.org/W2610135452;https://openalex.org/W2750268731;https://openalex.org/W2765272403;https://openalex.org/W2911300722;https://openalex.org/W2913240367;https://openalex.org/W2936086693;https://openalex.org/W2940524603;https://openalex.org/W2943491685;https://openalex.org/W2947814289;https://openalex.org/W2969881216;https://openalex.org/W2999445018;https://openalex.org/W3006913750;https://openalex.org/W3011742849;https://openalex.org/W3016123475;https://openalex.org/W3019449433;https://openalex.org/W3021448296;https://openalex.org/W3035142875;https://openalex.org/W3042276730;https://openalex.org/W3048071928;https://openalex.org/W3080627676;https://openalex.org/W3087893815;https://openalex.org/W3088589938;https://openalex.org/W3092371431;https://openalex.org/W3093861859;https://openalex.org/W3101604855;https://openalex.org/W3118261252;https://openalex.org/W3150904570;https://openalex.org/W3160856016;https://openalex.org/W3182064909;https://openalex.org/W3186830123;https://openalex.org/W3193630181;https://openalex.org/W3205993471;https://openalex.org/W4206935801;https://openalex.org/W4221086457;https://openalex.org/W4225289891;https://openalex.org/W4230193413;https://openalex.org/W4239687298;https://openalex.org/W4242733110;https://openalex.org/W4244631099;https://openalex.org/W4280553349;https://openalex.org/W4281483819;https://openalex.org/W4281616267;https://openalex.org/W4281630308;https://openalex.org/W4297478379;https://openalex.org/W4298152878;https://openalex.org/W4308200999;https://openalex.org/W4312187060;https://openalex.org/W4318051995;https://openalex.org/W4327852009;https://openalex.org/W4362666995;https://openalex.org/W4366310212;https://openalex.org/W4366588036;https://openalex.org/W4379057423;https://openalex.org/W4381054425;https://openalex.org/W4383682762;https://openalex.org/W4384944264;https://openalex.org/W4385342688;https://openalex.org/W4385835130;https://openalex.org/W4385884980;https://openalex.org/W4386803046;https://openalex.org/W4386859003;https://openalex.org/W4386961689;https://openalex.org/W4389254465;https://openalex.org/W4391279317;https://openalex.org/W4402040730;https://openalex.org/W6734298742;https://openalex.org/W6744623101;https://openalex.org/W6747851429;https://openalex.org/W6752140623;https://openalex.org/W6753024113;https://openalex.org/W6755400373;https://openalex.org/W6779397425;https://openalex.org/W6780281546;https://openalex.org/W6783001559;https://openalex.org/W6800879869;https://openalex.org/W6810097369;https://openalex.org/W6810864097;https://openalex.org/W6842497516;https://openalex.org/W6851301879;https://openalex.org/W6851579319;https://openalex.org/W6853477173;https://openalex.org/W6855493788;https://openalex.org/W6903323286,Data science;Analytics;Health care;Computer science;Political science,Artificial Intelligence in Healthcare;COVID-19 diagnosis using AI;Artificial Intelligence in Healthcare and Education
-OPENALEX,https://openalex.org/W4220894906,https://doi.org/10.1108/bij-12-2021-0755,,Research themes in machine learning applications in supply chain management using bibliometric analysis tools,BENCHMARKING AN INTERNATIONAL JOURNAL,BENCHMARKING AN INTERNATIONAL JOURNAL,2022,article,en,Sultan Qaboos University,"Purpose This paper conducts a Systematic Literature Review (SLR) of Machine Learning (ML) in Supply Chain Management through bibliometric and network analysis, the authors are able to grasp key features of the contemporary literature. The study makes use of state-of-the-art analytical framework based on a unified approach to reveal insights from the present body of knowledge and the potentials for future research developments. Design/methodology/approach Unlike standard literature reviews, in SLR, a structured approach is followed. The approach enables utilizing contemporary tools and software packages such as R-package “bibliometrix” and Gephi for exploratory and visual analytics. A number of clustering methods are employed to form clusters. Later, multivariate analysis methodologies are adopted to determine the dominant clusters for the influential co-cited references. Findings Using contemporary tools from Bibliometric Analysis (BA), the authors identify in an exploratory analysis, the influential authors, sources, regions, affiliations and papers. In addition, the use of network analysis tools reveals research clusters, topological analysis, key research topics, interrelation and authors’ collaboration along with their patterns. Finally, the optimum number of clusters computed for cluster analysis is decided using a systematic procedure based on multivariate analysis such as k-means and factor analysis. Originality/value Modern-day supply chains increasingly depend on developing superior insights from large amounts of data available from diverse sources in unstructured and semi-structured formats. In order to maintain a competitive edge, the supply chains need to perform speedy analysis of big data using efficient tools that provide real-time decision-making insights. Such an analysis necessitates automated processing using intelligent ML algorithms. Through a BA followed by a detailed data visualization in a network analysis enabled grasping key features of the contemporary literature. The analysis is based on 155 documents from the period 2008 to 2018 selected using a systematic selection procedure.",30,3,834,867,"Raza, 2022, BENCHMARKING AN INTERNATIONAL JOURNAL",30,"Raza, Syed Asif;Govindaluri, Srikrishna Madhumohan;Bhutta, M. Khurrum S.","Raza, Syed Asif;Govindaluri, Srikrishna Madhumohan;Bhutta, M. Khurrum S.",Sultan Qaboos University;Ohio University,https://openalex.org/W58954717;https://openalex.org/W139852187;https://openalex.org/W1089297094;https://openalex.org/W1491972678;https://openalex.org/W1511719718;https://openalex.org/W1529681343;https://openalex.org/W1554242993;https://openalex.org/W1554291173;https://openalex.org/W1575873103;https://openalex.org/W1856263053;https://openalex.org/W1890122984;https://openalex.org/W1911451788;https://openalex.org/W1965442071;https://openalex.org/W1965746216;https://openalex.org/W1966538856;https://openalex.org/W1968475341;https://openalex.org/W1969390721;https://openalex.org/W1970647173;https://openalex.org/W1970952970;https://openalex.org/W1972968145;https://openalex.org/W1978172667;https://openalex.org/W1979379096;https://openalex.org/W1979458009;https://openalex.org/W1982585826;https://openalex.org/W1983344181;https://openalex.org/W1984025929;https://openalex.org/W1985273827;https://openalex.org/W1988277750;https://openalex.org/W1988428279;https://openalex.org/W1992880712;https://openalex.org/W1992983421;https://openalex.org/W1994492724;https://openalex.org/W1997383668;https://openalex.org/W1998485133;https://openalex.org/W1999504788;https://openalex.org/W2003074920;https://openalex.org/W2005168277;https://openalex.org/W2005207065;https://openalex.org/W2008707928;https://openalex.org/W2009540046;https://openalex.org/W2011430131;https://openalex.org/W2011874921;https://openalex.org/W2011957532;https://openalex.org/W2013258619;https://openalex.org/W2014121877;https://openalex.org/W2014644425;https://openalex.org/W2015453663;https://openalex.org/W2017702422;https://openalex.org/W2017821581;https://openalex.org/W2018021847;https://openalex.org/W2020360355;https://openalex.org/W2020432772;https://openalex.org/W2021519095;https://openalex.org/W2023629325;https://openalex.org/W2023944948;https://openalex.org/W2024390183;https://openalex.org/W2025883194;https://openalex.org/W2026605760;https://openalex.org/W2026676048;https://openalex.org/W2026729402;https://openalex.org/W2026794123;https://openalex.org/W2026816730;https://openalex.org/W2029552401;https://openalex.org/W2029907981;https://openalex.org/W2029969606;https://openalex.org/W2033380040;https://openalex.org/W2033459821;https://openalex.org/W2033693670;https://openalex.org/W2034409962;https://openalex.org/W2035854859;https://openalex.org/W2035987259;https://openalex.org/W2038258091;https://openalex.org/W2038742525;https://openalex.org/W2039509301;https://openalex.org/W2042218363;https://openalex.org/W2042876421;https://openalex.org/W2049294565;https://openalex.org/W2049806837;https://openalex.org/W2058027354;https://openalex.org/W2059508663;https://openalex.org/W2060284579;https://openalex.org/W2061150529;https://openalex.org/W2061842011;https://openalex.org/W2061993807;https://openalex.org/W2062015398;https://openalex.org/W2062140782;https://openalex.org/W2063011680;https://openalex.org/W2063056835;https://openalex.org/W2066704625;https://openalex.org/W2068394020;https://openalex.org/W2070410573;https://openalex.org/W2070960381;https://openalex.org/W2071496984;https://openalex.org/W2074634340;https://openalex.org/W2075801623;https://openalex.org/W2076309564;https://openalex.org/W2076983736;https://openalex.org/W2080693034;https://openalex.org/W2080696742;https://openalex.org/W2082985613;https://openalex.org/W2083457386;https://openalex.org/W2084176908;https://openalex.org/W2084674986;https://openalex.org/W2090813263;https://openalex.org/W2092595282;https://openalex.org/W2092620885;https://openalex.org/W2093948910;https://openalex.org/W2097148950;https://openalex.org/W2097359812;https://openalex.org/W2097529207;https://openalex.org/W2098215764;https://openalex.org/W2100033597;https://openalex.org/W2104925392;https://openalex.org/W2106467926;https://openalex.org/W2106488040;https://openalex.org/W2110205957;https://openalex.org/W2111780926;https://openalex.org/W2113348250;https://openalex.org/W2115721093;https://openalex.org/W2116348957;https://openalex.org/W2116859162;https://openalex.org/W2117871237;https://openalex.org/W2124344619;https://openalex.org/W2124778735;https://openalex.org/W2125910575;https://openalex.org/W2127151227;https://openalex.org/W2131681506;https://openalex.org/W2131814102;https://openalex.org/W2135133355;https://openalex.org/W2135455887;https://openalex.org/W2137694668;https://openalex.org/W2141833837;https://openalex.org/W2152727262;https://openalex.org/W2156733681;https://openalex.org/W2157812421;https://openalex.org/W2160203079;https://openalex.org/W2160781605;https://openalex.org/W2161160262;https://openalex.org/W2163187547;https://openalex.org/W2164308914;https://openalex.org/W2165691491;https://openalex.org/W2167482691;https://openalex.org/W2168004073;https://openalex.org/W2170493570;https://openalex.org/W2171718612;https://openalex.org/W2261525379;https://openalex.org/W2293068345;https://openalex.org/W2302535939;https://openalex.org/W2302800291;https://openalex.org/W2314105938;https://openalex.org/W2334266275;https://o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science;GRASP;Data science;Network analysis;Cluster analysis;Originality;Supply chain;Supply chain management;Systematic review;Exploratory analysis;Social network analysis;Data mining;Management science;Artificial intelligence;Engineering;Software engineering;Sociology;Qualitative research,Sustainable Supply Chain Management;Supply Chain Resilience and Risk Management;Big Data and Business Intelligence
-OPENALEX,https://openalex.org/W4378191097,https://doi.org/10.1016/j.desal.2023.116715,,"Faradaic deionization technology: Insights from bibliometric, data mining and machine learning approaches",DESALINATION,DESALINATION,2023,article,en,Universidad Complutense de Madrid,"Faradaic deionization (FDI) is an emerging water treatment technology based on electrodes able to remove ionic species from water by charge transfer reactions. It is a young and promising technology that has attracted much attention due to its large capacity to store ions and the high selectivity of the faradaic electrode materials. This study reviews published papers on FDI from different angles: data mining, bibliometric and machine learning. Metrics such as annual growth rate, most important journals, relevant authors, collaborations maps, sentiment and subjectivity analysis, similarity and clustering analysis were performed. The results indicated that the strong interest in FDI really started in 2016, China is the most active country in FDI, and Desalination is the most important journal publishing FDI articles. The word cloud method showed that the most preferred adopted words are deionization, capacitive, electrode, material. Sentiment analysis results indicated that most of the researchers are optimistic about FDI technology. The title similarity method revealed that FDI researchers were successful in proposing unique and appropriate titles. The clustering approach stressed that FDI literature is concentrated on electrode material production, desalination application, lithium recovery and comparison with CDI.",563,,116715,116715,"Aytaç, 2023, DESALINATION",26,"Aytaç, Ersin;Fombona‐Pascual, Alba;Lado, Julio J.;García‐Quismondo, Enrique;Palma, Jesús;Khayet, M.","Aytaç, Ersin;Fombona‐Pascual, Alba;Lado, Julio J.;García‐Quismondo, Enrique;Palma, Jesús;Khayet, M.",IMDEA Energy Institute;Universidad Complutense de Madrid;Bülent Ecevit University;IMDEA 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deionization;Cluster analysis;Foreign direct investment;Desalination;Sentiment analysis;Similarity (geometry);Computer science;Materials science;Data science;Artificial intelligence;Chemistry;Political science;Membrane,Membrane-based Ion Separation Techniques;Membrane Separation Technologies;Advanced battery technologies research
-OPENALEX,https://openalex.org/W4406125397,https://doi.org/10.1021/acsestwater.4c01047,,Machine Learning in Wastewater Treatment: A Comprehensive Bibliometric Review,ACS ES&T WATER,ACS ES&T WATER,2025,article,en,Harbin Institute of Technology,"Accurate identification and control of wastewater treatment processes are critical for the efficient use of water resources. Advances in online monitoring and computational capabilities have facilitated the integration of artificial intelligence (AI), particularly machine learning (ML), into wastewater treatment systems. This review analyzes 433 studies on ML applications in wastewater treatment from 2000 to 2022 using bibliometric methods, examining research trends, hotspots, and future directions. Since 2015, the field has experienced a significant surge in publications. The United States and Spain are notable for their long-standing contributions, while China, despite entering the field late in 2012, has emerged as the leading contributor in publication volume. Keyword analysis reveals “neural networks” and “artificial neural networks” as the most frequently applied ML techniques, alongside terms like “prediction”, “optimization”, “fault detection”, and “design”. Our comprehensive review further shows that ML applications in wastewater treatment primarily focus on feature identification, parameter prediction, anomaly detection, and optimized control with key application scenarios including systems, wastewater, waste gas, and sludge. As the demand for AI in wastewater treatment continues to grow, multimodel integration and in-depth development may become the focus of future research to address multiobjective challenges in wastewater treatment more effectively.",5,2,511,524,"Yang, 2025, ACS ES&T WATER",19,"Yang, Wenqi;Li, Haiyan","Yang, Wenqi;Li, Haiyan",Harbin Institute of Technology,https://openalex.org/W1498625477;https://openalex.org/W1559665635;https://openalex.org/W1966525532;https://openalex.org/W2002134958;https://openalex.org/W2031055067;https://openalex.org/W2049125224;https://openalex.org/W2067164435;https://openalex.org/W2076063813;https://openalex.org/W2263928265;https://openalex.org/W2323408656;https://openalex.org/W2599784308;https://openalex.org/W2803984689;https://openalex.org/W2825946107;https://openalex.org/W2888639312;https://openalex.org/W2889740942;https://openalex.org/W2901048536;https://openalex.org/W2901334219;https://openalex.org/W2914192042;https://openalex.org/W2924370663;https://openalex.org/W2924962937;https://openalex.org/W2931156301;https://openalex.org/W2942882782;https://openalex.org/W2949006411;https://openalex.org/W2953794436;https://openalex.org/W2957524790;https://openalex.org/W2978132992;https://openalex.org/W2991816849;https://openalex.org/W3003626942;https://openalex.org/W3004957259;https://openalex.org/W3010160624;https://openalex.org/W3016301891;https://openalex.org/W3036186095;https://openalex.org/W3046662766;https://openalex.org/W3093963740;https://openalex.org/W3094733926;https://openalex.org/W3094868952;https://openalex.org/W3097106463;https://openalex.org/W3098217728;https://openalex.org/W3108333992;https://openalex.org/W3121064191;https://openalex.org/W3133990288;https://openalex.org/W3140569390;https://openalex.org/W3153748568;https://openalex.org/W3169952140;https://openalex.org/W3173215944;https://openalex.org/W3175615720;https://openalex.org/W3176785116;https://openalex.org/W3177236192;https://openalex.org/W3177594093;https://openalex.org/W3188508961;https://openalex.org/W3194848936;https://openalex.org/W3194888710;https://openalex.org/W3201506545;https://openalex.org/W3206016295;https://openalex.org/W3209893417;https://openalex.org/W4206450009;https://openalex.org/W4210512173;https://openalex.org/W4210628035;https://openalex.org/W4210784138;https://openalex.org/W4213455913;https://openalex.org/W4220679516;https://openalex.org/W4223466529;https://openalex.org/W4225158299;https://openalex.org/W4238591974;https://openalex.org/W4281682503;https://openalex.org/W4282589860;https://openalex.org/W4285098376;https://openalex.org/W4285178158;https://openalex.org/W4288391517;https://openalex.org/W4291278873;https://openalex.org/W4294167385;https://openalex.org/W4304992101;https://openalex.org/W4307288953;https://openalex.org/W4310006980;https://openalex.org/W4379647502;https://openalex.org/W4387082123;https://openalex.org/W4394063272;https://openalex.org/W4401004978,Wastewater;Sewage treatment;Computer science;Environmental science;Environmental engineering,Water Quality Monitoring and Analysis;Water Quality Monitoring Technologies
