https://socictopen.socict.org/files/original/7aa59dfc7c4e2e46dc61bff832cceffe.pdf e455bb314e7ca76978b47d277feb1508 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource COVID-X: Novel Health-Fog Framework Based on Neutrosophic Classifier for Confrontation Covid-19 Creator An entity primarily responsible for making the resource Ibrahim Yasser, Abeer Twakol, A. A. Abd El-Khalek, Ahmed Samrah, A. A. Salama Description An account of the resource The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highlytransmittable and pathogenic with no clinically approved antiviral drug or vaccine available fortreatment. 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 diverseresponse characteristics based on target application. In this paper, we present a novel Health-Fogframework universal system to automatically assist the early diagnosis, treatment, and preventiveof people with COVID-19 in an efficient manner. Achieving an empirical of the proposed frameworkwhich 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 smartmask, 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 abnormalitiesand help to monitor the progression of the disease, potentially reduce mortality rates. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource coronavirus pneumonia; covid-19; intelligent medical system; fog computing; health-fog; neutrosophic; deep learning; computer-aided diagnosis Identifier An unambiguous reference to the resource within a given context 10.5281/zenodo.3951625 Source A related resource from which the described resource is derived Neutrosophic Sets and Systems Publisher An entity responsible for making the resource available University of New Mexico Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mathematics, Electronic computers. Computer science