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An integrated fog and Artificial Intelligence smart health framework to predict and prevent COVID-19

Nowadays, COVID-19 is spreading at a rapid rate in almost all the continents of the world. It has already affected many people who are further spreading it day by day. Hence, it is the most essential to alert nearby people to be aware of it due to its communicable behavior. Till May 2020, no vaccine...

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Autores principales: Singh, Prabhdeep, Kaur, Rajbir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7659515/
https://www.ncbi.nlm.nih.gov/pubmed/33205037
http://dx.doi.org/10.1016/j.glt.2020.11.002
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author Singh, Prabhdeep
Kaur, Rajbir
author_facet Singh, Prabhdeep
Kaur, Rajbir
author_sort Singh, Prabhdeep
collection PubMed
description Nowadays, COVID-19 is spreading at a rapid rate in almost all the continents of the world. It has already affected many people who are further spreading it day by day. Hence, it is the most essential to alert nearby people to be aware of it due to its communicable behavior. Till May 2020, no vaccine is available for the treatment of this COVID-19, but the existing technologies can be used to minimize its effect. Cloud/fog computing could be used to monitor and control this rapidly spreading infection in a cost-effective and time-saving manner. To strengthen COVID-19 patient prediction, Artificial Intelligence(AI) can be integrated with cloud/fog computing for practical solutions. In this paper, fog assisted the internet of things based quality of service framework is presented to prevent and protect from COVID-19. It provides real-time processing of users’ health data to predict the COVID-19 infection by observing their symptoms and immediately generates an emergency alert, medical reports, and significant precautions to the user, their guardian as well as doctors/experts. It collects sensitive information from the hospitals/quarantine shelters through the patient IoT devices for taking necessary actions/decisions. Further, it generates an alert message to the government health agencies for controlling the outbreak of chronic illness and for tanking quick and timely actions.
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spelling pubmed-76595152020-11-13 An integrated fog and Artificial Intelligence smart health framework to predict and prevent COVID-19 Singh, Prabhdeep Kaur, Rajbir Glob Transit Article Nowadays, COVID-19 is spreading at a rapid rate in almost all the continents of the world. It has already affected many people who are further spreading it day by day. Hence, it is the most essential to alert nearby people to be aware of it due to its communicable behavior. Till May 2020, no vaccine is available for the treatment of this COVID-19, but the existing technologies can be used to minimize its effect. Cloud/fog computing could be used to monitor and control this rapidly spreading infection in a cost-effective and time-saving manner. To strengthen COVID-19 patient prediction, Artificial Intelligence(AI) can be integrated with cloud/fog computing for practical solutions. In this paper, fog assisted the internet of things based quality of service framework is presented to prevent and protect from COVID-19. It provides real-time processing of users’ health data to predict the COVID-19 infection by observing their symptoms and immediately generates an emergency alert, medical reports, and significant precautions to the user, their guardian as well as doctors/experts. It collects sensitive information from the hospitals/quarantine shelters through the patient IoT devices for taking necessary actions/decisions. Further, it generates an alert message to the government health agencies for controlling the outbreak of chronic illness and for tanking quick and timely actions. The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. 2020 2020-11-12 /pmc/articles/PMC7659515/ /pubmed/33205037 http://dx.doi.org/10.1016/j.glt.2020.11.002 Text en © 2020 The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Singh, Prabhdeep
Kaur, Rajbir
An integrated fog and Artificial Intelligence smart health framework to predict and prevent COVID-19
title An integrated fog and Artificial Intelligence smart health framework to predict and prevent COVID-19
title_full An integrated fog and Artificial Intelligence smart health framework to predict and prevent COVID-19
title_fullStr An integrated fog and Artificial Intelligence smart health framework to predict and prevent COVID-19
title_full_unstemmed An integrated fog and Artificial Intelligence smart health framework to predict and prevent COVID-19
title_short An integrated fog and Artificial Intelligence smart health framework to predict and prevent COVID-19
title_sort integrated fog and artificial intelligence smart health framework to predict and prevent covid-19
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7659515/
https://www.ncbi.nlm.nih.gov/pubmed/33205037
http://dx.doi.org/10.1016/j.glt.2020.11.002
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