-OPENALEX,https://openalex.org/W4399714463,https://doi.org/10.54216/jisiot.130115,,Systematic Analysis based on Conflux of Machine Learning and Internet of Things using Bibliometric analysis,JOURNAL OF INTELLIGENT SYSTEMS AND INTERNET OF THINGS,JOURNAL OF INTELLIGENT SYSTEMS AND INTERNET OF THINGS,2024,article,en,,"IoT devices produce a gigantic amount of data and it has grown exponentially in previous years. To get insights from this multi-property data, machine learning has proved its worth across the industry. The present paper provides an overview of the variety of data collected through IoT devices. The conflux of machine learning with IoT is also explained using the bibliometric analysis technique. This paper presents a systematic literature review using bibliometric analysis of the data collected from Scopus and WoS. Academic literature for the last six years is used to explore research insights, patterns, and trends in the field of IoT using machine learning. This study analyses and assesses research for the last six years using machine learning in seven IoT domains like Healthcare, Smart City, Energy systems, Industrial IoT, Security, Climate, and Agriculture. The author’s and country-wise citation analysis is also presented in this study. VOSviewer version 1.6.18 is used to provide a graphical representation of author citation analysis. This study may be quite helpful for researchers and practitioners to develop a blueprint of machine learning techniques in various IoT domains.",13,1,196,224,"Nasib, 2024, JOURNAL OF INTELLIGENT SYSTEMS AND INTERNET OF THINGS",17,"Nasib, Nasib;Addula, Santosh Reddy;Jain, Anurag;Gulia, Preeti;Gill, Nasib Singh;Veerasamy, Bala Dhandayuthapani","Nasib, Nasib;Addula, Santosh Reddy;Jain, Anurag;Gulia, Preeti;Gill, Nasib Singh;Veerasamy, Bala Dhandayuthapani",,,Computer science;Blueprint;Internet of Things;Data science;Field (mathematics);Variety (cybernetics);Artificial intelligence;Machine learning;Citation analysis;Bibliometrics;Citation;World Wide Web;Engineering,Organizational and Employee Performance;Internet of Things and AI;IoT and Edge/Fog Computing
-OPENALEX,https://openalex.org/W4399466595,https://doi.org/10.1016/j.heliyon.2024.e32548,https://pubmed.ncbi.nlm.nih.gov/38975193,Diagnosis of mental disorders using machine learning: Literature review and bibliometric mapping from 2012 to 2023,HELIYON,HELIYON,2024,article,en,University of Petroleum and Energy Studies,"Background: Mental disorders (MDs) are becoming a leading burden in non-communicable diseases (NCDs). As per the World Health Organization's 2022 assessment report, there was a steep increase of 25 % in MDs during the COVID-19 pandemic. Early diagnosis of MDs can significantly improve treatment outcome and save disability-adjusted life years (DALYs). In recent times, the application of machine learning (ML) and deep learning (DL)) has shown promising results in the diagnosis of MDs, and the field has witnessed a huge research output in the form of research publications. Therefore, a bibliometric mapping along with a review of recent advancements is required. Methods: This study presents a bibliometric analysis and review of the research, published over the last 10 years. Literature searches were conducted in the Scopus database for the period from January 1, 2012, to June 9, 2023. The data was filtered and screened to include only relevant and reliable publications. A total of 2811 journal articles were found. The data was exported to a comma-separated value (CSV) format for further analysis. Furthermore, a review of 40 selected studies was performed. Results: The popularity of ML techniques in diagnosing MDs has been growing, with an annual research growth rate of 17.05 %. The Journal of Affective Disorders published the most documents (n = 97), while Wang Y. (n = 64) has published the most articles. Lotka's law is observed, with a minority of authors contributing the majority of publications. The top affiliating institutes are the West China Hospital of Sichuan University followed by the University of California, with China and the US dominating the top 10 institutes. While China has more publications, papers affiliated with the US receive more citations. Depression and schizophrenia are the primary focuses of ML and deep learning (DL) in mental disease detection. Co-occurrence network analysis reveals that ML is associated with depression, schizophrenia, autism, anxiety, ADHD, obsessive-compulsive disorder, and PTSD. Popular algorithms include support vector machine (SVM) classifier, decision tree classifier, and random forest classifier. Furthermore, DL is linked to neuroimaging techniques such as MRI, fMRI, and EEG, as well as bipolar disorder. Current research trends encompass DL, LSTM, generalized anxiety disorder, feature fusion, and convolutional neural networks.",10,12,e32548,e32548,"Sharma, 2024, HELIYON",24,"Sharma, Chandra Mani;Chariar, Vijayaraghavan M.","Sharma, Chandra Mani;Chariar, Vijayaraghavan M.",University of Petroleum and Energy Studies;Indian Institute of Technology Delhi,https://openalex.org/W55134547;https://openalex.org/W87477386;https://openalex.org/W1267646904;https://openalex.org/W1537120672;https://openalex.org/W1972498024;https://openalex.org/W1991750181;https://openalex.org/W2019452590;https://openalex.org/W2021741903;https://openalex.org/W2026231733;https://openalex.org/W2027106132;https://openalex.org/W2043699623;https://openalex.org/W2075105655;https://openalex.org/W2090357978;https://openalex.org/W2098145773;https://openalex.org/W2121468917;https://openalex.org/W2124662932;https://openalex.org/W2134676768;https://openalex.org/W2136530770;https://openalex.org/W2150692529;https://openalex.org/W2168921227;https://openalex.org/W2170372220;https://openalex.org/W2294481976;https://openalex.org/W2310177520;https://openalex.org/W2513038336;https://openalex.org/W2520441859;https://openalex.org/W2525381941;https://openalex.org/W2604718245;https://openalex.org/W2622897137;https://openalex.org/W2759476730;https://openalex.org/W2771901310;https://openalex.org/W2885340087;https://openalex.org/W2885994488;https://openalex.org/W2886733253;https://openalex.org/W2887089934;https://openalex.org/W2907121178;https://openalex.org/W2908631447;https://openalex.org/W2911462778;https://openalex.org/W2911925130;https://openalex.org/W2914884512;https://openalex.org/W2957607612;https://openalex.org/W2960600329;https://openalex.org/W2962865354;https://openalex.org/W2969522137;https://openalex.org/W2973721184;https://openalex.org/W2977637323;https://openalex.org/W2981074672;https://openalex.org/W2996393987;https://openalex.org/W3000225498;https://openalex.org/W3005077487;https://openalex.org/W3011369183;https://openalex.org/W3014318280;https://openalex.org/W3016807694;https://openalex.org/W3036243984;https://openalex.org/W3081983074;https://openalex.org/W3085360921;https://openalex.org/W3087914553;https://openalex.org/W3087928091;https://openalex.org/W3090929564;https://openalex.org/W3100407435;https://openalex.org/W3115163248;https://openalex.org/W3119383401;https://openalex.org/W3119592672;https://openalex.org/W3134762208;https://openalex.org/W3148510751;https://openalex.org/W3151786997;https://openalex.org/W3162039091;https://openalex.org/W3166320351;https://openalex.org/W3169261141;https://openalex.org/W3177109830;https://openalex.org/W3190588417;https://openalex.org/W3193625712;https://openalex.org/W3193976933;https://openalex.org/W3195135152;https://openalex.org/W3195344199;https://openalex.org/W3196283869;https://openalex.org/W3201174872;https://openalex.org/W3202188208;https://openalex.org/W3209406277;https://openalex.org/W3210968912;https://openalex.org/W3215021794;https://openalex.org/W3215080383;https://openalex.org/W3215557350;https://openalex.org/W4205749015;https://openalex.org/W4206163452;https://openalex.org/W4211145814;https://openalex.org/W4214557645;https://openalex.org/W4223890698;https://openalex.org/W4224299511;https://openalex.org/W4225145922;https://openalex.org/W4225597923;https://openalex.org/W4226147693;https://openalex.org/W4238243588;https://openalex.org/W4281994922;https://openalex.org/W4286218913;https://openalex.org/W4293202050;https://openalex.org/W4293254210;https://openalex.org/W4293374259;https://openalex.org/W4295912519;https://openalex.org/W4297051283;https://openalex.org/W4300981010;https://openalex.org/W4310239155;https://openalex.org/W4311039275;https://openalex.org/W4313062391;https://openalex.org/W4313361314;https://openalex.org/W4313443450;https://openalex.org/W4313597570;https://openalex.org/W4315477647;https://openalex.org/W4315498350;https://openalex.org/W4316465174;https://openalex.org/W4318053895;https://openalex.org/W4318459844;https://openalex.org/W4321636216;https://openalex.org/W4323286786;https://openalex.org/W4362469304;https://openalex.org/W4367146937;https://openalex.org/W4388020455;https://openalex.org/W4388826001;https://openalex.org/W4389438760;https://openalex.org/W6602278584;https://openalex.org/W6643318537;https://openalex.org/W6677125828;https://openalex.org/W6684194278;https://openalex.org/W6757700926;https://openalex.org/W6783081853;https://openalex.org/W6846943747;https://openalex.org/W6849512276,Coronavirus disease 2019 (COVID-19);Pandemic;Mental health;Bibliometrics;Medicine;Psychiatry;Computer science;Library science;Pathology,Mental Health via Writing;Machine Learning in Healthcare;Digital Mental Health Interventions
-OPENALEX,https://openalex.org/W4381425857,https://doi.org/10.3390/land12051050,,Review of Land Use Change Detection—A Method Combining Machine Learning and Bibliometric Analysis,LAND,LAND,2023,article,en,Chinese Academy of Sciences,"Land use change detection (LUCD) is a critical technology with applications in various fields, including forest disturbance, cropland changes, and urban expansion. However, the current review articles on LUCD tend to be limited in scope, rendering a comprehensive review challenging due to the vast number of publications. This paper systematically reviewed 3512 articles retrieved from the Web of Science Core database between 1985 and 2022, utilizing a combination of bibliometric analysis and machine learning methods with LUCD as the main focus. The results indicated an exponential increase in the number of LUCD studies, indicating continued growth in this research field. Commonly used methods include classification-based, threshold-based, model-based, and deep learning-based change detection, with research themes encompassing forest logging and vegetation succession, urban landscape dynamics, and biodiversity conservation and management. To build an intelligent change detection system, researchers need to develop a flexible framework that integrates data preprocessing, feature extraction, land use type interpretation, and accuracy evaluation, given the continuous evolution and application of remote sensing data, deep learning, big data, and artificial intelligence.",12,5,1050,1050,"Liu, 2023, LAND",21,"Liu, Bo;Song, Wei;Meng, Zhan;Liu, Xinwei","Liu, Bo;Song, Wei;Meng, Zhan;Liu, Xinwei",Liaoning Technical University;Chinese Academy of Sciences;Institute of Geographic Sciences and Natural Resources Research;Ministry of Natural Resources;Land Consolidation and Rehabilitation Center,https://openalex.org/W1021000864;https://openalex.org/W1499645008;https://openalex.org/W1592201228;https://openalex.org/W1601805895;https://openalex.org/W1761030516;https://openalex.org/W1767470961;https://openalex.org/W1965825034;https://openalex.org/W1972923945;https://openalex.org/W1988590943;https://openalex.org/W1989369384;https://openalex.org/W2009175701;https://openalex.org/W2011572981;https://openalex.org/W2011888094;https://openalex.org/W2012567975;https://openalex.org/W2019452249;https://openalex.org/W2030257787;https://openalex.org/W2030851497;https://openalex.org/W2030864384;https://openalex.org/W2036632898;https://openalex.org/W2036798369;https://openalex.org/W2040511722;https://openalex.org/W2055718260;https://openalex.org/W2060476649;https://openalex.org/W2062277418;https://openalex.org/W2063145744;https://openalex.org/W2083350099;https://openalex.org/W2087278589;https://openalex.org/W2092141993;https://openalex.org/W2097148950;https://openalex.org/W2100335098;https://openalex.org/W2101719682;https://openalex.org/W2102953485;https://openalex.org/W2104588681;https://openalex.org/W2104896032;https://openalex.org/W2114668774;https://openalex.org/W2117698578;https://openalex.org/W2123455699;https://openalex.org/W2134505588;https://openalex.org/W2136369781;https://openalex.org/W2137341666;https://openalex.org/W2138916851;https://openalex.org/W2139709933;https://openalex.org/W2140023211;https://openalex.org/W2140908571;https://openalex.org/W2157026765;https://openalex.org/W2160544350;https://openalex.org/W2161001197;https://openalex.org/W2161273109;https://openalex.org/W2161336494;https://openalex.org/W2165577558;https://openalex.org/W2602900933;https://openalex.org/W2604518702;https://openalex.org/W2621485502;https://openalex.org/W2725897987;https://openalex.org/W2744439755;https://openalex.org/W2755950973;https://openalex.org/W2779156595;https://openalex.org/W2792827505;https://openalex.org/W2883444262;https://openalex.org/W2884678245;https://openalex.org/W2885696803;https://openalex.org/W2899768353;https://openalex.org/W2902306014;https://openalex.org/W2904327792;https://openalex.org/W2916848715;https://openalex.org/W2925093688;https://openalex.org/W2951991161;https://openalex.org/W2968744947;https://openalex.org/W2973258416;https://openalex.org/W2976013652;https://openalex.org/W2976058877;https://openalex.org/W2979341199;https://openalex.org/W2998849113;https://openalex.org/W3000049009;https://openalex.org/W3010940523;https://openalex.org/W3036656090;https://openalex.org/W3037023836;https://openalex.org/W3084438775;https://openalex.org/W3096936835;https://openalex.org/W3102127038;https://openalex.org/W3111278950;https://openalex.org/W3114429882;https://openalex.org/W3122768880;https://openalex.org/W3133726918;https://openalex.org/W3134910218;https://openalex.org/W3139098936;https://openalex.org/W3144332889;https://openalex.org/W3160330105;https://openalex.org/W3168270921;https://openalex.org/W3169559612;https://openalex.org/W3186478549;https://openalex.org/W3197119010;https://openalex.org/W3209059809;https://openalex.org/W3212611764;https://openalex.org/W3217442030;https://openalex.org/W4200540763;https://openalex.org/W4220662878;https://openalex.org/W4220832189;https://openalex.org/W4224885909;https://openalex.org/W4281743276;https://openalex.org/W4282555586;https://openalex.org/W4291449246;https://openalex.org/W4295129484;https://openalex.org/W4295857914;https://openalex.org/W4310060537;https://openalex.org/W4310382823;https://openalex.org/W4310382826;https://openalex.org/W4320502083;https://openalex.org/W6607259140;https://openalex.org/W6647520412;https://openalex.org/W6756425435;https://openalex.org/W6757323612;https://openalex.org/W6761442410;https://openalex.org/W6767050217;https://openalex.org/W6783326637;https://openalex.org/W6791113896,"Computer science;Change detection;Deep learning;Field (mathematics);Artificial intelligence;Land use, land-use change and forestry;Data science;Preprocessor;Land use;Machine learning;Ecology",Land Use and Ecosystem Services;Remote Sensing in Agriculture;Remote-Sensing Image Classification
