APPLICATION OF SOFT COMPUTING TECHNIQUES IN THE ANALYSIS OF COVID – 19: A REVIEW
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 6, Pages 2480-2503
AbstractBackground and objective: Many trends are being raised across global countries for the novel disease 'Corona-Virus' (COVID-19). Shelter-in-place (SIP) orders are implemented worldwide to reduce the proliferation rate of this virus. Since the World Health Organization (WHO) proclaimed it as 'pandemic,' its emergent state prevails worldwide. As a successor of SARS-CoV-2, higher destruction has been enforced on human lives intolerably. However, the prior detection could help shorten this disease's proliferation rate; various prediction and diagnosis tools are useful with artificial intelligence. These tools might assist in preventive measures against the virus infection. The main objective of this article is two-folded. First, to explore the application of soft computing techniques, the overview of machine learning (ML), deep learning (DL), internet of things (IoT), and support vector machines (SVM) have been interpreted concerning COVID-19 analysis. Next, to analyze performance, a comparative study has been represented to show that the soft computing models outperform some of the prevailed analytical models on the COVID-19. Method: To forecast and predict a pandemic outbreak, Cauchy distribution probabilistic approaches are applied. An extensive study of mathematical and theoretical, and approximate models based on soft computing done for COVID-19 prediction. Consecutively, some curious factor such as infection rate based on age groups could be forecasted. Also, Sick rate, and immunity rate during the quarantine period have been analyzed for nearly 500 sample tests taken globally. Result: This research scopes for extensive analysis of COVID-19 entitled to health policies. It would facilitate the technocrats to develop heuristic models for this wide-spread disease through forecasting of real-time data. It also addresses the statistical analysis of COVID-19 articles and future scope for technocrats regarding COVID-19 prediction and diagnosis.
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