Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks.

Chiroma, Haruna; Ezugwu, Absalom E; Jauro, Fatsuma; Al-Garadi, Mohammed A; Abdullahi, Idris N; Shuib, Liyana

Chiroma, Haruna; Ezugwu, Absalom E; Jauro, Fatsuma; Al-Garadi, Mohammed A; Abdullahi, Idris N; Shuib, Liyana.

Afiliación

Chiroma H; Future Technology Research Center, National Yunlin University of Science and Technology, Yulin, Taiwan.
Ezugwu AE; School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, KwaZulu-Natal, South Africa.
Jauro F; Department of Computer Science, Faculty of Science, Ahmadu Bello University, Zaria, Nigeria.
Al-Garadi MA; Department of Biomedical Informatics, Emory University, Atlanta, GA, USA.
Abdullahi IN; Department of Medical Laboratory Science, College of Medical Sciences, Ahmadu Bello University, Zaria, Nigeria.
Shuib L; Department of Information System, Universiti Malaya, Kuala Lumpur, Malaysia.

PeerJ Comput Sci ; 6: e313, 2020.

Article en En | MEDLINE | ID: mdl-33816964

Palabras clave

Bibliometric analysis; COVID-19 diagnosis tool; COVID-19 pandemic; Convolutional neural network; Machine learning

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Qualitative_research Idioma: En Revista: PeerJ Comput Sci Año: 2020 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Estados Unidos

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