-OPENALEX,https://openalex.org/W4307321660,https://doi.org/10.1080/24694452.2022.2130143,,A Systematic Review of COVID-19 Geographical Research: Machine Learning and Bibliometric Approach,ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS,ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS,2022,review,en,Chinese Academy of Sciences,"The rampant COVID-19 pandemic swept the globe rapidly in 2020, causing a tremendous impact on human health and the global economy. This pandemic has stimulated an explosive increase of related studies in various disciplines, including geography, which has contributed to pandemic mitigation with a unique spatiotemporal perspective. Reviewing relevant research has implications for understanding the contribution of geography to COVID-19 research. The sheer volume of publications, however, makes the review work more challenging. Here we use the support vector machine and term frequency-inverse document frequency algorithm to identify geographical studies and bibliometrics to discover primary research themes, accelerating the systematic review of COVID-19 geographical research. We confirmed 1, 171 geographical papers about COVID-19 published from 1 January 2020 to 31 December 2021, of which a large proportion are in the areas of geographic information systems (GIS) and human geography. We identified four main research themes—the spread of the pandemic, social management, public behavior, and impacts of the pandemic—embodying the contribution of geography. Our findings show the feasibility of machine learning methods in reviewing large-scale literature and highlight the value of geography in the fight against COVID-19. This review could provide references for decision makers to formulate policies combined with spatial thinking and for scholars to find future research directions in which they can strengthen collaboration with geographers.",113,3,581,598,"Xi, 2022, ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS",23,"Xi, Jinglun;Liu, Xiaolu;Wang, Jianghao;Ling, Yao;Zhou, Chenghu","Xi, Jinglun;Liu, Xiaolu;Wang, Jianghao;Ling, Yao;Zhou, Chenghu",Chinese Academy of Sciences;Institute of Geographic Sciences and Natural Resources Research;University of Chinese Academy of Sciences,https://openalex.org/W1021000864;https://openalex.org/W1973749534;https://openalex.org/W1988273412;https://openalex.org/W2019431510;https://openalex.org/W2150874198;https://openalex.org/W2755950973;https://openalex.org/W2896775444;https://openalex.org/W2902306014;https://openalex.org/W2921659045;https://openalex.org/W3016717403;https://openalex.org/W3016927051;https://openalex.org/W3019445951;https://openalex.org/W3021755432;https://openalex.org/W3022841838;https://openalex.org/W3023029957;https://openalex.org/W3026146554;https://openalex.org/W3033503930;https://openalex.org/W3033845183;https://openalex.org/W3035597798;https://openalex.org/W3035779462;https://openalex.org/W3036828051;https://openalex.org/W3038273726;https://openalex.org/W3039144150;https://openalex.org/W3039399227;https://openalex.org/W3041985295;https://openalex.org/W3042533667;https://openalex.org/W3043220749;https://openalex.org/W3045244566;https://openalex.org/W3046822448;https://openalex.org/W3047281328;https://openalex.org/W3047456365;https://openalex.org/W3058765000;https://openalex.org/W3080452576;https://openalex.org/W3081809733;https://openalex.org/W3082062151;https://openalex.org/W3087868682;https://openalex.org/W3088139267;https://openalex.org/W3088969476;https://openalex.org/W3092274689;https://openalex.org/W3092986825;https://openalex.org/W3093121799;https://openalex.org/W3094489852;https://openalex.org/W3094643898;https://openalex.org/W3094827779;https://openalex.org/W3095667454;https://openalex.org/W3096272784;https://openalex.org/W3100188676;https://openalex.org/W3106264152;https://openalex.org/W3107819211;https://openalex.org/W3108491385;https://openalex.org/W3111564425;https://openalex.org/W3111745571;https://openalex.org/W3111767993;https://openalex.org/W3111880169;https://openalex.org/W3112073971;https://openalex.org/W3115548486;https://openalex.org/W3116298071;https://openalex.org/W3117378346;https://openalex.org/W3118643190;https://openalex.org/W3125061193;https://openalex.org/W3125897910;https://openalex.org/W3126811273;https://openalex.org/W3127125777;https://openalex.org/W3128235268;https://openalex.org/W3130026954;https://openalex.org/W3132169495;https://openalex.org/W3133767462;https://openalex.org/W3134895645;https://openalex.org/W3135690257;https://openalex.org/W3135839523;https://openalex.org/W3135896764;https://openalex.org/W3136361928;https://openalex.org/W3136669015;https://openalex.org/W3137842555;https://openalex.org/W3138200732;https://openalex.org/W3139110099;https://openalex.org/W3139385319;https://openalex.org/W3140554209;https://openalex.org/W3140918691;https://openalex.org/W3143968054;https://openalex.org/W3144945315;https://openalex.org/W3146803533;https://openalex.org/W3153907075;https://openalex.org/W3153938949;https://openalex.org/W3153954796;https://openalex.org/W3154193433;https://openalex.org/W3155699756;https://openalex.org/W3156297748;https://openalex.org/W3158895480;https://openalex.org/W3162802638;https://openalex.org/W3163076983;https://openalex.org/W3164609737;https://openalex.org/W3164766304;https://openalex.org/W3165870311;https://openalex.org/W3166174067;https://openalex.org/W3171584213;https://openalex.org/W3175964015;https://openalex.org/W3176243336;https://openalex.org/W3183607613;https://openalex.org/W3183741867;https://openalex.org/W3184526714;https://openalex.org/W3184964873;https://openalex.org/W3185864622;https://openalex.org/W3189548734;https://openalex.org/W3193258988;https://openalex.org/W3193447245;https://openalex.org/W3194700959;https://openalex.org/W3194739479;https://openalex.org/W3197255421;https://openalex.org/W3197614927;https://openalex.org/W3198092367;https://openalex.org/W3199673685;https://openalex.org/W3200799815;https://openalex.org/W3200898723;https://openalex.org/W3205521780;https://openalex.org/W3205579578;https://openalex.org/W3205857559;https://openalex.org/W3206397466;https://openalex.org/W3206979440;https://openalex.org/W3210905992;https://openalex.org/W3212449458;https://openalex.org/W3212642323;https://openalex.org/W3215812863;https://openalex.org/W3216178313;https://openalex.org/W3216289208;https://openalex.org/W4200610001;https://openalex.org/W4220790135;https://openalex.org/W4225528804;https://openalex.org/W4235271607;https://openalex.org/W4239510810,Pandemic;Bibliometrics;Globe;Coronavirus disease 2019 (COVID-19);Geography;Data science;Regional science;Geographic information system;Human geography;Social science;Sociology;Cartography;Economic geography;Computer science;Medicine;Library science,COVID-19 epidemiological studies;Data-Driven Disease Surveillance;COVID-19 Pandemic Impacts
-OPENALEX,https://openalex.org/W4393442565,https://doi.org/10.3389/fneur.2024.1374443,https://pubmed.ncbi.nlm.nih.gov/38628694,Machine learning applied to epilepsy: bibliometric and visual analysis from 2004 to 2023,FRONTIERS IN NEUROLOGY,FRONTIERS IN NEUROLOGY,2024,review,en,Zunyi Medical University,"Background: Epilepsy is one of the most common serious chronic neurological disorders, which can have a serious negative impact on individuals, families and society, and even death. With the increasing application of machine learning techniques in medicine in recent years, the integration of machine learning with epilepsy has received close attention, and machine learning has the potential to provide reliable and optimal performance for clinical diagnosis, prediction, and precision medicine in epilepsy through the use of various types of mathematical algorithms, and promises to make better parallel advances. However, no bibliometric assessment has been conducted to evaluate the scientific progress in this area. Therefore, this study aims to visually analyze the trend of the current state of research related to the application of machine learning in epilepsy through bibliometrics and visualization. Methods: Relevant articles and reviews were searched for 2004-2023 using Web of Science Core Collection database, and bibliometric analyses and visualizations were performed in VOSviewer, CiteSpace, and Bibliometrix (R-Tool of R-Studio). Results: A total of 1,284 papers related to machine learning in epilepsy were retrieved from the Wo SCC database. The number of papers shows an increasing trend year by year. These papers were mainly from 1,957 organizations in 87 countries/regions, with the majority from the United States and China. The journal with the highest number of published papers is EPILEPSIA. Acharya, U. Rajendra (Ngee Ann Polytechnic, Singapore) is the authoritative author in the field and his paper ""Deep Convolutional Neural Networks for Automated Detection and Diagnosis of Epileptic Seizures Using EEG Signals"" was the most cited. Literature and keyword analysis shows that seizure prediction, epilepsy management and epilepsy neuroimaging are current research hotspots and developments. Conclusions: This study is the first to use bibliometric methods to visualize and analyze research in areas related to the application of machine learning in epilepsy, revealing research trends and frontiers in the field. This information will provide a useful reference for epilepsy researchers focusing on machine learning.",15,,1374443,1374443,"Huo, 2024, FRONTIERS IN NEUROLOGY",14,"Huo, Qing;Luo, Xu;Xu, Zucai;Yang, Xiao-Yan","Huo, Qing;Luo, Xu;Xu, Zucai;Yang, Xiao-Yan",Zunyi Medical University,https://openalex.org/W1901616594;https://openalex.org/W1969949383;https://openalex.org/W1978437325;https://openalex.org/W2027927824;https://openalex.org/W2049600401;https://openalex.org/W2068452509;https://openalex.org/W2135216654;https://openalex.org/W2136874658;https://openalex.org/W2142164352;https://openalex.org/W2150220236;https://openalex.org/W2151801982;https://openalex.org/W2163539724;https://openalex.org/W2171707538;https://openalex.org/W2177870565;https://openalex.org/W2592509339;https://openalex.org/W2755950973;https://openalex.org/W2759483166;https://openalex.org/W2789894922;https://openalex.org/W2799610518;https://openalex.org/W2811392751;https://openalex.org/W2943120052;https://openalex.org/W2964287064;https://openalex.org/W2966085124;https://openalex.org/W2971893337;https://openalex.org/W2980967712;https://openalex.org/W3008231817;https://openalex.org/W3042619474;https://openalex.org/W3092737060;https://openalex.org/W3097480381;https://openalex.org/W3120755852;https://openalex.org/W3135507703;https://openalex.org/W3165430704;https://openalex.org/W3172067149;https://openalex.org/W3183795358;https://openalex.org/W3215570818;https://openalex.org/W4205446631;https://openalex.org/W4206765588;https://openalex.org/W4210702983;https://openalex.org/W4220720372;https://openalex.org/W4221069728;https://openalex.org/W4225985782;https://openalex.org/W4226203977;https://openalex.org/W4238591974;https://openalex.org/W4243890309;https://openalex.org/W4281257892;https://openalex.org/W4291017261;https://openalex.org/W4297102205;https://openalex.org/W4306970993;https://openalex.org/W4313704713;https://openalex.org/W4315479026;https://openalex.org/W4377013301;https://openalex.org/W4377240400;https://openalex.org/W4379473685;https://openalex.org/W4381996956;https://openalex.org/W4386096729;https://openalex.org/W4386373719;https://openalex.org/W4386485450;https://openalex.org/W4386893981;https://openalex.org/W4387654919;https://openalex.org/W4389444860;https://openalex.org/W4389940775,Bibliometrics;Epilepsy;Artificial intelligence;Web of science;Machine learning;Convolutional neural network;Computer science;Deep learning;MEDLINE;Psychology;Psychiatry;Library science;Political science,EEG and Brain-Computer Interfaces;Epilepsy research and treatment;Brain Tumor Detection and Classification
-OPENALEX,https://openalex.org/W4200116323,https://doi.org/10.3390/s21248401,https://pubmed.ncbi.nlm.nih.gov/34960494,A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities,SENSORS,SENSORS,2021,article,en,Universidad de Deusto,"Traffic accidents are of worldwide concern, as they are one of the leading causes of death globally. One policy designed to cope with them is the design and deployment of road safety systems. These aim to predict crashes based on historical records, provided by new Internet of Things (IoT) technologies, to enhance traffic flow management and promote safer roads. Increasing data availability has helped machine learning (ML) to address the prediction of crashes and their severity. The literature reports numerous contributions regarding survey papers, experimental comparisons of various techniques, and the design of new methods at the point where crash severity prediction (CSP) and ML converge. Despite such progress, and as far as we know, there are no comprehensive research articles that theoretically and practically approach the model selection problem (MSP) in CSP. Thus, this paper introduces a bibliometric analysis and experimental benchmark of ML and automated machine learning (AutoML) as a suitable approach to automatically address the MSP in CSP. Firstly, 2318 bibliographic references were consulted to identify relevant authors, trending topics, keywords evolution, and the most common ML methods used in related-case studies, which revealed an opportunity for the use AutoML in the transportation field. Then, we compared AutoML (AutoGluon, Auto-sklearn, TPOT) and ML (CatBoost, Decision Tree, Extra Trees, Gradient Boosting, Gaussian Naive Bayes, Light Gradient Boosting Machine, Random Forest) methods in three case studies using open data portals belonging to the cities of Medellín, Bogotá, and Bucaramanga in Colombia. Our experimentation reveals that AutoGluon and CatBoost are competitive and robust ML approaches to deal with various CSP problems. In addition, we concluded that general-purpose AutoML effectively supports the MSP in CSP without developing domain-focused AutoML methods for this supervised learning problem. Finally, based on the results obtained, we introduce challenges and research opportunities that the community should explore to enhance the contributions that ML and AutoML can bring to CSP and other transportation areas.",21,24,8401,8401,"Angarita-Zapata, 2021, SENSORS",28,"Angarita-Zapata, Juan S.;Maestre-Góngora, Gina;Calderín, Jenny Fajardo","Angarita-Zapata, Juan S.;Maestre-Góngora, Gina;Calderín, Jenny Fajardo",Universidad de Deusto;Universidad Cooperativa de Colombia,https://openalex.org/W575847903;https://openalex.org/W1977619318;https://openalex.org/W2007707130;https://openalex.org/W2015605078;https://openalex.org/W2038083916;https://openalex.org/W2095033980;https://openalex.org/W2129660761;https://openalex.org/W2138273245;https://openalex.org/W2151554678;https://openalex.org/W2157730700;https://openalex.org/W2165466912;https://openalex.org/W2182361439;https://openalex.org/W2190194936;https://openalex.org/W2309832917;https://openalex.org/W2460404912;https://openalex.org/W2489079084;https://openalex.org/W2518685276;https://openalex.org/W2528491735;https://openalex.org/W2586798638;https://openalex.org/W2618924819;https://openalex.org/W2745090846;https://openalex.org/W2750591756;https://openalex.org/W2755950973;https://openalex.org/W2802508687;https://openalex.org/W2884282566;https://openalex.org/W2886576602;https://openalex.org/W2897805291;https://openalex.org/W2898873842;https://openalex.org/W2899037650;https://openalex.org/W2899457449;https://openalex.org/W2901772120;https://openalex.org/W2902834302;https://openalex.org/W2913985905;https://openalex.org/W2914666594;https://openalex.org/W2917767525;https://openalex.org/W2941110559;https://openalex.org/W2944574993;https://openalex.org/W2944802617;https://openalex.org/W2947982105;https://openalex.org/W2954929116;https://openalex.org/W2963017062;https://openalex.org/W2963048283;https://openalex.org/W2973700402;https://openalex.org/W2981731882;https://openalex.org/W2991137082;https://openalex.org/W2996309201;https://openalex.org/W3000998105;https://openalex.org/W3006260484;https://openalex.org/W3006913750;https://openalex.org/W3008021512;https://openalex.org/W3010639929;https://openalex.org/W3033008316;https://openalex.org/W3038712064;https://openalex.org/W3041192002;https://openalex.org/W3043432080;https://openalex.org/W3045954046;https://openalex.org/W3046883034;https://openalex.org/W3082998439;https://openalex.org/W3091123608;https://openalex.org/W3093697928;https://openalex.org/W3096310447;https://openalex.org/W3097953753;https://openalex.org/W3103443220;https://openalex.org/W3137758634;https://openalex.org/W3139250374;https://openalex.org/W3160856016;https://openalex.org/W3197260976;https://openalex.org/W3201485800;https://openalex.org/W3206704750;https://openalex.org/W3207932876;https://openalex.org/W4213308398;https://openalex.org/W4240516790;https://openalex.org/W6744394771;https://openalex.org/W6756490639;https://openalex.org/W6759934792;https://openalex.org/W6765220052;https://openalex.org/W6766167598;https://openalex.org/W6772901213;https://openalex.org/W6773821278;https://openalex.org/W6780280805;https://openalex.org/W6785588604,Machine learning;Crash;Computer science;Random forest;Benchmark (surveying);Artificial intelligence;Decision tree;Naive Bayes classifier;Boosting (machine learning);Gradient boosting;SAFER;Operations research;Data science;Data mining;Support vector machine;Engineering;Computer security,Traffic and Road Safety;Traffic Prediction and Management Techniques;Anomaly Detection Techniques and Applications
