COVID-X: Novel Health-Fog Framework Based on Neutrosophic Classifier for Confrontation Covid-19

• Main Authors: Ibrahim Yasser, Abeer Twakol, A. A. Abd El-Khalek, Ahmed Samrah, A. A. Salama
• Format: Article
• Language: English
• Published: University of New Mexico 2020-07-01
• Series: Neutrosophic Sets and Systems
• Subjects: coronavirus pneumonia; covid-19; intelligent medical system; fog computing; health-fog; neutrosophic; deep learning; computer-aided diagnosis
• Online Access: http://fs.unm.edu/NSS/COVIDXNovelHealth1.pdf

The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. Technological developments like edge computing, fog computing, Internet of Things (IoT), and Big Data have gained importance due to their robustness and ability to provide diverse response characteristics based on target application. In this paper, we present a novel Health-Fog framework universal system to automatically assist the early diagnosis, treatment, and preventive of people with COVID-19 in an efficient manner. Achieving an empirical of the proposed framework which mix between deep learning and Neutrosophic classifiers in the task of classifying COVID-19. There are some proposed applications based on the proposed COVID-X framework such as smart mask, smart medical suit, safe spacer, and Medical Mobile Learning (MML) will be presented. Computer-aided diagnosis systems could assist in the early detection of COVID-19 abnormalities and help to monitor the progression of the disease, potentially reduce mortality rates.