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- W4367011679 abstract "In both the first and second waves of COVID-19, India suffered many casualties at the hands of the infectious virus in the ongoing pandemic. As the days passed, the number of daily cases steadily increased, with most cases reported from large cities and outbreaks in rural areas. In this proposed paper, we tried to explore the effects of the previous data on the number of daily new cases using the ARIMA Model in machine learning. To forecast COVID-19 spread, we analyzed the effectiveness of multiple machine learning approaches. The accuracy of models was compared using the root mean squared error (RMSE), mean absolute error (MAE), R2 coefficient of determination (R2), and represent fundamental percentage error (MAPE) parameters. The primary goal of this research is to figure out how these new COVID-19 instances will influence the rest of the globe and how many people will be affected by the pandemic. This paper works with the ARIMA Model to predict the accurate growth rate and COVID-19 cases, which can help the government and various organizations plan their systematic strategies. In this paper, we use the ARIMA Model to analyze the growth rate and different factors related to the COVID-19. The model deployed accurately predicts the confirmed cases with an accuracy of 98.97%." @default.
- W4367011679 created "2023-04-27" @default.
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- W4367011679 date "2023-01-01" @default.
- W4367011679 modified "2023-09-25" @default.
- W4367011679 title "Prediction of COVID-19 Cases Using the ARIMA Model and Machine Learning" @default.
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- W4367011679 doi "https://doi.org/10.1007/978-981-19-5191-6_37" @default.
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