-OPENALEX,https://openalex.org/W4321598508,https://doi.org/10.3390/su15054026,,State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary,SUSTAINABILITY,SUSTAINABILITY,2023,article,en,Aligarh Muslim University,"Academicians and practitioners have recently begun to accord Artificial Intelligence (AI) and Big Data Analytics (BDA) significant consideration when exploring emerging research trends in different fields. The technique of bibliometric review has been extensively applied to the AI and BDA literature to map out existing scholarships. We summarise 711 bibliometric articles on AI & its sub-sets and BDA published in multiple fields to identify academic disciplines with significant research contributions. We pulled bibliometric review papers from the Scopus Q1 and Q2 journal database published between 2012 and 2022. The Scopus database returned 711 documents published in journals of different disciplines from 59 countries, averaging 17.9 citations per year. Multiple software and Database Analysers were used to investigate the data and illustrate the most active scientific bibliometric indicators such as authors and co-authors, citations, co-citations, countries, institutions, journal sources, and subject areas. The USA was the most influential nation (101 documents; 5405 citations), while China was the most productive nation (204 documents; 2371 citations). The most productive institution was Symbiosis International University, India (32 documents; 4.5%). The results reveal a substantial increase in bibliometric reviews in five clusters of disciplines: (a) Business & Management, (b) Engineering and Construction, (c) Healthcare, (d) Sustainable Operations & I4.0, and (e) Tourism and Hospitality Studies, the majority of which investigate the applications and use cases of AI and BDA to address real-world problems in the field. The keyword co-occurrence in the past bibliometric analyses indicates that BDA, AI, Machine Learning, Deep Learning, NLP, Fuzzy Logic, and Expert Systems will remain conspicuous research areas in these five diverse clusters of domain areas. Therefore, this paper summarises the bibliometric reviews on AI and BDA in the fields of Business, Engineering, Healthcare, Sustainable Operations, and Hospitality Tourism and serves as a starting point for novice and experienced researchers interested in these topics.",15,5,4026,4026,"Thayyib, 2023, SUSTAINABILITY",148,"Thayyib, P. V.;Mamilla, Rajesh;Khan, Mohsin;Khan, Mohsin;Fatima, Humaira;Asim, Mohammed;Anwar, Imran;Shamsudheen, M. K.;Khan, Mohd. Asif;Khan, Mohd. Asif","Thayyib, P. V.;Mamilla, Rajesh;Khan, Mohsin;Khan, Mohsin;Fatima, Humaira;Asim, Mohammed;Anwar, Imran;Shamsudheen, M. K.;Khan, Mohd. Asif;Khan, Mohd. Asif",Vellore Institute of Technology University;Aligarh Muslim University;Chandigarh University;University of Kerala,https://openalex.org/W229682254;https://openalex.org/W1001370459;https://openalex.org/W1494192115;https://openalex.org/W1976453866;https://openalex.org/W2016387931;https://openalex.org/W2025053102;https://openalex.org/W2041100749;https://openalex.org/W2047914690;https://openalex.org/W2071096576;https://openalex.org/W2091064058;https://openalex.org/W2117871237;https://openalex.org/W2128083427;https://openalex.org/W2131307433;https://openalex.org/W2141975087;https://openalex.org/W2143959207;https://openalex.org/W2154655801;https://openalex.org/W2157954477;https://openalex.org/W2173213060;https://openalex.org/W2215252103;https://openalex.org/W2232810130;https://openalex.org/W2261525379;https://openalex.org/W2275696275;https://openalex.org/W2280321044;https://openalex.org/W2288572574;https://openalex.org/W2295827386;https://openalex.org/W2330029115;https://openalex.org/W2337145305;https://openalex.org/W2337976471;https://openalex.org/W2397575900;https://openalex.org/W2403237691;https://openalex.org/W2416848540;https://openalex.org/W2471432754;https://openalex.org/W2514873829;https://openalex.org/W2519706319;https://openalex.org/W2534263193;https://openalex.org/W2550329658;https://openalex.org/W2571253853;https://openalex.org/W2576404523;https://openalex.org/W2586281947;https://openalex.org/W2588559096;https://openalex.org/W2591793331;https://openalex.org/W2594043246;https://openalex.org/W2605366653;https://openalex.org/W2608983138;https://openalex.org/W2620092997;https://openalex.org/W2726150830;https://openalex.org/W2735332871;https://openalex.org/W2755950973;https://openalex.org/W2783089003;https://openalex.org/W2783127227;https://openalex.org/W2792712441;https://openalex.org/W2793350944;https://openalex.org/W2797694788;https://openalex.org/W2801722167;https://openalex.org/W2804537022;https://openalex.org/W2883543619;https://openalex.org/W2899595320;https://openalex.org/W2899856450;https://openalex.org/W2903619742;https://openalex.org/W2904029666;https://openalex.org/W2906422317;https://openalex.org/W2908094560;https://openalex.org/W2909924818;https://openalex.org/W2911311605;https://openalex.org/W2912791769;https://openalex.org/W2922512717;https://openalex.org/W2931283717;https://openalex.org/W2952912936;https://openalex.org/W2953301966;https://openalex.org/W2953468444;https://openalex.org/W2957520325;https://openalex.org/W2958974105;https://openalex.org/W2961273696;https://openalex.org/W2963849010;https://openalex.org/W2968216620;https://openalex.org/W2969536608;https://openalex.org/W2969625533;https://openalex.org/W2979085846;https://openalex.org/W2979610116;https://openalex.org/W2979906316;https://openalex.org/W2980079839;https://openalex.org/W2982980691;https://openalex.org/W2983519043;https://openalex.org/W2986617680;https://openalex.org/W3002229233;https://openalex.org/W3006963679;https://openalex.org/W3009452520;https://openalex.org/W3010600010;https://openalex.org/W3011931926;https://openalex.org/W3014057684;https://openalex.org/W3019570724;https://openalex.org/W3021613067;https://openalex.org/W3025370095;https://openalex.org/W3034465384;https://openalex.org/W3041382323;https://openalex.org/W3042710080;https://openalex.org/W3085940513;https://openalex.org/W3087259180;https://openalex.org/W3102444842;https://openalex.org/W3109259686;https://openalex.org/W3115894432;https://openalex.org/W3116890626;https://openalex.org/W3117942735;https://openalex.org/W3125603166;https://openalex.org/W3128384299;https://openalex.org/W3131345956;https://openalex.org/W3131937156;https://openalex.org/W3138237914;https://openalex.org/W3140761414;https://openalex.org/W3160856016;https://openalex.org/W3170554175;https://openalex.org/W3175889490;https://openalex.org/W3184641885;https://openalex.org/W3185086149;https://openalex.org/W3191737511;https://openalex.org/W3195662367;https://openalex.org/W3198357836;https://openalex.org/W3210604023;https://openalex.org/W3212784166;https://openalex.org/W3215605706;https://openalex.org/W4220842325;https://openalex.org/W4224037372;https://openalex.org/W4225126411;https://openalex.org/W4225305016;https://openalex.org/W4245952513;https://openalex.org/W4283795305;https://openalex.org/W4288456457;https://openalex.org/W4292166681;https://openalex.org/W4296056389;https://openalex.org/W4306407804;https://openalex.org/W4382987554;https://openalex.org/W6660703778;https://openalex.org/W6673054594;https://openalex.org/W6688549355;https://openalex.org/W6695147765;https://openalex.org/W6720264217;https://openalex.org/W6787889959;https://openalex.org/W6790787248;https://openalex.org/W6792136125;https://openalex.org/W6799918696;https://openalex.org/W6800479145,Scopus;Big data;Bibliometrics;China;Web of science;Hospitality;Analytics;Library science;Computer science;Data science;Political science;Tourism;MEDLINE;Data mining,Big Data and Business Intelligence;Imbalanced Data Classification Techniques;Artificial Intelligence in Healthcare
-OPENALEX,https://openalex.org/W4404110584,https://doi.org/10.3390/electronics13224352,,Machine Learning and Deep Learning Applications in Disinformation Detection: A Bibliometric Assessment,ELECTRONICS,ELECTRONICS,2024,article,en,Bucharest University of Economic Studies,"Fake news is one of the biggest challenging issues in today’s technological world and has a huge impact on the population’s decision-making and way of thinking. Disinformation can be classified as a subdivision of fake news, the main purpose of which is to manipulate and generate confusion among people in order to influence their opinion and obtain certain advantages in multiple domains (politics, economics, etc.). Propaganda, rumors, and conspiracy theories are just a few examples of common disinformation. Therefore, there is an urgent need to understand this phenomenon and offer the scientific community a paper that provides a comprehensive examination of the existing literature, lay the foundation for future research areas, and contribute to the fight against disinformation. The present manuscript provides a detailed bibliometric analysis of the articles oriented towards disinformation detection, involving high-performance machine learning and deep learning algorithms. The dataset has been collected from the popular Web of Science database, through the use of specific keywords such as “disinformation”, “machine learning”, or “deep learning”, followed by a manual check of the papers included in the dataset. The documents were examined using the popular R tool, Biblioshiny 4.2.0; the bibliometric analysis included multiple perspectives and various facets: dataset overview, sources, authors, papers, n-gram analysis, and mixed analysis. The results highlight an increased interest from the scientific community on disinformation topics in the context of machine learning and deep learning, supported by an annual growth rate of 96.1%. The insights gained from the research bring to light surprising details, while the study provides a solid basis for both future research in this area, as well for the development of new strategies addressing this complex issue of disinformation and ensuring a trustworthy and safe online environment.",13,22,4352,4352,"Sandu, 2024, ELECTRONICS",13,"Sandu, Andra;Cotfas, Liviu‐Adrian;Delcea, Camelia;Ioanăş, Corina;Florescu, Margareta‐Stela;Orzan, Mihai","Sandu, Andra;Cotfas, Liviu‐Adrian;Delcea, Camelia;Ioanăş, Corina;Florescu, Margareta‐Stela;Orzan, Mihai",Bucharest University of Economic Studies,https://openalex.org/W1802683368;https://openalex.org/W2106988983;https://openalex.org/W2189279266;https://openalex.org/W2263682169;https://openalex.org/W2571108916;https://openalex.org/W2584429674;https://openalex.org/W2744152618;https://openalex.org/W2755950973;https://openalex.org/W2901966191;https://openalex.org/W2921404976;https://openalex.org/W2935692497;https://openalex.org/W2977083636;https://openalex.org/W2978591550;https://openalex.org/W3009409260;https://openalex.org/W3013255332;https://openalex.org/W3018745840;https://openalex.org/W3019200173;https://openalex.org/W3024620668;https://openalex.org/W3027404439;https://openalex.org/W3027499708;https://openalex.org/W3028000264;https://openalex.org/W3040042046;https://openalex.org/W3093871831;https://openalex.org/W3094608436;https://openalex.org/W3095725379;https://openalex.org/W3112211286;https://openalex.org/W3115311952;https://openalex.org/W3125803510;https://openalex.org/W3127402721;https://openalex.org/W3131648434;https://openalex.org/W3135809943;https://openalex.org/W3155668196;https://openalex.org/W3158874961;https://openalex.org/W3168293258;https://openalex.org/W3193054117;https://openalex.org/W3203412342;https://openalex.org/W3207732087;https://openalex.org/W3210850996;https://openalex.org/W3216145030;https://openalex.org/W4200022165;https://openalex.org/W4200048988;https://openalex.org/W4205145976;https://openalex.org/W4206289281;https://openalex.org/W4206419127;https://openalex.org/W4206518851;https://openalex.org/W4206775153;https://openalex.org/W4206994887;https://openalex.org/W4210519269;https://openalex.org/W4212778779;https://openalex.org/W4213327489;https://openalex.org/W4221040532;https://openalex.org/W4224041794;https://openalex.org/W4224259327;https://openalex.org/W4224313013;https://openalex.org/W4280610708;https://openalex.org/W4281398834;https://openalex.org/W4284961720;https://openalex.org/W4288077742;https://openalex.org/W4289108963;https://openalex.org/W4289711818;https://openalex.org/W4290098476;https://openalex.org/W4292722554;https://openalex.org/W4306392724;https://openalex.org/W4307804863;https://openalex.org/W4310398021;https://openalex.org/W4311372995;https://openalex.org/W4311793477;https://openalex.org/W4312186169;https://openalex.org/W4312415160;https://openalex.org/W4313524100;https://openalex.org/W4323350915;https://openalex.org/W4365149444;https://openalex.org/W4375955734;https://openalex.org/W4385380836;https://openalex.org/W4385544249;https://openalex.org/W4385623762;https://openalex.org/W4385756293;https://openalex.org/W4385759973;https://openalex.org/W4387100119;https://openalex.org/W4387194023;https://openalex.org/W4387308347;https://openalex.org/W4387407166;https://openalex.org/W4387552651;https://openalex.org/W4387653485;https://openalex.org/W4387668982;https://openalex.org/W4387671508;https://openalex.org/W4387856280;https://openalex.org/W4388328186;https://openalex.org/W4388478659;https://openalex.org/W4388705549;https://openalex.org/W4388836287;https://openalex.org/W4389049809;https://openalex.org/W4389050210;https://openalex.org/W4389686096;https://openalex.org/W4389916022;https://openalex.org/W4391034917;https://openalex.org/W4391575974;https://openalex.org/W4391718339;https://openalex.org/W4391816773;https://openalex.org/W4391836808;https://openalex.org/W4391855222;https://openalex.org/W4391931465;https://openalex.org/W4392167882;https://openalex.org/W4392739981;https://openalex.org/W4393142360;https://openalex.org/W4393982624;https://openalex.org/W4394603970;https://openalex.org/W4394837077;https://openalex.org/W4395110605;https://openalex.org/W4397026401;https://openalex.org/W4398254678;https://openalex.org/W4399215659;https://openalex.org/W4399700542;https://openalex.org/W4400088271;https://openalex.org/W4400763998;https://openalex.org/W4401063616;https://openalex.org/W4401193759;https://openalex.org/W4401731573;https://openalex.org/W4403122938;https://openalex.org/W6687039334;https://openalex.org/W6754367067;https://openalex.org/W6776225016;https://openalex.org/W6790573878;https://openalex.org/W6870482965,Disinformation;Computer science;Artificial intelligence;Deep learning;Machine learning;Natural language processing;Data science;World Wide Web;Social media,Misinformation and Its Impacts;Big Data and Business Intelligence
-OPENALEX,https://openalex.org/W3210804500,https://doi.org/10.3390/math9212792,,Bibliometrics of Machine Learning Research Using Homomorphic Encryption,MATHEMATICS,MATHEMATICS,2021,article,en,Zhejiang Wanli University,"Since the first fully homomorphic encryption scheme was published in 2009, many papers have been published on fully homomorphic encryption and its applications. Machine learning is one of the most interesting applications and has drawn a lot of attention from researchers. To better represent and understand the field of Homomorphic Encryption in Machine Learning (HEML), this paper utilizes automated citation and topic analysis to characterize the HEML research literature over the years and provide the bibliometrics assessments for this burgeoning field. This is conducted by using a bibliometric statistical analysis approach. We make use of web-based literature databases and automated tools to present the development of HEML. This allows us to target several popular topics for in-depth discussion. To achieve these goals, we have chosen the well-established Scopus literature database and analyzed them through keyword counts and Scopus relevance searches. The results show a relative increase in the number of papers published each year that involve both homomorphic cryptography and machine learning. Using text mining of articles titles, we have found that cloud computing is a popular topic in this field, which also includes neural networks, big data, and the Internet of Things. The analysis results show that China, the US, and India have generated almost half of all the research contributions in HEML. The citation statistics, keyword statistics, and topic analyses give us a quick overview of the development of the field, which can be of great help to new researchers. It is also possible to apply our methodology to other research areas, and we see great value in this approach.",9,21,2792,2792,"Chen, 2021, MATHEMATICS",13,"Chen, Zhigang;Hu, Gang;Zheng, Mengce;Song, Xinxia;Chen, Liqun","Chen, Zhigang;Hu, Gang;Zheng, Mengce;Song, Xinxia;Chen, Liqun",Zhejiang Wanli University;University of Surrey,https://openalex.org/W50045167;https://openalex.org/W133884053;https://openalex.org/W1621599745;https://openalex.org/W1971286892;https://openalex.org/W1971991172;https://openalex.org/W1999051528;https://openalex.org/W2118006580;https://openalex.org/W2435473771;https://openalex.org/W2597998853;https://openalex.org/W2604462068;https://openalex.org/W2770638201;https://openalex.org/W2781091734;https://openalex.org/W2783769334;https://openalex.org/W2798479296;https://openalex.org/W2801958627;https://openalex.org/W2889746123;https://openalex.org/W2896938420;https://openalex.org/W2897925395;https://openalex.org/W2900763147;https://openalex.org/W2924179447;https://openalex.org/W2951527632;https://openalex.org/W2955023110;https://openalex.org/W2963106566;https://openalex.org/W2966536036;https://openalex.org/W2967497108;https://openalex.org/W2971122390;https://openalex.org/W2979524102;https://openalex.org/W2982499393;https://openalex.org/W2983065655;https://openalex.org/W2987932087;https://openalex.org/W3040364935;https://openalex.org/W3079221395;https://openalex.org/W3100617755;https://openalex.org/W3183960220;https://openalex.org/W6639805184;https://openalex.org/W6757619422;https://openalex.org/W6763982081;https://openalex.org/W6780329937,Computer science;Scopus;Homomorphic encryption;Bibliometrics;Field (mathematics);Data science;Citation;Encryption;Relevance (law);The Internet;Information retrieval;Data mining;Artificial intelligence;World Wide Web;Computer security;Mathematics,Cryptography and Data Security;Privacy-Preserving Technologies in Data;Nanocluster Synthesis and Applications
-OPENALEX,https://openalex.org/W4376627022,https://doi.org/10.1108/s1569-37592023000110a010,,Artificial Intelligence and Machine Learning in Insurance: A Bibliometric Analysis,CONTEMPORARY STUDIES IN ECONOMIC AND FINANCIAL ANALYSIS,CONTEMPORARY STUDIES IN ECONOMIC AND FINANCIAL ANALYSIS,2023,book-chapter,en,,"Purpose: The aim of this chapter is to provide a quantitative literature review on machine learning (ML) and artificial intelligence (AI) in the Insurance Sector.Need for the Study: The current study maps the literature regarding AI and ML in the insurance sector through bibliometric tools to identify the significant gaps in the available literature, considerable insights that emerged, and a scientific evaluation of AI and ML in the Insurance sector.Methodology: The VOS viewer method was used to conduct the depth and quantitative analysis of the AI and ML in Insurance. The study of 450 articles has been retrieved through the Scopus database from 2012 to 2021. The implication of performance analysis methods has helped to explore influential journals, authors, countries, Keywords, and affiliations, elevating the literature in AI and the Insurance Sector.Finding: This study conducts an exploratory analysis and identifies the prominent authors, sources, countries, affiliations, and articles using modern bibliometric analysis (BA) tools. The geographic scattering of the study indicates that the USA and the UK have highly influential publications and contribute to AI and Insurance. East and Southern Asia countries are far behind.Practical Implication: Furthermore, this chapter can be used as a reference paper to explore the new field of study in the insurance sector using AI. The search criteria were set in the study to limit the sample published papers/articles included in Scopus data based on the AI and ML in Insurance.",,,191,202,"Kumar, 2023, CONTEMPORARY STUDIES IN ECONOMIC AND FINANCIAL ANALYSIS",15,"Kumar, Praveen;Taneja, Sanjay;Özen, Ercan;Singh, Satinderpal","Kumar, Praveen;Taneja, Sanjay;Özen, Ercan;Singh, Satinderpal",,https://openalex.org/W1978071138;https://openalex.org/W2275696275;https://openalex.org/W2560122591;https://openalex.org/W2891520355;https://openalex.org/W2914735843;https://openalex.org/W2922260383;https://openalex.org/W3013109186;https://openalex.org/W3112196969;https://openalex.org/W3135398899;https://openalex.org/W3135409708;https://openalex.org/W3201845915;https://openalex.org/W3204425738;https://openalex.org/W3210870265,Scopus;Exploratory analysis;Exploratory research;Artificial intelligence;Actuarial science;Computer science;Data science;Political science;Business;Social science;Sociology,Blockchain Technology Applications and Security;Insurance and Financial Risk Management
