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

Q1 Social Sciences Global Transitions Pub Date : 2020-01-01 DOI:10.1016/j.glt.2020.11.002
Prabhdeep Singh , Rajbir Kaur
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引用次数: 30

Abstract

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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集成雾和人工智能智能健康框架,预测和预防COVID-19。
当前,新冠肺炎疫情正在世界各大洲迅速蔓延。它已经影响了许多人,这些人每天都在进一步传播它。因此,由于它的传染性行为,提醒附近的人注意它是最重要的。直到2020年5月,还没有疫苗可用于治疗这种COVID-19,但现有技术可以用来最大限度地减少其影响。云/雾计算可用于以具有成本效益和节省时间的方式监测和控制这种迅速蔓延的感染。为了加强COVID-19患者预测,可以将人工智能(AI)与云/雾计算相结合,以获得实用的解决方案。本文提出了基于雾辅助物联网的服务质量框架,以预防和保护COVID-19。实时处理用户健康数据,通过观察用户症状预测新冠肺炎感染情况,并立即向用户、其监护人以及医生/专家发出紧急警报、医疗报告和重要注意事项。它通过患者物联网设备从医院/检疫场所收集敏感信息,以便采取必要的行动/决策。此外,它还向政府卫生机构发出警报信息,以控制慢性病的爆发,并采取迅速及时的行动。
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来源期刊
Global Transitions
Global Transitions Social Sciences-Development
CiteScore
18.90
自引率
0.00%
发文量
1
审稿时长
20 weeks
期刊最新文献
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