Risk-Aware Identification of Highly Suspected COVID-19 Cases in Social IoT: A Joint Graph Theory and Reinforcement Learning Approach.

Wang, Bowen; Sun, Yanjing; Duong, Trung Q; Nguyen, Long D; Hanzo, Lajos

Wang, Bowen; Sun, Yanjing; Duong, Trung Q; Nguyen, Long D; Hanzo, Lajos.

Afiliación

Wang B; Xuzhou Engineering Research Center of Intelligent Industry Safety and Emergency CollaborationXuzhou221116China.
Sun Y; Xuzhou Engineering Research Center of Intelligent Industry Safety and Emergency CollaborationXuzhou221116China.
Duong TQ; School of Electronics, Electrical Engineering, and Computer ScienceQueen's University BelfastBelfastBT7 1NNU.K.
Nguyen LD; Duy Tan UniversityDa Nang810000Vietnam.
Hanzo L; School of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonSO17 1BJU.K.

IEEE Access ; 8: 115655-115661, 2020.

Article en En | MEDLINE | ID: mdl-34192110

Palabras clave

COVID-19; Social Internet of Thing (SIoT); graph theory; reinforcement learning

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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: IEEE Access Año: 2020 Tipo del documento: Article

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