-OPENALEX,https://openalex.org/W3129429979,https://doi.org/10.1049/cim2.12004,,Machine learning‐based scheduling: a bibliometric perspective,IET COLLABORATIVE INTELLIGENT MANUFACTURING,IET COLLABORATIVE INTELLIGENT MANUFACTURING,2021,article,en,Zhejiang University of Technology,"Abstract In recent years, the rapid development of artificial intelligence and data science has given rise to the study of data driven algorithms in highly volatile systems. The scheduling of complex shop floor resources falls into such a category, which is often non‐linear in nature, time varying, multi‐objective, and subject to interruptions. Ergo, the machine learning‐based scheduling, has become a research hotspot and attracted the attention of many scholars. In the literature, the research methods employed in solving scheduling problems are based on various perspectives, such as mathematical programming, combinatorial optimization, and heuristic rules. However, due to the inherent complexity of the problem, many issues remain to be addressed. In particular, with the availability of production data, the progress of computing power, and the breakthrough in intelligent algorithms, a novel branch of data driven algorithms present great potential, for example, the deep learning and reinforcement learning‐based algorithms. To reveal the value of machine learning‐based scheduling methods, bibliometric analysis was conducted to analyse the relevant articles and documents from the year 1980 to 2019. Finally, the future research trend in the domain of machine learning‐based scheduling is considered and tips are provided for researchers as well as practitioners to find leading scientists for collaborations.",3,2,131,146,"Li, 2021, IET COLLABORATIVE INTELLIGENT MANUFACTURING",12,"Li, Shiyun;Yu, Tianzong;Cao, Xu;Pei, Zhi;Yi, Wenchao;Chen, Yong;Lv, Ruifeng","Li, Shiyun;Yu, Tianzong;Cao, Xu;Pei, Zhi;Yi, Wenchao;Chen, Yong;Lv, Ruifeng",Zhejiang University of Technology,https://openalex.org/W1509562426;https://openalex.org/W1966804431;https://openalex.org/W1976491873;https://openalex.org/W1980111888;https://openalex.org/W1983974888;https://openalex.org/W1985921566;https://openalex.org/W1987261608;https://openalex.org/W1990699111;https://openalex.org/W1993853979;https://openalex.org/W1994940961;https://openalex.org/W1997200022;https://openalex.org/W2000630887;https://openalex.org/W2002152339;https://openalex.org/W2004438130;https://openalex.org/W2009100149;https://openalex.org/W2009274717;https://openalex.org/W2009423917;https://openalex.org/W2011234500;https://openalex.org/W2020813379;https://openalex.org/W2030236912;https://openalex.org/W2031638734;https://openalex.org/W2051945096;https://openalex.org/W2068770142;https://openalex.org/W2076998221;https://openalex.org/W2086640479;https://openalex.org/W2093851652;https://openalex.org/W2094870107;https://openalex.org/W2099245132;https://openalex.org/W2099929785;https://openalex.org/W2108673915;https://openalex.org/W2119717200;https://openalex.org/W2120386704;https://openalex.org/W2122842398;https://openalex.org/W2124715093;https://openalex.org/W2133940713;https://openalex.org/W2141503635;https://openalex.org/W2147466788;https://openalex.org/W2158703918;https://openalex.org/W2169337357;https://openalex.org/W2198244262;https://openalex.org/W2323284870;https://openalex.org/W2343935207;https://openalex.org/W2532994954;https://openalex.org/W2548055351;https://openalex.org/W2571075193;https://openalex.org/W2618749399;https://openalex.org/W2790861445;https://openalex.org/W2804251482;https://openalex.org/W2805268279;https://openalex.org/W2810684840;https://openalex.org/W2896868750;https://openalex.org/W2898302070;https://openalex.org/W2900896126;https://openalex.org/W2908084908;https://openalex.org/W2911964244;https://openalex.org/W2915564669;https://openalex.org/W2916944508;https://openalex.org/W2919115771;https://openalex.org/W2941916820;https://openalex.org/W2942715783;https://openalex.org/W2952137279;https://openalex.org/W2952578973;https://openalex.org/W2963078014;https://openalex.org/W2971858640;https://openalex.org/W2974201054;https://openalex.org/W2982143798;https://openalex.org/W2982919137;https://openalex.org/W2988096906;https://openalex.org/W2993936449;https://openalex.org/W2997873913;https://openalex.org/W3005335721;https://openalex.org/W3012656115;https://openalex.org/W3017771540;https://openalex.org/W3035619533;https://openalex.org/W3041202696;https://openalex.org/W3043202537;https://openalex.org/W3047863327;https://openalex.org/W3049468855;https://openalex.org/W3079713928;https://openalex.org/W4249253882,Computer science;Scheduling (production processes);Artificial intelligence;Reinforcement learning;Machine learning;Data science;Industrial engineering;Operations research;Mathematical optimization;Mathematics;Engineering,Scheduling and Optimization Algorithms;Advanced Manufacturing and Logistics Optimization;Optimization and Search Problems
-OPENALEX,https://openalex.org/W4406337842,https://doi.org/10.3389/frsus.2024.1508647,,Artificial intelligence and machine learning in production efficiency enhancement and sustainable development: a comprehensive bibliometric review,FRONTIERS IN SUSTAINABILITY,FRONTIERS IN SUSTAINABILITY,2025,article,en,University of Macedonia,"This research presents a comprehensive bibliometric review of the role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing production efficiency and fostering sustainable development. With the increasing focus on sustainability, AI and ML technologies have emerged as pivotal tools for optimizing industrial processes, improving resource management and minimizing environmental impacts. The study analyzes key ML algorithms in various production settings. This study conducts systematic bibliometric analysis using the Scopus database and Bibliometrix R package, examining global trends, key collaborations, and thematic focuses on AI and ML applications in production efficiency and sustainable development. Novel contributions include uncovering underexplored ethical dimensions of AI adoption and emphasizing the pivotal role of SMEs and developing economies in advancing sustainable practices. Key research trends identified include the integration of AI with sustainable energy management, circular economy practices, and precision agriculture. Furthermore, the analysis reveals geographical contributions, with countries like China, the United States, and the United Kingdom leading in research output and impact. Despite the promising advancements, the review identifies gaps in ethical considerations, especially in data privacy and labor market implications, and suggests avenues for future research, including the implementation of AI and ML in developing economies and Small and Medium Enterprises (SMEs).",5,,,,"Bitzenis, 2025, FRONTIERS IN SUSTAINABILITY",20,"Bitzenis, Aristidis;Κουτσουπιάς, Νίκος;Nosios, Marios","Bitzenis, Aristidis;Κουτσουπιάς, Νίκος;Nosios, Marios",University of Macedonia,https://openalex.org/W1901616594;https://openalex.org/W2122410182;https://openalex.org/W2742982421;https://openalex.org/W2747467399;https://openalex.org/W2942835563;https://openalex.org/W2995645234;https://openalex.org/W3007397514;https://openalex.org/W3011089232;https://openalex.org/W3011617363;https://openalex.org/W3039419186;https://openalex.org/W3127908559;https://openalex.org/W3137875885;https://openalex.org/W3160856016;https://openalex.org/W3183728074;https://openalex.org/W3197680002;https://openalex.org/W4214717370;https://openalex.org/W4220959955;https://openalex.org/W4283205652;https://openalex.org/W4291197252;https://openalex.org/W4378882744;https://openalex.org/W4386953199;https://openalex.org/W4389155033;https://openalex.org/W4389483579;https://openalex.org/W4390236512;https://openalex.org/W4398231304;https://openalex.org/W4401266758;https://openalex.org/W4402494219;https://openalex.org/W6870296669,Scopus;Sustainability;Sustainable development;Production (economics);Big data;Resource efficiency;Knowledge management;Business;Computer science;Political science;Economics,"Digital Transformation in Industry;Sustainable Supply Chain Management;Energy, Environment, Economic Growth"
-OPENALEX,https://openalex.org/W4307812418,https://doi.org/10.1080/1573062x.2022.2138460,,Machine learning solutions in sewer systems: a bibliometric analysis,URBAN WATER JOURNAL,URBAN WATER JOURNAL,2022,article,en,Universitat de Lleida,"The use of machine learning solutions has been rising recently, and the water domain is reaping several benefits from its application. However, there is still room in the literature regarding machine learning applied to sewer systems. In this article, we study applied solutions to the predictive problem of four factors in the sewer: pipe defects, sedimentation, and failure and blockage events. Even with the number of publications available to solve each problem, there is still a need for improvement. This article aims to identify existing literature gaps through a bibliometric analysis based on data extracted from Scopus and Web of Science. Results show an increasing trend in published papers studying the domain and identify different knowledge gaps within the literature related to the correct use of data, the need for models capable of generalization, and the identification of novel techniques to be studied in the future.",20,1,1,14,"Ribalta, 2022, URBAN WATER JOURNAL",13,"Ribalta, Marc;Béjar, Ramón;Mateu, Carles;Rubión, Edgar","Ribalta, Marc;Béjar, Ramón;Mateu, Carles;Rubión, Edgar",Universitat de Lleida,https://openalex.org/W1490567082;https://openalex.org/W1560739766;https://openalex.org/W1576808520;https://openalex.org/W1975029787;https://openalex.org/W1991176479;https://openalex.org/W2012444155;https://openalex.org/W2024766536;https://openalex.org/W2033210535;https://openalex.org/W2038212585;https://openalex.org/W2054409147;https://openalex.org/W2076201204;https://openalex.org/W2077967845;https://openalex.org/W2087016914;https://openalex.org/W2089886004;https://openalex.org/W2090855180;https://openalex.org/W2110242546;https://openalex.org/W2154446579;https://openalex.org/W2165698076;https://openalex.org/W2166844739;https://openalex.org/W2247602019;https://openalex.org/W2298535137;https://openalex.org/W2349467391;https://openalex.org/W2514297841;https://openalex.org/W2572939427;https://openalex.org/W2739176025;https://openalex.org/W2740801217;https://openalex.org/W2771878080;https://openalex.org/W2792741217;https://openalex.org/W2887280559;https://openalex.org/W2889035772;https://openalex.org/W2891399420;https://openalex.org/W2902255491;https://openalex.org/W2953888523;https://openalex.org/W2968126678;https://openalex.org/W2979085846;https://openalex.org/W2979610116;https://openalex.org/W2982512126;https://openalex.org/W2986617680;https://openalex.org/W2998284959;https://openalex.org/W2999010889;https://openalex.org/W3001491100;https://openalex.org/W3006433450;https://openalex.org/W3015046602;https://openalex.org/W3016192360;https://openalex.org/W3044744503;https://openalex.org/W3046405119;https://openalex.org/W3048116889;https://openalex.org/W3081464307;https://openalex.org/W3093551863;https://openalex.org/W3094335619;https://openalex.org/W3096348630;https://openalex.org/W3103112322;https://openalex.org/W3106832382;https://openalex.org/W3113227330;https://openalex.org/W3119096079;https://openalex.org/W3131948008;https://openalex.org/W3134462217;https://openalex.org/W3137876849;https://openalex.org/W3141992354;https://openalex.org/W3160856016;https://openalex.org/W3164103262;https://openalex.org/W3179346037;https://openalex.org/W3182134864;https://openalex.org/W3186709726;https://openalex.org/W3209750664;https://openalex.org/W3214778491;https://openalex.org/W3216759837;https://openalex.org/W4205954259;https://openalex.org/W4211237505;https://openalex.org/W4213194009;https://openalex.org/W4214925094;https://openalex.org/W4221078344;https://openalex.org/W4229008245;https://openalex.org/W4242297404;https://openalex.org/W4286640356;https://openalex.org/W4310895557,Scopus;Computer science;Identification (biology);Domain (mathematical analysis);Machine learning;Generalization;Sanitary sewer;Data science;Data mining;Artificial intelligence;Engineering;Environmental engineering;Mathematics,Infrastructure Maintenance and Monitoring;Water Systems and Optimization;Urban Stormwater Management Solutions
-OPENALEX,https://openalex.org/W4391575974,https://doi.org/10.3390/a17020070,,Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research,ALGORITHMS,ALGORITHMS,2024,article,en,Bucharest University of Economic Studies,"Fake news is an explosive subject, being undoubtedly among the most controversial and difficult challenges facing society in the present-day environment of technology and information, which greatly affects the individuals who are vulnerable and easily influenced, shaping their decisions, actions, and even beliefs. In the course of discussing the gravity and dissemination of the fake news phenomenon, this article aims to clarify the distinctions between fake news, misinformation, and disinformation, along with conducting a thorough analysis of the most widely read academic papers that have tackled the topic of fake news research using various machine learning techniques. Utilizing specific keywords for dataset extraction from Clarivate Analytics’ Web of Science Core Collection, the bibliometric analysis spans six years, offering valuable insights aimed at identifying key trends, methodologies, and notable strategies within this multidisciplinary field. The analysis encompasses the examination of prolific authors, prominent journals, collaborative efforts, prior publications, covered subjects, keywords, bigrams, trigrams, theme maps, co-occurrence networks, and various other relevant topics. One noteworthy aspect related to the extracted dataset is the remarkable growth rate observed in association with the analyzed subject, indicating an impressive increase of 179.31%. The growth rate value, coupled with the relatively short timeframe, further emphasizes the research community’s keen interest in this subject. In light of these findings, the paper draws attention to key contributions and gaps in the existing literature, providing researchers and decision-makers innovative viewpoints and perspectives on the ongoing battle against the spread of fake news in the age of information.",17,2,70,70,"Sandu, 2024, ALGORITHMS",22,"Sandu, Andra;Ioanăş, Ioana;Delcea, Camelia;Florescu, Margareta‐Stela;Cotfas, Liviu‐Adrian","Sandu, Andra;Ioanăş, Ioana;Delcea, Camelia;Florescu, Margareta‐Stela;Cotfas, Liviu‐Adrian",Bucharest University of Economic Studies;University of Bucharest,https://openalex.org/W83921790;https://openalex.org/W2263682169;https://openalex.org/W2744152618;https://openalex.org/W2755950973;https://openalex.org/W2778443828;https://openalex.org/W2791544114;https://openalex.org/W2883370365;https://openalex.org/W2906570840;https://openalex.org/W2945928904;https://openalex.org/W2977083636;https://openalex.org/W2981194182;https://openalex.org/W2995481985;https://openalex.org/W3001895040;https://openalex.org/W3030685711;https://openalex.org/W3038930935;https://openalex.org/W3043445890;https://openalex.org/W3045984998;https://openalex.org/W3094229442;https://openalex.org/W3107058614;https://openalex.org/W3110713756;https://openalex.org/W3112211286;https://openalex.org/W3119467012;https://openalex.org/W3124863924;https://openalex.org/W3129318751;https://openalex.org/W3131345956;https://openalex.org/W3135727734;https://openalex.org/W3155668196;https://openalex.org/W3158874961;https://openalex.org/W3169337282;https://openalex.org/W3179526882;https://openalex.org/W3193226555;https://openalex.org/W3203412342;https://openalex.org/W4200563494;https://openalex.org/W4206369292;https://openalex.org/W4206419127;https://openalex.org/W4206775153;https://openalex.org/W4206994887;https://openalex.org/W4210511847;https://openalex.org/W4220779961;https://openalex.org/W4221034982;https://openalex.org/W4221040532;https://openalex.org/W4281989533;https://openalex.org/W4289711818;https://openalex.org/W4290098476;https://openalex.org/W4294940193;https://openalex.org/W4295846679;https://openalex.org/W4306392724;https://openalex.org/W4308869726;https://openalex.org/W4311372995;https://openalex.org/W4319983294;https://openalex.org/W4378515574;https://openalex.org/W4385593217;https://openalex.org/W4385759973;https://openalex.org/W4385954993;https://openalex.org/W4387100119;https://openalex.org/W4387668982;https://openalex.org/W4387671508;https://openalex.org/W4387806574;https://openalex.org/W4387856280;https://openalex.org/W4388304633;https://openalex.org/W4388723715;https://openalex.org/W4389049809;https://openalex.org/W4389686096;https://openalex.org/W4391034917;https://openalex.org/W6752939354;https://openalex.org/W6798657162,Computer science;Artificial intelligence;Fake news;Machine learning;Data science;Internet privacy,Misinformation and Its Impacts;Sentiment Analysis and Opinion Mining
