Time series prediction of the COVID-19 outbreak in India using LSTM based deep learning models

The epidemic caused by COVID-19 presents a significant risk to the continuation of human civilisation and has already done irreparable damage to society. In this paper, forecasting of Coronavirus outbreak in India is performed by LSTM and CovnLSTM deep neural network techniques. COVID-19 data of confirmed cases of India is used. It was taken from John Hopkins University. The loss rate of ConvLSTM is lower than LSTM and RMSE of ConvLSTM is lower than LSTM. For training Covn-LSTM shows 0.069% and testing ConvLSTM shows 0.32% improvement over LSTM model. Therefore, ConvLSTM outperformed over LSTM model. Further wise selection of hyper-parameters could increase the accuracy of the models. © 2023 IEEE.

A STUDY OF THE DIGITAL RESPONSE BY RETAILERS IN THE PANDEMIC THROUGH LITERATURE REVIEW

COVID-19, the epicenter of the century, has had a profound effect on the entire industry, and on our health in an unprecedented way. The closure announced by the government to monitor the spread of the disease has halted many businesses on a large scale. As a result, US GDP for 2Q2020 (Apr-Jun) decreased by 9%, while UK GDP contracted by 20% (GDP Growth-Second Quarter 2020, OECD, 2020). Such setbacks have never been heard of in the last decade. Against this backdrop, it will be interesting to see how businesses have performed on this issue, what steps they have taken to deal with natural storms, and what technologies they have chosen to survive. A vicious cycle that leads to economic downturns has begun to emerge. Retail is one of the most important sectors of the economy. This paper proposes a strategy to combat the risk or robustness of the digital retail business.