IoT-Based COVID-19 Diagnosing and Monitoring Systems: A Survey.

Anjum, Nasreen; Alibakhshikenari, Mohammad; Rashid, Junaid; Jabeen, Fouzia; Asif, Amna; Mohamed, Ehab Mahmoud; Falcone, Francisco

Anjum, Nasreen; Alibakhshikenari, Mohammad; Rashid, Junaid; Jabeen, Fouzia; Asif, Amna; Mohamed, Ehab Mahmoud; Falcone, Francisco.

Afiliación

Anjum N; Department of Cyber and Technical Computing, School of Computing and EngineeringUniversity of Gloucestershire, Park Campus, Cheltenham Gloucestershire GL50 2RH U.K.
Alibakhshikenari M; Department of Signal Theory and CommunicationsUniversidad Carlos III de Madrid, 28911 Leganés Madrid Spain.
Rashid J; Department of Computer and EngineeringKongju National University Cheonan 31080 South Korea.
Jabeen F; Department of Computer ScienceShaheed Benazir Bhutto Women University Peshawar Peshawar 00384 Pakistan.
Asif A; Department of Computer ScienceShaheed Benazir Bhutto Women University Peshawar Peshawar 00384 Pakistan.
Mohamed EM; Department of Electrical EngineeringCollege of Engineering in Wadi AddawasirPrince Sattam Bin Abdulaziz University Wadi Addawasir 11991 Saudi Arabia.
Falcone F; Department of Electrical EngineeringAswan University Aswan 81542 Egypt.

IEEE Access ; 10: 87168-87181, 2022.

Article en En | MEDLINE | ID: mdl-36345377

Palabras clave

COVID-19 pandemic; Internet of Things (IoTs); artificial intelligence (AI); coronavirus; machine learning algorithms

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: IEEE Access Año: 2022 Tipo del documento: Article Pais de publicación: Estados Unidos

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