-OPENALEX,https://openalex.org/W4402761213,https://doi.org/10.3233/hsm-240114,,Artificial intelligence and machine learning in corporate governance: A bibliometric analysis,HUMAN SYSTEMS MANAGEMENT,HUMAN SYSTEMS MANAGEMENT,2024,article,en,Universitat de València,"BACKGROUND: The study deeply explores the thriving domains of artificial intelligence (AI) and machine learning (ML) in corporate governance. OBJECTIVE: The study aims to thoroughly examine the rapidly developing fields of artificial intelligence (AI) and machine learning (ML) in corporate governance. METHODS: After completing an in-depth analysis of 229 research studies published between 2008 and 2023 (using software tools such as RStudio, VOSviewer, and Excel),), the study reveals a notable increase in publications since 2022. Corporate social responsibility (CSR), environmental, social, and governance (ESG) issues, executive remuneration, and sustainability are all considered as important key focal areas of focus. Scholars in this field are notably at the forefront from Taiwan, the United States, and China. IMPLICATIONS: However, the study stress the necessity for further researches to estimate the efficacy of different AI and ML methodologies. This may guide evidence-based governance practices various industries and geographical areas.",,,1,27,"Samara, 2024, HUMAN SYSTEMS MANAGEMENT",37,"Samara, Husni;Qudah, Hanan Ahmad;Mohsin, Hayder Jerri;Abualhijad, Seba;Hani, Laith Yousef Bani;rahamneh, Samer Al;AlQudah, Mohammad Zakaria","Samara, Husni;Qudah, Hanan Ahmad;Mohsin, Hayder Jerri;Abualhijad, Seba;Hani, Laith Yousef Bani;rahamneh, Samer Al;AlQudah, Mohammad Zakaria",Universitat de València;Al-Balqa Applied University;Southern Technical University;Princess Sumaya University for Technology;Andhra University;Universidad de Extremadura,https://openalex.org/W1498422751;https://openalex.org/W1542807197;https://openalex.org/W1940628912;https://openalex.org/W1987341880;https://openalex.org/W1997838466;https://openalex.org/W2042060819;https://openalex.org/W2096039739;https://openalex.org/W2114971411;https://openalex.org/W2150220236;https://openalex.org/W2166114234;https://openalex.org/W2168835522;https://openalex.org/W2171160632;https://openalex.org/W2592084954;https://openalex.org/W2604344292;https://openalex.org/W2737757376;https://openalex.org/W2769143121;https://openalex.org/W2784332084;https://openalex.org/W2801233141;https://openalex.org/W2963453445;https://openalex.org/W2969247272;https://openalex.org/W2979433608;https://openalex.org/W2981297782;https://openalex.org/W2981537393;https://openalex.org/W3043958357;https://openalex.org/W3080588292;https://openalex.org/W3089470783;https://openalex.org/W3110723264;https://openalex.org/W3111916360;https://openalex.org/W3121464887;https://openalex.org/W3122862660;https://openalex.org/W3123935160;https://openalex.org/W3124400623;https://openalex.org/W3124978547;https://openalex.org/W3125062024;https://openalex.org/W3125614333;https://openalex.org/W3157495551;https://openalex.org/W3167169082;https://openalex.org/W3167204998;https://openalex.org/W3169861144;https://openalex.org/W3172631954;https://openalex.org/W3195390651;https://openalex.org/W3197899567;https://openalex.org/W3205533322;https://openalex.org/W4200403900;https://openalex.org/W4206630697;https://openalex.org/W4211004689;https://openalex.org/W4220677682;https://openalex.org/W4220740304;https://openalex.org/W4225120170;https://openalex.org/W4281671474;https://openalex.org/W4283465583;https://openalex.org/W4285988371;https://openalex.org/W4294837616;https://openalex.org/W4304186503;https://openalex.org/W4308747777;https://openalex.org/W4310128742;https://openalex.org/W4360856906;https://openalex.org/W4379932132;https://openalex.org/W4380031413;https://openalex.org/W4380258939;https://openalex.org/W4385278262;https://openalex.org/W4385356576;https://openalex.org/W4388798770;https://openalex.org/W4390948925;https://openalex.org/W4392461758;https://openalex.org/W4400119734;https://openalex.org/W6741631311;https://openalex.org/W6751209984;https://openalex.org/W6781162693;https://openalex.org/W6782074629;https://openalex.org/W6794601679;https://openalex.org/W6796787185;https://openalex.org/W6796882001;https://openalex.org/W6855387159;https://openalex.org/W6860802656,Corporate governance;Artificial intelligence;Business;Computer science;Knowledge management;Finance,"Innovation, Sustainability, Human-Machine Systems;Economic and Technological Systems Analysis"
-OPENALEX,https://openalex.org/W3087259180,https://doi.org/10.1016/j.psep.2020.09.038,,Review and analysis of supervised machine learning algorithms for hazardous events in drilling operations,PROCESS SAFETY AND ENVIRONMENTAL PROTECTION,PROCESS SAFETY AND ENVIRONMENTAL PROTECTION,2020,article,en,Memorial University of Newfoundland,,147,,367,384,"Osarogiagbon, 2020, PROCESS SAFETY AND ENVIRONMENTAL PROTECTION",114,"Osarogiagbon, Augustine Uhunoma;Khan, Faisal;Venkatesan, R.;Gillard, Paul","Osarogiagbon, Augustine Uhunoma;Khan, Faisal;Venkatesan, R.;Gillard, Paul",Memorial University of Newfoundland,https://openalex.org/W782754607;https://openalex.org/W1423339008;https://openalex.org/W1501213224;https://openalex.org/W1569512666;https://openalex.org/W1825077972;https://openalex.org/W1886561810;https://openalex.org/W1924770834;https://openalex.org/W1968755086;https://openalex.org/W1975169837;https://openalex.org/W1976932268;https://openalex.org/W1985258458;https://openalex.org/W1986880669;https://openalex.org/W1995391341;https://openalex.org/W2014694450;https://openalex.org/W2019509828;https://openalex.org/W2021088555;https://openalex.org/W2048571789;https://openalex.org/W2058738197;https://openalex.org/W2059121759;https://openalex.org/W2064675550;https://openalex.org/W2071709160;https://openalex.org/W2078257268;https://openalex.org/W2079486651;https://openalex.org/W2099471712;https://openalex.org/W2106525823;https://openalex.org/W2122410182;https://openalex.org/W2159115511;https://openalex.org/W2165698076;https://openalex.org/W2185950796;https://openalex.org/W2189116525;https://openalex.org/W2297589637;https://openalex.org/W2319521731;https://openalex.org/W2395579298;https://openalex.org/W2471451605;https://openalex.org/W2521861943;https://openalex.org/W2547743818;https://openalex.org/W2559785631;https://openalex.org/W2565516711;https://openalex.org/W2587037909;https://openalex.org/W2587043449;https://openalex.org/W2596116135;https://openalex.org/W2604319603;https://openalex.org/W2609368436;https://openalex.org/W2619927053;https://openalex.org/W2623293810;https://openalex.org/W2745794750;https://openalex.org/W2745868649;https://openalex.org/W2753389842;https://openalex.org/W2755428343;https://openalex.org/W2765811365;https://openalex.org/W2767547957;https://openalex.org/W2782924405;https://openalex.org/W2783454406;https://openalex.org/W2792420347;https://openalex.org/W2793699999;https://openalex.org/W2796925382;https://openalex.org/W2800017313;https://openalex.org/W2802067151;https://openalex.org/W2802626656;https://openalex.org/W2803649798;https://openalex.org/W2805669461;https://openalex.org/W2807998075;https://openalex.org/W2890023743;https://openalex.org/W2892341857;https://openalex.org/W2894187817;https://openalex.org/W2894965144;https://openalex.org/W2895978636;https://openalex.org/W2896840749;https://openalex.org/W2898433953;https://openalex.org/W2902390267;https://openalex.org/W2908257321;https://openalex.org/W2915542680;https://openalex.org/W2916018064;https://openalex.org/W2919115771;https://openalex.org/W2919358988;https://openalex.org/W2922367262;https://openalex.org/W2922490156;https://openalex.org/W2923730465;https://openalex.org/W2961504393;https://openalex.org/W2964248614;https://openalex.org/W2965683868;https://openalex.org/W2972666782;https://openalex.org/W2972899043;https://openalex.org/W2973061192;https://openalex.org/W2973829091;https://openalex.org/W2974328964;https://openalex.org/W2974665869;https://openalex.org/W2995786994;https://openalex.org/W2998768810;https://openalex.org/W3001110193;https://openalex.org/W3003329782;https://openalex.org/W3005942841;https://openalex.org/W3007097374;https://openalex.org/W3007997088;https://openalex.org/W3014377569;https://openalex.org/W3015728455;https://openalex.org/W3100789280;https://openalex.org/W3149428932;https://openalex.org/W4206307994;https://openalex.org/W4232982358;https://openalex.org/W4285719527;https://openalex.org/W4320013936;https://openalex.org/W6628124331;https://openalex.org/W6639296852;https://openalex.org/W6683613195;https://openalex.org/W6686339323;https://openalex.org/W6687432044;https://openalex.org/W6699921544;https://openalex.org/W6754316506;https://openalex.org/W6755474284;https://openalex.org/W6755518326;https://openalex.org/W6757702690;https://openalex.org/W6760175995;https://openalex.org/W6767766622;https://openalex.org/W6771577556;https://openalex.org/W6773429937;https://openalex.org/W6774030523;https://openalex.org/W6774134758,Machine learning;Artificial intelligence;Algorithm;Computer science;Drilling engineering;Deep learning;Artificial neural network;Support vector machine;Drilling;Engineering,Drilling and Well Engineering;Oil and Gas Production Techniques;Hydraulic Fracturing and Reservoir Analysis
-OPENALEX,https://openalex.org/W4396747232,https://doi.org/10.1007/s44217-024-00119-5,,Analyzing the evolution of machine learning integration in educational research: a bibliometric perspective,DISCOVER EDUCATION,DISCOVER EDUCATION,2024,article,en,University of Johannesburg,"Abstract Machine learning, a subset of artificial intelligence, has experienced rapid advancements and applications across various domains. In education, its integration holds great potential to revolutionize teaching, learning, and educational outcomes. Despite the growing interest, there needs to be more comprehensive bibliometric analyses that track the trajectory of machine learning’s integration into educational research. This study addresses this gap by providing a nuanced perspective derived from bibliometric insights. Using a dataset from 1986 to 2022, consisting of 449 documents from 145 sources retrieved from the Web of Science (WoS), the research employs network analysis to unveil collaborative clusters and identify influential authors. A temporal analysis of annual research output sheds light on evolving trends, while a thematic content analysis explores prevalent research themes through keyword frequency. The findings reveal that co-authorship network analysis exposes distinct clusters and influential figures shaping the landscape of machine learning in educational research. Scientific production over time reveals a significant surge in research output, indicating the field’s maturation. The co-occurrence analysis emphasizes a collective focus on student-centric outcomes and technology integration, with terms like “online” and “analytics” prevailing. This study provides a nuanced understanding of the collaborative and thematic fabric characterizing machine learning in educational research. The implications derived from the findings guide strategic collaborations, emphasizing the importance of cross-disciplinary engagement. Recommendations include investing in technological infrastructure and prioritizing student-centric research. The study contributes foundational insights to inform future endeavors in this ever-evolving field.",3,1,,,"Ayanwale, 2024, DISCOVER EDUCATION",14,"Ayanwale, Musa Adekunle;Molefi, Rethabile Rosemary;Oyeniran, Saheed","Ayanwale, Musa Adekunle;Molefi, Rethabile Rosemary;Oyeniran, Saheed",University of Johannesburg;National University of Lesotho;University of Ilorin,https://openalex.org/W2026048037;https://openalex.org/W2086848096;https://openalex.org/W2088037265;https://openalex.org/W2135455887;https://openalex.org/W2167872491;https://openalex.org/W2277805675;https://openalex.org/W2755950973;https://openalex.org/W2794750682;https://openalex.org/W2905031825;https://openalex.org/W2942193471;https://openalex.org/W2980673614;https://openalex.org/W2981863007;https://openalex.org/W2990688366;https://openalex.org/W3001491100;https://openalex.org/W3008957588;https://openalex.org/W3013838778;https://openalex.org/W3032511898;https://openalex.org/W3044169273;https://openalex.org/W3090741237;https://openalex.org/W3103420222;https://openalex.org/W3111498440;https://openalex.org/W3122391591;https://openalex.org/W3130503865;https://openalex.org/W3139118922;https://openalex.org/W3149370927;https://openalex.org/W3160856016;https://openalex.org/W3163935305;https://openalex.org/W3170811104;https://openalex.org/W3208482597;https://openalex.org/W4200401025;https://openalex.org/W4200553326;https://openalex.org/W4225296409;https://openalex.org/W4308320805;https://openalex.org/W4312205357;https://openalex.org/W4313445099;https://openalex.org/W4361010247;https://openalex.org/W4366988470;https://openalex.org/W4367187551;https://openalex.org/W4367394483;https://openalex.org/W4375949969;https://openalex.org/W4378908836;https://openalex.org/W4380685454;https://openalex.org/W4381327689;https://openalex.org/W4384563724;https://openalex.org/W4387886270;https://openalex.org/W6885045909,Perspective (graphical);Computer science;Data science;Sociology;Mathematics education;Artificial intelligence;Psychology,Online Learning and Analytics;Big Data and Business Intelligence;scientometrics and bibliometrics research
-OPENALEX,https://openalex.org/W4229015798,https://doi.org/10.1108/jbim-07-2021-0332,,Artificial intelligence in customer relationship management: literature review and future research directions,JOURNAL OF BUSINESS AND INDUSTRIAL MARKETING,JOURNAL OF BUSINESS AND INDUSTRIAL MARKETING,2022,article,en,University of Padua,"Purpose Due to the recent development of Big Data and artificial intelligence (AI) technology solutions in customer relationship management (CRM), this paper provides a systematic overview of the field, thus unveiling gaps and providing promising paths for future research. Design/methodology/approach A total of 212 peer-reviewed articles published between 1989 and 2020 were extracted from the Scopus database, and 2 bibliometric techniques were used: bibliographic coupling and keywords’ co-occurrence. Findings Outcomes of the bibliometric analysis enabled the authors to identify three main subfields of the AI literature within the CRM domain (Big Data and CRM as a database, AI and machine learning techniques applied to CRM activities and strategic management of AI–CRM integrations) and capture promising paths for future development for each of these subfields. This study also develops a three-step conceptual model for AI implementation in CRM, which can support, on one hand, scholars in further deepening the knowledge in this field and, on the other hand, managers in planning an appropriate and coherent strategy. Originality/value To the best of the authors’ knowledge, this study is the first to systematise and discuss the literature regarding the relationship between AI and CRM based on bibliometric analysis. Thus, both academics and practitioners can benefit from the study, as it unveils recent important directions in CRM management research and practices.",37,13,48,63,"Ledro, 2022, JOURNAL OF BUSINESS AND INDUSTRIAL MARKETING",175,"Ledro, Cristina;Nosella, Anna;Vinelli, Andrea","Ledro, Cristina;Nosella, Anna;Vinelli, Andrea",University of Padua,https://openalex.org/W264248106;https://openalex.org/W1479241889;https://openalex.org/W1481030120;https://openalex.org/W1531714299;https://openalex.org/W1909123760;https://openalex.org/W1966482890;https://openalex.org/W1980126189;https://openalex.org/W2017680781;https://openalex.org/W2055410913;https://openalex.org/W2056621186;https://openalex.org/W2099582074;https://openalex.org/W2104719746;https://openalex.org/W2129936978;https://openalex.org/W2131635820;https://openalex.org/W2150220236;https://openalex.org/W2186658092;https://openalex.org/W2275696275;https://openalex.org/W2312364390;https://openalex.org/W2422895071;https://openalex.org/W2470320792;https://openalex.org/W2470629814;https://openalex.org/W2486781253;https://openalex.org/W2510113590;https://openalex.org/W2515884889;https://openalex.org/W2531322043;https://openalex.org/W2560286635;https://openalex.org/W2601223312;https://openalex.org/W2620092997;https://openalex.org/W2769091608;https://openalex.org/W2784258506;https://openalex.org/W2792101231;https://openalex.org/W2794775296;https://openalex.org/W2804274048;https://openalex.org/W2886880619;https://openalex.org/W2888790491;https://openalex.org/W2893472614;https://openalex.org/W2899856450;https://openalex.org/W2901065167;https://openalex.org/W2906755608;https://openalex.org/W2912521856;https://openalex.org/W2921107389;https://openalex.org/W2947337027;https://openalex.org/W2951579601;https://openalex.org/W2953773016;https://openalex.org/W2956609189;https://openalex.org/W2963453445;https://openalex.org/W2969701135;https://openalex.org/W2971246084;https://openalex.org/W2974124990;https://openalex.org/W2980049533;https://openalex.org/W2981288736;https://openalex.org/W2984319529;https://openalex.org/W2991491450;https://openalex.org/W2995034539;https://openalex.org/W2995580029;https://openalex.org/W2995630456;https://openalex.org/W2997954837;https://openalex.org/W2998528801;https://openalex.org/W2999274754;https://openalex.org/W3001124561;https://openalex.org/W3002626430;https://openalex.org/W3005778784;https://openalex.org/W3008204822;https://openalex.org/W3008875928;https://openalex.org/W3011865677;https://openalex.org/W3013577653;https://openalex.org/W3013660926;https://openalex.org/W3015444426;https://openalex.org/W3021283416;https://openalex.org/W3024262366;https://openalex.org/W3027721908;https://openalex.org/W3027937825;https://openalex.org/W3033465599;https://openalex.org/W3033679711;https://openalex.org/W3036356806;https://openalex.org/W3036605171;https://openalex.org/W3040670154;https://openalex.org/W3043182100;https://openalex.org/W3081261125;https://openalex.org/W3085480867;https://openalex.org/W3100936220;https://openalex.org/W3108348876;https://openalex.org/W3114900208;https://openalex.org/W3124536214;https://openalex.org/W3125505924;https://openalex.org/W3125707221;https://openalex.org/W3138237914;https://openalex.org/W3160856016;https://openalex.org/W3199165842;https://openalex.org/W4255497883;https://openalex.org/W4322486585,Originality;Knowledge management;Scopus;Customer relationship management;Field (mathematics);Computer science;Big data;Bibliographic coupling;Data science;Business;Database;Sociology;Political science;Data mining;World Wide Web;Citation,Customer Service Quality and Loyalty;Employer Branding and e-HRM;Digital Marketing and Social Media
