ArticleGlobal transitions2020
An integrated fog and Artificial Intelligence smart health framework to predict and prevent COVID-19.
Article in Global transitions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis.Frontiers in public health · 2022Pooled it
- Applications of Fog Computing in Healthcare.Cureus · 2024Review
- Innovative applications of artificial intelligence during the COVID-19 pandemic.Infectious medicine · 2024Review
- A Novel Social Distancing Approach for Limiting the Number of Vehicles in Smart Buildings Using LiFi Hybrid-Network.International journal of environmental research and public health · 2023Article
- At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives.Sensors (Basel, Switzerland) · 2023Review
- Adopting effective hierarchal IoMTs computing with K-efficient clustering to control and forecast COVID-19 cases.Computers & electrical engineering : an international journal · 2022Article
- Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey.Healthcare (Basel, Switzerland) · 2022Review
- Wireless Technologies for Social Distancing in the Time of COVID-19: Literature Review, Open Issues, and Limitations.Sensors (Basel, Switzerland) · 2022Review
- A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing.Expert systems · 2022Article
- IoT-Enabled smart mask to detect COVID19 outbreak.Health and technology · 2022Article
- Detection of Dental Diseases through X-Ray Images Using Neural Search Architecture Network.Computational intelligence and neuroscience · 2022Article
- Smart Healthcare System for Severity Prediction and Critical Tasks Management of COVID-19 Patients in IoT-Fog Computing Environments.Computational intelligence and neuroscience · 2022Article
- A Novel Ensemble-based Classifier for Detecting the COVID-19 Disease for Infected Patients.Information systems frontiers : a journal of research and innovation · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.