Abstract
Deep learning approaches have played a major role in ongoing technological development in the last year, drawing a convincing research ground in areas such as outbreak prediction or forecasting, drug/vaccine development process for COVID-19, patient treatment, medication, screening and minimizing human involvement in medical practices for early diagnosis, and patient monitoring as well as academic and industrial applications. This research uses deep learning models like recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent units (GRU) and to predict the number of COVID-19 cases for the next 26 days. The major goal is to study the pace of increase in the count of cases and anticipate the spread of the pandemic so that society may be helped by raising awareness. The results showed that GRU performance is better as compared to LSTM and RNN based on the basis of graphs and metrics obtained in results.
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Sah, S., Kamerkar, A., Surendiran, B., Dhanalakshmi, R. (2022). Predicting the Trends of COVID-19 Cases Using LSTM, GRU and RNN in India. In: Satapathy, S.C., Peer, P., Tang, J., Bhateja, V., Ghosh, A. (eds) Intelligent Data Engineering and Analytics. Smart Innovation, Systems and Technologies, vol 266. Springer, Singapore. https://doi.org/10.1007/978-981-16-6624-7_46
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