-OPENALEX,https://openalex.org/W4391034571,https://doi.org/10.3390/ai5010012,,Bibliometric Mining of Research Trends in Machine Learning,AI,AI,2024,article,en,Blekinge Institute of Technology,"We present a method, including tool support, for bibliometric mining of trends in large and dynamic research areas. The method is applied to the machine learning research area for the years 2013 to 2022. A total number of 398,782 documents from Scopus were analyzed. A taxonomy containing 26 research directions within machine learning was defined by four experts with the help of a Python program and existing taxonomies. The trends in terms of productivity, growth rate, and citations were analyzed for the research directions in the taxonomy. Our results show that the two directions, Applications and Algorithms, are the largest, and that the direction Convolutional Neural Networks is the one that grows the fastest and has the highest average number of citations per document. It also turns out that there is a clear correlation between the growth rate and the average number of citations per document, i.e., documents in fast-growing research directions have more citations. The trends for machine learning research in four geographic regions (North America, Europe, the BRICS countries, and The Rest of the World) were also analyzed. The number of documents during the time period considered is approximately the same for all regions. BRICS has the highest growth rate, and, on average, North America has the highest number of citations per document. Using our tool and method, we expect that one could perform a similar study in some other large and dynamic research area in a relatively short time.",5,1,208,236,"Lundberg, 2024, AI",11,"Lundberg, Lars;Boldt, Martin;Borg, Anton;Grahn, Håkan","Lundberg, Lars;Boldt, Martin;Borg, Anton;Grahn, Håkan",Blekinge Institute of Technology,https://openalex.org/W1988388208;https://openalex.org/W2024932032;https://openalex.org/W2025365931;https://openalex.org/W2040153632;https://openalex.org/W2040966276;https://openalex.org/W2101234009;https://openalex.org/W2106956101;https://openalex.org/W2136166259;https://openalex.org/W2187244164;https://openalex.org/W2621121024;https://openalex.org/W2772451958;https://openalex.org/W2889666927;https://openalex.org/W2954057334;https://openalex.org/W2983519043;https://openalex.org/W3006087551;https://openalex.org/W3008105883;https://openalex.org/W3016533114;https://openalex.org/W3035953096;https://openalex.org/W3037654077;https://openalex.org/W3118240050;https://openalex.org/W3135534765;https://openalex.org/W3181204626;https://openalex.org/W3193226555;https://openalex.org/W3195428376;https://openalex.org/W3198357836;https://openalex.org/W3216722782;https://openalex.org/W4200115204;https://openalex.org/W4200116323;https://openalex.org/W4200118840;https://openalex.org/W4221076181;https://openalex.org/W4224037372;https://openalex.org/W4224246713;https://openalex.org/W4234556776;https://openalex.org/W4250336363;https://openalex.org/W4280604520;https://openalex.org/W4285236136;https://openalex.org/W4285797191;https://openalex.org/W4293254815;https://openalex.org/W4299512326;https://openalex.org/W4308391725;https://openalex.org/W4308700471;https://openalex.org/W4317743287;https://openalex.org/W4318462788;https://openalex.org/W4319441392;https://openalex.org/W4327698564;https://openalex.org/W4362522290;https://openalex.org/W4366588036;https://openalex.org/W4367394483;https://openalex.org/W4367671427;https://openalex.org/W4379089507;https://openalex.org/W4382810577;https://openalex.org/W4385337062;https://openalex.org/W4386609378;https://openalex.org/W4386803046;https://openalex.org/W6675354045;https://openalex.org/W6680398342;https://openalex.org/W6788256713;https://openalex.org/W6805120973;https://openalex.org/W6810640808,Python (programming language);Scopus;Computer science;Artificial intelligence;Annual growth %;Data science;Machine learning;Geography;Information retrieval;Political science,Big Data and Business Intelligence;Explainable Artificial Intelligence (XAI);Forecasting Techniques and Applications
-OPENALEX,https://openalex.org/W4385423139,https://doi.org/10.1002/sd.2706,,Artificial intelligence for Sustainable Development Goals: Bibliometric patterns and concept evolution trajectories,SUSTAINABLE DEVELOPMENT,SUSTAINABLE DEVELOPMENT,2023,article,en,Banaras Hindu University,"Abstract The development of artificial intelligence (AI) as a field has impacted almost all aspects of human life. More recently it has found a role in addressing developmental challenges, specifically the Sustainable Development Goals (SDGs). However, there are not enough systematic studies on analysis of the role of AI research towards the SDGs. Therefore, this article attempts to bridge this gap by identifying the major bibliometric trends and concept‐evolution trajectories in the area of AI applications for sustainable‐development goals. The research publication data for the last 20 years in the areas of artificial intelligence, machine learning, deep learning, and so forth, is obtained and computationally analysed using a framework comprising bibliometrics, path analysis and content analysis. The findings show an incremental trend in overall publications on the application of AI for SDGs across the different regions of the world. SDGs 3 (good health & well‐being) and 7 (affordable and clean energy) are found as the areas with the most applications of AI. In SDG3, the literature reflects application of AI techniques such as deep learning for precision and personalised medicine while in SDG7, a number of studies have employed AI techniques for the integration of systems for efficient generation of solar power and improving the energy efficiency of a building. Furthermore, SDG 4 (quality education), SDG 13 (climate action), SDG 11 (sustainable cities and communities) and SDG 16 (peace, justice and strong institutions) are the other SDGs where AI approaches and techniques are applied. The analytical results present a detailed insight of application of AI for achieving the SDGs.",32,1,724,754,"Singh, 2023, SUSTAINABLE DEVELOPMENT",173,"Singh, Aakash;Kanaujia, Anurag;Singh, Vivek Kumar;Vinuesa, Ricardo","Singh, Aakash;Kanaujia, Anurag;Singh, Vivek Kumar;Vinuesa, Ricardo",Banaras Hindu University;KTH Royal Institute of Technology,https://openalex.org/W204184694;https://openalex.org/W1517881847;https://openalex.org/W1964723091;https://openalex.org/W1968924621;https://openalex.org/W1979723077;https://openalex.org/W1984703120;https://openalex.org/W1987337578;https://openalex.org/W1990134054;https://openalex.org/W2035061933;https://openalex.org/W2051607409;https://openalex.org/W2064469609;https://openalex.org/W2069433056;https://openalex.org/W2070080270;https://openalex.org/W2070885076;https://openalex.org/W2088209891;https://openalex.org/W2121468917;https://openalex.org/W2127387319;https://openalex.org/W2137006212;https://openalex.org/W2155046806;https://openalex.org/W2161742217;https://openalex.org/W2170548780;https://openalex.org/W2311340678;https://openalex.org/W2472333518;https://openalex.org/W2560204283;https://openalex.org/W2569349941;https://openalex.org/W2745770579;https://openalex.org/W2754252319;https://openalex.org/W2760506659;https://openalex.org/W2763355946;https://openalex.org/W2767128594;https://openalex.org/W2784499877;https://openalex.org/W2785011159;https://openalex.org/W2790482354;https://openalex.org/W2893178486;https://openalex.org/W2896957338;https://openalex.org/W2945976633;https://openalex.org/W2953638632;https://openalex.org/W2963849010;https://openalex.org/W2981731882;https://openalex.org/W2985105610;https://openalex.org/W3000603264;https://openalex.org/W3011149445;https://openalex.org/W3032868513;https://openalex.org/W3080298681;https://openalex.org/W3089021530;https://openalex.org/W3097763105;https://openalex.org/W3098949126;https://openalex.org/W3116890626;https://openalex.org/W3121184641;https://openalex.org/W3125559012;https://openalex.org/W3126217793;https://openalex.org/W3135629427;https://openalex.org/W3140968591;https://openalex.org/W3160560894;https://openalex.org/W3160856016;https://openalex.org/W3194444240;https://openalex.org/W3208493710;https://openalex.org/W3215126599;https://openalex.org/W4360977132;https://openalex.org/W4377107836,Sustainable development;Bibliometrics;Artificial intelligence;Computer science;Field (mathematics);Management science;Data science;Political science;Engineering;Data mining;Mathematics,"Smart Cities and Technologies;Energy, Environment, Economic Growth;Explainable Artificial Intelligence (XAI)"
-OPENALEX,https://openalex.org/W4309461041,https://doi.org/10.3390/jrfm15110535,,A Bibliometric Analysis of Machine Learning Econometrics in Asset Pricing,JOURNAL OF RISK AND FINANCIAL MANAGEMENT,JOURNAL OF RISK AND FINANCIAL MANAGEMENT,2022,article,en,Louisiana State University Agricultural Center,"Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better suited for, problems of prediction. While some ML methods have been applied in econometrics for decades, their success in prediction has been limited, and examples of this abound in the asset pricing literature. In recent years, the ML literature has advanced new, more efficient, computation methods for regularization, modeling nonlinearity, and improved out-of-sample prediction. This article conducted a comprehensive, objective, and quantitative bibliometric analysis of this growing literature using Web of Science (WoS) data. We identified trends in the literature over the past decade, the geographical distribution of articles, authorship, and institutional contributions worldwide. The paper also identifies the dominant literature using citations in WoS and discusses computational algorithms that are expanding the econometric frontiers in asset pricing. The top cited papers were reviewed, highlighting their contribution. The limitations of ML learning methods and recent advances in ML were used to provide a conic view to future ML econometric practice.",15,11,535,535,"Zapata, 2022, JOURNAL OF RISK AND FINANCIAL MANAGEMENT",14,"Zapata, Héctor O.;Mukhopadhyay, Supratik","Zapata, Héctor O.;Mukhopadhyay, Supratik",Louisiana State University Agricultural Center;Louisiana State University,https://openalex.org/W189742998;https://openalex.org/W585891580;https://openalex.org/W1560724230;https://openalex.org/W1828120854;https://openalex.org/W1965520378;https://openalex.org/W1970001627;https://openalex.org/W2002890723;https://openalex.org/W2059852492;https://openalex.org/W2062896473;https://openalex.org/W2086994642;https://openalex.org/W2110603299;https://openalex.org/W2125943170;https://openalex.org/W2128722040;https://openalex.org/W2131055507;https://openalex.org/W2134407776;https://openalex.org/W2138083400;https://openalex.org/W2144499799;https://openalex.org/W2165698076;https://openalex.org/W2178225550;https://openalex.org/W2478307678;https://openalex.org/W2586702902;https://openalex.org/W2594182135;https://openalex.org/W2604765399;https://openalex.org/W2610886376;https://openalex.org/W2734498027;https://openalex.org/W2755950973;https://openalex.org/W2794022442;https://openalex.org/W2832688480;https://openalex.org/W2971672318;https://openalex.org/W2994536315;https://openalex.org/W3001366352;https://openalex.org/W3033755685;https://openalex.org/W3040959470;https://openalex.org/W3096831136;https://openalex.org/W3098509316;https://openalex.org/W3103443220;https://openalex.org/W3120045749;https://openalex.org/W3122648113;https://openalex.org/W3122785968;https://openalex.org/W3123671536;https://openalex.org/W3126973451;https://openalex.org/W3170454297;https://openalex.org/W4205539948;https://openalex.org/W4220662281;https://openalex.org/W4230661391;https://openalex.org/W4239488419;https://openalex.org/W4241115065;https://openalex.org/W4285466868;https://openalex.org/W4300567850;https://openalex.org/W4385795614;https://openalex.org/W6607672814;https://openalex.org/W6657548618;https://openalex.org/W6747397434;https://openalex.org/W6807091881,Econometrics;Capital asset pricing model;Computer science;Inference;Financial econometrics;Econometric model;Asset (computer security);Machine learning;Economics;Artificial intelligence;Finance;Financial analysis,Financial Markets and Investment Strategies;Stock Market Forecasting Methods;Complex Systems and Time Series Analysis
-OPENALEX,https://openalex.org/W3124960274,https://doi.org/10.1021/acsomega.0c05591,https://pubmed.ncbi.nlm.nih.gov/33553934,Using Bibliometric Analysis and Machine Learning to Identify Compounds Binding to Sialidase-1,ACS OMEGA,ACS OMEGA,2021,article,en,Collaborations Pharmaceuticals (United States),"8.88 ± 4.02 μM), which validated our approach to identifying new molecules binding to this protein, which could represent possible drug candidates that can be evaluated further as potential chaperones for this ultrarare lysosomal disease for which there is currently no treatment. Combining bibliometric and machine learning approaches has the ability to assist in curating small molecule data and model building, respectively, for rare disease drug discovery. This approach also has the capability to identify new compounds that are potential drug candidates.",6,4,3186,3193,"Klein, 2021, ACS OMEGA",15,"Klein, Jennifer J.;Baker, Nancy;Foil, Daniel H.;Zorn, Kimberley M.;Urbina, Fabio;Puhl, Ana C.;Ekins, Sean","Klein, Jennifer J.;Baker, Nancy;Foil, Daniel H.;Zorn, Kimberley M.;Urbina, Fabio;Puhl, Ana C.;Ekins, Sean",Collaborations Pharmaceuticals (United States),https://openalex.org/W342897826;https://openalex.org/W1733233436;https://openalex.org/W1971129897;https://openalex.org/W1971192989;https://openalex.org/W2016575780;https://openalex.org/W2026584056;https://openalex.org/W2031826717;https://openalex.org/W2039451511;https://openalex.org/W2042711932;https://openalex.org/W2048317526;https://openalex.org/W2053154970;https://openalex.org/W2053643033;https://openalex.org/W2054904603;https://openalex.org/W2075095334;https://openalex.org/W2109553965;https://openalex.org/W2110818108;https://openalex.org/W2115864709;https://openalex.org/W2124846732;https://openalex.org/W2135293364;https://openalex.org/W2142109861;https://openalex.org/W2153804780;https://openalex.org/W2300160852;https://openalex.org/W2401677822;https://openalex.org/W2415953308;https://openalex.org/W2423770865;https://openalex.org/W2423833949;https://openalex.org/W2426863919;https://openalex.org/W2461563124;https://openalex.org/W2555971108;https://openalex.org/W2558999090;https://openalex.org/W2622236128;https://openalex.org/W2781993106;https://openalex.org/W2801291332;https://openalex.org/W2801347829;https://openalex.org/W2810207189;https://openalex.org/W2887039457;https://openalex.org/W2898047885;https://openalex.org/W2900668971;https://openalex.org/W2901618012;https://openalex.org/W2908654150;https://openalex.org/W2918140740;https://openalex.org/W2929961692;https://openalex.org/W2940242941;https://openalex.org/W2973267506;https://openalex.org/W2994844251;https://openalex.org/W3000443609;https://openalex.org/W3028130337;https://openalex.org/W4230927156;https://openalex.org/W4234505218;https://openalex.org/W4242365118,Sialidase;Computer science;Chemistry;Computational biology;Machine learning;Artificial intelligence;Biochemistry;Biology;Neuraminidase;Enzyme,Glycosylation and Glycoproteins Research;Lysosomal Storage Disorders Research;Studies on Chitinases and Chitosanases
