","

"Authors","Author full names","Author(s) ID","Titles","Year","Source title","Volume","Issue","Art. No.","Page start","Page end","Page count","DOI","Cited by","Link","Affiliations","Authors with affiliations","Abstract","Indexed Keywords","Author Keywords","Molecular Sequence Numbers","Chemicals/CAS","Funding Details","Funding Texts","References","Correspondence Address","Editors","Tradenames","Manufacturers","Publisher","Conference name","Conference code","Conference location","Conference date","Sponsors","ISSN","ISBN","CODEN","PubMed ID","Language of Original Document","Abbreviated Source Title","Document Type","Publication Stage","Open Access","Source","EID" "Chicaiza J.; Villota S.D.; Vinueza-Naranjo P.G.; Rumipamba-Zambrano R.","Chicaiza, Janneth (56535509300); Villota, Stephany D. (57039217000); Vinueza-Naranjo, Paola G. (57192924899); Rumipamba-Zambrano, Ruben (57191039229)","56535509300; 57039217000; 57192924899; 57191039229","Contribution of Deep-Learning Techniques Toward Fighting COVID-19: A Bibliometric Analysis of Scholarly Production During 2020","2022","IEEE Access","10","","","33281","33300","19","10.1109/ACCESS.2022.3159025","1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126326388&doi=10.1109%2fACCESS.2022.3159025&partnerID=40&md5=e59260ae9cf7597088ff5fea6e0d231e","Departamento de Ciencias de la Computación y Electrónica, Universidad Técnica Particular de Loja, Loja, 110105, Ecuador; Gestión de Investigación, Desarrollo e Innovación, Instituto Nacional de Investigación en Salud Pública, Quito, 170136, Ecuador; Facultad de Ingeniería, Universidad Nacional de Chimborazo, Riobamba, 060108, Ecuador; Corporación Nacional de Telecomunicaciones - Cnt E.P., Quito, 170528, Ecuador; Universidad Ecotec, Guayas, Samborondn, 092302, Ecuador","Chicaiza J., Departamento de Ciencias de la Computación y Electrónica, Universidad Técnica Particular de Loja, Loja, 110105, Ecuador; Villota S.D., Gestión de Investigación, Desarrollo e Innovación, Instituto Nacional de Investigación en Salud Pública, Quito, 170136, Ecuador; Vinueza-Naranjo P.G., Facultad de Ingeniería, Universidad Nacional de Chimborazo, Riobamba, 060108, Ecuador; Rumipamba-Zambrano R., Corporación Nacional de Telecomunicaciones - Cnt E.P., Quito, 170528, Ecuador, Universidad Ecotec, Guayas, Samborondn, 092302, Ecuador","COVID-19 has dramatically affected various aspects of human society with worldwide repercussions. Firstly, a serious public health issue has been generated, resulting in millions of deaths. Also, the global economy, social coexistence, psychological status, mental health, and the human-environment relationship/dynamics have been seriously affected. Indeed, abrupt changes in our daily lives have been enforced, starting with a mandatory quarantine and the application of biosafety measures. Due to the magnitude of these effects, research efforts from different fields were rapidly concentrated around the current pandemic to mitigate its impact. Among these fields, Artificial Intelligence (AI) and Deep Learning (DL) have supported many research papers to help combat COVID-19. The present work addresses a bibliometric analysis of this scholarly production during 2020. Specifically, we analyse quantitative and qualitative indicators that give us insights into the factors that have allowed papers to reach a significant impact on traditional metrics and alternative ones registered in social networks, digital mainstream media, and public policy documents. In this regard, we study the correlations between these different metrics and attributes. Finally, we analyze how the last DL advances have been exploited in the context of the COVID-19 situation. © 2013 IEEE.","Public health; Bibliometrics analysis; COVID-19; Deep learning; Global economies; Human environment; Human society; Learning techniques; Mental health; Public health issues; Scholarly production; Deep learning","Bibliometric analysis; COVID-19; deep learning; scholarly production","","","","","C. Huang, Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, and L. 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