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COVID-19 has become the most devastating disease of the current century and is pandemic. As per WHO report, there are globally 31,174,627confirmed cases including 962,613 deaths as of 22nd September,2020. The disease is spreading through outbreaks despite the availability of latest technologies for treatment of patients. In this paper, we proposed a neural network-based prediction of number of cases in India due to COVID-19. Recurrent neural network (RNN) based LSTM is applied on India dataset for prediction. LSTM networks are a type of RNN capable of learning order dependence in sequence forecasting problems. We analyze the performance of the network and then compare it with two parameter reduced variants of LSTM, obtained by elimination of hidden unit signals, bias and input signal. For performance evaluation, we used the MSE measure.
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10.1016/j.matpr.2020.11.117
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document_parses/pdf_json/0666e3641c12bf50d611431400d4a635540a4821.json
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Recurrent neural network based prediction of number of COVID-19 cases in India
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