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Machine Learning Model for Identification of Covid-19 Future Forecasting

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Innovations in Bio-Inspired Computing and Applications (IBICA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 419))

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Abstract

The global proliferation of COVID-19 has put humanity in jeopardy. The assets of the world’s most powerful economies are at risk due to the disease’s high infectivity and contagiousness. The ability of ML algorithms to forecast the number of future patients COVID-19 has an effect on, which is currently regarded as a potential threat to humanity. In this study, five common guaging models, including LR, LASSO, SVM, ES, and LSTM, were used to assess the COVID-19 underpinning variables. Each model makes three types of predictions: the number of recently contaminated cases, the number of passings, and the number of recoveries. However, it is impossible to predict the exact prognosis for the patients.

To combat the problem, a proposed technique based on the long transient memory (LSTM) predicts the number of COVID-19 cases in the following 10 days and the influence of preventive measures such as social seclusion and lockdown on COVID-19 spread.

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Correspondence to N. Anitha , C. Soundarajan , V. Swathi or M. Tamilselvan .

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Anitha, N., Soundarajan, C., Swathi, V., Tamilselvan, M. (2022). Machine Learning Model for Identification of Covid-19 Future Forecasting. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_28

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