-OPENALEX,https://openalex.org/W4399326707,https://doi.org/10.1016/j.atech.2024.100483,,Enhancing precision agriculture: A comprehensive review of machine learning and AI vision applications in all-terrain vehicle for farm automation,SMART AGRICULTURAL TECHNOLOGY,SMART AGRICULTURAL TECHNOLOGY,2024,review,en,Assam University,"The automation of all-terrain vehicles (ATVs) through the integration of advanced technologies such as machine learning (ML) and artificial intelligence (AI) vision has significantly changed precision agriculture. This paper aims to analyse and develop trends to provide comprehensive knowledge of the current state of ATV-based precision agriculture and the future possibilities of ML and AI. A bibliometric analysis was conducted through network diagram with keywords taken from previous publications in the domain. This review comprehensively analyses the potential of machine learning and artificial intelligence in transforming farming operations through the automation of tasks and the deployment of all-terrain vehicles. The research extensively analyses how machine learning methods have influenced several aspects of agricultural activities, such as planting, harvesting, spraying, weeding, crop monitoring, and others. AI vision systems are being researched for their ability to enhance precise and prompt decision-making in ATV-driven agricultural automation. These technologies have been thoroughly tested to show how they can improve crop yield, reducing overall investment, and make farming more efficient. Examples include machine learning-based seeding accuracy, AI-enabled crop health monitoring, and the use of AI vision for accurate pesticide application. The assessment examines challenges such as data privacy problems and scalability constraints, along with potential advancements and future prospects in the field. This will assist researchers and practitioners in making well-informed judgments regarding farming practices that are efficient, sustainable, and technologically robust.",8,,100483,100483,"Padhiary, 2024, SMART AGRICULTURAL TECHNOLOGY",183,"Padhiary, Mrutyunjay;Saha, Debapam;Kumar, Raushan;Sethi, Laxmi Narayan;Kumar, Avinash","Padhiary, Mrutyunjay;Saha, Debapam;Kumar, Raushan;Sethi, Laxmi Narayan;Kumar, Avinash",Assam University;Indian Institute of Technology Kharagpur,https://openalex.org/W1995463142;https://openalex.org/W2010255117;https://openalex.org/W2019168270;https://openalex.org/W2021496016;https://openalex.org/W2031775731;https://openalex.org/W2125592902;https://openalex.org/W2252452514;https://openalex.org/W2529578931;https://openalex.org/W2591965935;https://openalex.org/W2598645336;https://openalex.org/W2620819300;https://openalex.org/W2621194016;https://openalex.org/W2736427881;https://openalex.org/W2764276830;https://openalex.org/W2765366036;https://openalex.org/W2885770726;https://openalex.org/W2888801627;https://openalex.org/W2898543370;https://openalex.org/W2899867782;https://openalex.org/W2900330501;https://openalex.org/W2906706532;https://openalex.org/W2910307221;https://openalex.org/W2916244613;https://openalex.org/W2939435175;https://openalex.org/W2941748068;https://openalex.org/W2943983563;https://openalex.org/W2946809859;https://openalex.org/W2950814059;https://openalex.org/W2966528149;https://openalex.org/W2969625533;https://openalex.org/W2971480543;https://openalex.org/W2993044411;https://openalex.org/W2995946162;https://openalex.org/W3004921674;https://openalex.org/W3005029250;https://openalex.org/W3007397514;https://openalex.org/W3008057074;https://openalex.org/W3012071850;https://openalex.org/W3015774841;https://openalex.org/W3019464069;https://openalex.org/W3019576236;https://openalex.org/W3029468131;https://openalex.org/W3039878613;https://openalex.org/W3041549894;https://openalex.org/W3048302016;https://openalex.org/W3080577972;https://openalex.org/W3087879900;https://openalex.org/W3088154325;https://openalex.org/W3088348166;https://openalex.org/W3096859136;https://openalex.org/W3101819905;https://openalex.org/W3119187382;https://openalex.org/W3119438583;https://openalex.org/W3119938903;https://openalex.org/W3121342653;https://openalex.org/W3128126482;https://openalex.org/W3134295130;https://openalex.org/W3135028703;https://openalex.org/W3142507861;https://openalex.org/W3152804902;https://openalex.org/W3153990350;https://openalex.org/W3155739706;https://openalex.org/W3156943756;https://openalex.org/W3159310484;https://openalex.org/W3173822881;https://openalex.org/W3179864921;https://openalex.org/W3180424200;https://openalex.org/W3186848791;https://openalex.org/W3189663779;https://openalex.org/W3195556073;https://openalex.org/W3199489893;https://openalex.org/W3216900205;https://openalex.org/W3217492654;https://openalex.org/W4200423153;https://openalex.org/W4206342482;https://openalex.org/W4213273581;https://openalex.org/W4225156945;https://openalex.org/W4226252355;https://openalex.org/W4243293843;https://openalex.org/W4244664184;https://openalex.org/W4249116893;https://openalex.org/W4253566740;https://openalex.org/W4255954202;https://openalex.org/W4281690062;https://openalex.org/W4281804579;https://openalex.org/W4283820772;https://openalex.org/W4285788992;https://openalex.org/W4285794763;https://openalex.org/W4288514369;https://openalex.org/W4289335852;https://openalex.org/W4289792395;https://openalex.org/W4290999919;https://openalex.org/W4295007588;https://openalex.org/W4295953483;https://openalex.org/W4298417552;https://openalex.org/W4306770082;https://openalex.org/W4307486628;https://openalex.org/W4309078364;https://openalex.org/W4312635606;https://openalex.org/W4313458650;https://openalex.org/W4317423857;https://openalex.org/W4317733647;https://openalex.org/W4361252974;https://openalex.org/W4366598229;https://openalex.org/W4380841429;https://openalex.org/W4381251904;https://openalex.org/W4382466596;https://openalex.org/W4382651820;https://openalex.org/W4384827812;https://openalex.org/W4385285736;https://openalex.org/W4385320121;https://openalex.org/W4385446086;https://openalex.org/W4385759708;https://openalex.org/W4385856339;https://openalex.org/W4385876655;https://openalex.org/W4386572629;https://openalex.org/W4387340609;https://openalex.org/W4387582182;https://openalex.org/W4387642754;https://openalex.org/W4387673749;https://openalex.org/W4387891416;https://openalex.org/W4388026124;https://openalex.org/W4388651637;https://openalex.org/W4389472701;https://openalex.org/W4389922353;https://openalex.org/W4390270820;https://openalex.org/W4391924270;https://openalex.org/W4392769393;https://openalex.org/W4393164682;https://openalex.org/W4394628039;https://openalex.org/W4395075425;https://openalex.org/W4401905099;https://openalex.org/W6617438000;https://openalex.org/W6674244637;https://openalex.org/W6714546793;https://openalex.org/W6731373686;https://openalex.org/W6732368028;https://openalex.org/W6757807117;https://openalex.org/W6758323257;https://openalex.org/W6760455596;https://openalex.org/W6766838752;https://openalex.org/W6774171151;https://openalex.org/W6776567814;https://openalex.org/W6778724598;https://openalex.org/W6782419424;https://openalex.org/W6783816205;https://openalex.org/W6787603540;https://openalex.org/W6791046924;https://openalex.org/W6804602675;https://openalex.org/W6806136341;https://openalex.org/W6811099727;https://openalex.org/W6838687261;https://openalex.org/W6839084267;https://openalex.org/W6843120439;https://openalex.org/W6852958117;https://openalex.org/W6853457688;https://openalex.org/W6853819881;https://openalex.org/W6854053170;https://openalex.org/W6856312780;https://openalex.org/W6857119555;https://openalex.org/W6857323859;https://openalex.org/W6858234696;https://openalex.org/W6859760625;https://openalex.org/W6860601728;https://openalex.org/W6862900603,Precision agriculture;Automation;Artificial intelligence;Computer science;Machine learning;Software deployment;Agriculture;Machine vision;Terrain;Field (mathematics);Engineering;Software engineering,Smart Agriculture and AI;Remote Sensing in Agriculture;Date Palm Research Studies
-OPENALEX,https://openalex.org/W4220822358,https://doi.org/10.3390/cancers14071697,https://pubmed.ncbi.nlm.nih.gov/35406469,A Machine-Learning-Based Bibliometric Analysis of the Scientific Literature on Anal Cancer,CANCERS,CANCERS,2022,article,en,Università degli Studi del Piemonte Orientale “Amedeo Avogadro”,"Squamous-cell carcinoma of the anus (ASCC) is a rare disease. Barriers have been encountered to conduct clinical and translational research in this setting. Despite this, ASCC has been a prime example of collaboration amongst researchers. We performed a bibliometric analysis of ASCC-related literature of the last 20 years, exploring common patterns in research, tracking collaboration and identifying gaps. The electronic Scopus database was searched using the keywords ""anal cancer"", to include manuscripts published in English, between 2000 and 2020. Data analysis was performed using R-Studio 0.98.1091 software. A machine-learning bibliometric method was applied. The bibliometrix R package was used. A total of 2322 scientific documents was found. The average annual growth rate in publication was around 40% during 2000-2020. The five most productive countries were United States of America (USA), United Kingdom (UK), France, Italy and Australia. The USA and UK had the greatest link strength of international collaboration (22.6% and 19.0%). Two main clusters of keywords for published research were identified: (a) prevention and screening and (b) overall management. Emerging topics included imaging, biomarkers and patient-reported outcomes. Further efforts are required to increase collaboration and funding to sustain future research in the setting of ASCC.",14,7,1697,1697,"Franco, 2022, CANCERS",15,"Franco, Pierfrancesco;Segelov, Eva;Johnsson, Anders;Riechelmann, Rachel P.;Guren, Marianne G.;Das, Prajnan;Rao, Sheela;Arnold, Dirk;Spindler, Karen‐Lise Garm;Deutsch, Éric;Krengli, Marco;Tombolini, Vincenzo;Sebag‐Montefiore, David;Felice, Francesca De","Franco, Pierfrancesco;Segelov, Eva;Johnsson, Anders;Riechelmann, Rachel P.;Guren, Marianne G.;Das, Prajnan;Rao, Sheela;Arnold, Dirk;Spindler, Karen‐Lise Garm;Deutsch, Éric;Krengli, Marco;Tombolini, Vincenzo;Sebag‐Montefiore, David;Felice, Francesca De",Università degli Studi del Piemonte Orientale “Amedeo Avogadro”;Monash Health;Monash University;Skåne University Hospital;AC Camargo Hospital;Oslo University Hospital;University of Oslo;The University of Texas MD Anderson Cancer Center;Royal Marsden Hospital;Asklepios Klinik Altona;Aarhus University Hospital;Institut Gustave Roussy;Policlinico Umberto I;Sapienza University of Rome;University of Leeds,https://openalex.org/W776995397;https://openalex.org/W1804130247;https://openalex.org/W1964106115;https://openalex.org/W1965416300;https://openalex.org/W1971960862;https://openalex.org/W2029919879;https://openalex.org/W2041176483;https://openalex.org/W2050654184;https://openalex.org/W2055442814;https://openalex.org/W2060575649;https://openalex.org/W2076341925;https://openalex.org/W2106115615;https://openalex.org/W2108952004;https://openalex.org/W2118632290;https://openalex.org/W2124675713;https://openalex.org/W2126330670;https://openalex.org/W2128978912;https://openalex.org/W2164644812;https://openalex.org/W2224538875;https://openalex.org/W2416810470;https://openalex.org/W2543747182;https://openalex.org/W2552356277;https://openalex.org/W2563123241;https://openalex.org/W2604816500;https://openalex.org/W2750756488;https://openalex.org/W2755950973;https://openalex.org/W2762345927;https://openalex.org/W2773124333;https://openalex.org/W2803009863;https://openalex.org/W2900187858;https://openalex.org/W2901532466;https://openalex.org/W2940815904;https://openalex.org/W2955245499;https://openalex.org/W3034884769;https://openalex.org/W3099049175;https://openalex.org/W3128656334;https://openalex.org/W3163069028;https://openalex.org/W3163816882;https://openalex.org/W3164232230;https://openalex.org/W3173506613;https://openalex.org/W3199622155;https://openalex.org/W4214891571;https://openalex.org/W4300707346;https://openalex.org/W6728763948,Anal cancer;Computer science;Scientific literature;Information retrieval;Data science;Cancer;Medicine;Internal medicine;Biology,Colorectal and Anal Carcinomas;Diagnosis and treatment of tuberculosis
diff --git a/www/services/standardizer.py b/www/services/standardizer.py
index d1194523..a743f024 100644
--- a/www/services/standardizer.py
+++ b/www/services/standardizer.py
@@ -126,30 +126,34 @@
"Publication Type": "DT", # Tipo documento
"Source Title": "SO", # Nome Rivista
"Volume": "VL", # Volume
- "Issue": "IS", # Fascicolo
+ "Issue Number": "IS", # <-- Corretto (Prima era "Issue")
"Start Page": "BP", # Pagina iniziale
"End Page": "EP", # Pagina finale
"Abstract": "AB", # Abstract
- "Citing Works Count": "TC", # Citazioni
+ "Citing Works Count": "TC" # Citazioni totali
}
# -----------------------------------------------------------------------------
-# DIZIONARIO DI MAPPING PER COCHRANE (TXT EXPORT)
+# MAPPING DICTIONARY FOR COCHRANE (TXT EXPORT)
# -----------------------------------------------------------------------------
COCHRANE_SCALAR_MAP: dict[str, str] = {
"TI": "TI", # Titolo
- "SO": "SO", # Source / Nome Rivista
- "YR": "PY", # Cochrane spesso usa YR per l'anno
- "PY": "PY", # Alternativa per l'anno
- "DO": "DI", # DOI in Cochrane è DO
- "DI": "DI",
+ "SO": "SO", # Nome rivista
+ "YR": "PY", # Anno
+ "PY": "PY",
+ "DOI": "DI", # <-- RISOLTO: Aggiunta etichetta DOI esplicita
+ "DO": "DI", # Manteniamo DO per esportazioni più vecchie
+ "ID": "UT", # <-- RISOLTO: L'ID di Cochrane diventa il nostro UT
+ "AN": "UT",
"AB": "AB", # Abstract
"VL": "VL", # Volume
- "NO": "IS", # Issue number in Cochrane è spesso NO o IS
- "IS": "IS",
+ "NO": "IS", # Fascicolo
+ "IS": "IS",
"PT": "DT", # Publication Type
+ "LA": "LA", # Lingua
}
+
# Campi scalari annidati: (percorso_nested, tag_WoS)
# Il percorso è una lista di chiavi da seguire nel dict raw.
OPENALEX_NESTED_SCALAR_MAP: list[tuple[list[str], str]] = [
@@ -871,14 +875,9 @@ def transform_dimensions_record(raw_record: dict) -> dict:
return standardized
# -----------------------------------------------------------------------------
-# FUNZIONE DI TRASFORMAZIONE PER COCHRANE
+# TRANSFORMATION FUNCTION FOR COCHRANE (TXT EXPORT)
# -----------------------------------------------------------------------------
def transform_cochrane_record(raw_record: dict) -> dict:
- """
- Converte un record estratto dal file di testo Cochrane nei tag WoS standard.
- Il parser originale 'parse_cochrane_data' concatena già i campi multipli
- con il punto e virgola, quindi applichiamo uno split.
- """
standardized: dict = {
tag: _get_default_value(contract)
for tag, contract in COLUMN_TYPE_CONTRACTS.items()
@@ -886,13 +885,16 @@ def transform_cochrane_record(raw_record: dict) -> dict:
standardized["DB"] = "COCHRANE"
- # 1. Mappatura campi scalari diretti
+ # 1. Mappa i campi scalari (usando il dizionario)
for coch_key, wos_tag in COCHRANE_SCALAR_MAP.items():
if coch_key in raw_record and raw_record[coch_key]:
standardized[wos_tag] = _cast_scalar(raw_record[coch_key], COLUMN_TYPE_CONTRACTS[wos_tag])
- # 2. Gestione Speciale: Paginazione
- # In Cochrane le pagine (PG) possono essere "1-10" o singole
+ # Se UT è ancora vuoto, usa il DOI come fallback
+ if not standardized["UT"] and standardized.get("DI"):
+ standardized["UT"] = standardized["DI"]
+
+ # 2. Divisione Pagine (PG -> BP ed EP)
pg = str(raw_record.get("PG", ""))
if "-" in pg:
parts = pg.split("-", 1)
@@ -901,28 +903,30 @@ def transform_cochrane_record(raw_record: dict) -> dict:
elif pg:
standardized["BP"] = pg.strip()
- # 3. Campi Multi-Valore (Split)
-
- # Autori (AU e AF): Il parser li ha uniti con "; "
+ # 3. Autori (AU e AF)
au_str = str(raw_record.get("AU", ""))
if au_str and au_str.strip():
- authors_list = [a.strip() for a in au_str.split(";") if a.strip()]
+ # A volte Cochrane esporta liste già pronte, altre volte stringhe separate
+ if isinstance(raw_record["AU"], list):
+ authors_list = raw_record["AU"]
+ else:
+ authors_list = [a.strip() for a in au_str.split(";") if a.strip()]
+
standardized["AU"] = authors_list
standardized["AF"] = authors_list
- # Parole chiave (KW in Cochrane diventa DE in WoS)
- kw_str = str(raw_record.get("KW", ""))
+ # 4. Le Parole Chiave (KY o KW -> DE e ID) <--- RISOLTO!
+ # Controlliamo prima se c'è "KY" (come nel tuo file), altrimenti cerchiamo "KW"
+ kw_str = str(raw_record.get("KY", raw_record.get("KW", "")))
if kw_str and kw_str.strip():
- standardized["DE"] = [k.strip() for k in kw_str.split(";") if k.strip()]
+ kw_list = [k.strip() for k in kw_str.split(";") if k.strip()]
+ standardized["DE"] = kw_list
+ standardized["ID"] = kw_list
- # Pulizia standard su Nome Rivista
+ # Normalizzazione SO
if standardized.get("SO"):
standardized["SO"] = standardized["SO"].upper()
- # Nota: Cochrane raramente esporta References strutturate (CR) o Affiliazioni (C1)
- # in un formato facilmente parsabile nel txt base. I Type Contracts
- # assicureranno che rimangano liste vuote [] senza causare crash.
-
return standardized
# -----------------------------------------------------------------------------
@@ -931,7 +935,6 @@ def transform_cochrane_record(raw_record: dict) -> dict:
def transform_lens_record(raw_record: dict) -> dict:
"""
Converte una riga del file CSV esportato da Lens nei tag WoS standard.
- Lens separa quasi esclusivamente i campi multi-valore con il punto e virgola.
"""
standardized: dict = {
tag: _get_default_value(contract)
@@ -940,12 +943,16 @@ def transform_lens_record(raw_record: dict) -> dict:
standardized["DB"] = "LENS"
+ # ==========================================
# 1. Mappatura campi scalari diretti
+ # ==========================================
for lens_key, wos_tag in LENS_SCALAR_MAP.items():
if lens_key in raw_record and raw_record[lens_key]:
standardized[wos_tag] = _cast_scalar(raw_record[lens_key], COLUMN_TYPE_CONTRACTS[wos_tag])
- # 2. Campi Multi-Valore (Split)
+ # ==========================================
+ # 2. Campi Multi-Valore (Split sulle Liste)
+ # ==========================================
# Autori (AU e AF): Lens li esporta nella colonna "Author/s" separati da ';'
authors_str = str(raw_record.get("Author/s", ""))
@@ -959,22 +966,24 @@ def transform_lens_record(raw_record: dict) -> dict:
if kw_str and kw_str.strip():
standardized["DE"] = [k.strip() for k in kw_str.split(";") if k.strip()]
- # Index Keywords (ID): Lens usa "Fields of Study" (basato su concetti AI come Dimensions)
+ # Index Keywords (ID): Lens usa "Fields of Study"
fos_str = str(raw_record.get("Fields of Study", ""))
if fos_str and fos_str.strip():
standardized["ID"] = [f.strip() for f in fos_str.split(";") if f.strip()]
- # Riferimenti Citati (CR): Lens usa "References" o "Lens ID delle referenze"
+ # Riferimenti Citati (CR): Lens usa "References" (Spesso vuoto, ma gestito in sicurezza)
refs_str = str(raw_record.get("References", ""))
if refs_str and refs_str.strip():
standardized["CR"] = [r.strip() for r in refs_str.split(";") if r.strip()]
- # Affiliazioni (C1): Spesso esportate come "Affiliations" in Lens
+ # Affiliazioni (C1): Esportate come "Affiliations" (Spesso vuoto in Lens)
aff_str = str(raw_record.get("Affiliations", ""))
if aff_str and aff_str.strip():
standardized["C1"] = [aff.strip() for aff in aff_str.split(";") if aff.strip()]
- # Pulizia standard su Nome Rivista (in maiuscolo come da convenzione)
+ # ==========================================
+ # 3. Normalizzazioni finali
+ # ==========================================
if standardized.get("SO"):
standardized["SO"] = standardized["SO"].upper()