RETRACTED ARTICLE: A novel IoT–fog–cloud-based healthcare system for monitoring and predicting COVID-19 outspread

Abstract

Rapid communication of viral sicknesses is an arising public medical issue across the globe. Out of these, COVID-19 is viewed as the most critical and novel infection nowadays. The current investigation gives an effective framework for the monitoring and prediction of COVID-19 virus infection (C-19VI). To the best of our knowledge, no research work is focused on incorporating IoT technology for C-19 outspread over spatial–temporal patterns. Moreover, limited work has been done in the direction of prediction of C-19 in humans for controlling the spread of COVID-19. The proposed framework includes a four-level architecture for the expectation and avoidance of COVID-19 contamination. The presented model comprises COVID-19 Data Collection (C-19DC) level, COVID-19 Information Classification (C-19IC) level, COVID-19-Mining and Extraction (C-19ME) level, and COVID-19 Prediction and Decision Modeling (C-19PDM) level. Specifically, the presented model is used to empower a person/community to intermittently screen COVID-19 Fever Measure (C-19FM) and forecast it so that proactive measures are taken in advance. Additionally, for prescient purposes, the probabilistic examination of C-19VI is quantified as degree of membership, which is cumulatively characterized as a COVID-19 Fever Measure (C-19FM). Moreover, the prediction is realized utilizing the temporal recurrent neural network. Additionally, based on the self-organized mapping technique, the presence of C-19VI is determined over a geographical area. Simulation is performed over four challenging datasets. In contrast to other strategies, altogether improved outcomes in terms of classification efficiency, prediction viability, and reliability were registered for the introduced model.

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

“…Ahanger et al developed in [ 93 ] a fog/cloud framework to forecast COVID-19 cases, employ user-held devices, and track the disease’s spread. First, to identify contaminated individuals and areas, the authors used fuzzy C-mean classification.…”

Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review

“…They observed fog based paradigm outcomes in terms of classification efficiency, prediction viability, and reliability were markedly improved. 37 …”

Computer-aided methods for combating Covid-19 in prevention, detection, and service provision approaches

“…Ahanger et al [ 1 ] proposed a Covid-19 monitoring and forecasting framework that merges the IoT with spatial and temporal patterns through a model known as temporal recurrent neural networks (TRNN). TRNN is a DL technique that forecasts the likelihood of Covid-19 symptoms in geographical patterns.…”

“…Covid-19 is viewed as the most critical and new virus of our time, as it has the potential to spread viral diseases worldwide [ 1 ]. In December 2019, a new infectious disease was found in Wuhan, China, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and characterized by mild or moderate respiratory symptoms.…”