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COVID-19 Pandemic Analysis and Prediction Using Machine Learning Approaches in India
International Conference on Intelligent Computing and Advances in Communication, ICAC 2020 ; 202 LNNS:307-316, 2021.
Article in English | Scopus | ID: covidwho-1340420
ABSTRACT
An extremely infectious disease, coronavirus disease 2019 (COVID-19) nowadays, is continuously threatening the whole world by its omnidirectional spreading. The aggressiveness and impact of COVID-19 disease worldwide have made the World Health Organization (WHO) to declare it as global pandemic. Machine learning (ML), an application of AI, can be implemented efficiently to track COVID-19, predict the increase of the pandemic, and design approaches to limit its spread. In this paper, three ML regression techniques, namely linear regression, polynomial regression, and support vector regression, are used to propose a model. Some experiments are performed on the online time series data from the dashboard of Johns Hopkins University sourced from Github repository using Python language to predict the affected people in the next 60 days. This article will help out in creating awareness among people toward the prevention of spread of infection due to COVID-19. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies / Prognostic study Language: English Journal: International Conference on Intelligent Computing and Advances in Communication, ICAC 2020 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies / Prognostic study Language: English Journal: International Conference on Intelligent Computing and Advances in Communication, ICAC 2020 Year: 2021 Document Type: Article