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• W4378469629 abstract "Medical cyber-physical systems (MCPS) firmly integrate a network of medical objects. These systems are highly efficacious and have been progressively used in the Healthcare 4.0 to achieve continuous high-quality services. Healthcare 4.0 encompasses numerous emerging technologies and their applications have been realized in the monitoring of a variety of virus outbreaks. As a growing healthcare trend, coronavirus disease (COVID-19) can be cured and its spread can be prevented using MCPS. This virus spreads from human to human and can have devastating consequences. Moreover, with the alarmingly rising death rate and new cases across the world, there is an urgent need for continuous identification and screening of infected patients to mitigate their spread. Motivated by the facts, we propose a framework for early detection, prevention, and control of the COVID-19 outbreak by using novel Industry 5.0 technologies. The proposed framework uses a dimensionality reduction technique in the fog layer, allowing high-quality data to be used for classification purposes. The fog layer also uses the ensemble learning-based data classification technique for the detection of COVID-19 patients based on the symptomatic dataset. In addition, in the cloud layer, social network analysis (SNA) has been performed to control the spread of COVID-19. The experimental results reveal that compared with state-of-the-art methods, the proposed framework achieves better results in terms of accuracy (82.28 %), specificity (91.42 %), sensitivity (90 %) and stability with effective response time. Furthermore, the utilization of CVI-based alert generation at the fog layer improves the novelty aspects of the proposed system." @default.
• W4378469629 created "2023-05-27" @default.
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• W4378469629 date "2023-10-01" @default.
• W4378469629 modified "2023-10-11" @default.
• W4378469629 title "FCMCPS-COVID: AI propelled fog–cloud inspired scalable medical cyber-physical system, specific to coronavirus disease" @default.
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• W4378469629 doi "https://doi.org/10.1016/j.iot.2023.100828" @default.
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