NOVEL PLASMA GENERATION OPTIMIZATION-BASED LSTM FRAMEWORK FOR COVID-19 PREDICTION

Abstract

COVID-19 epidemic now disturbs the whole world which rapidly spread across the country. Millions of people have
been diseased. Furthermore, to limit the spread of the virus, economies have been shut down. Several scientists are presently
working in numerous fields to tackle this epidemic and its environments. With a view to avoid death, it is of much importance to
identify the future cases, the spread rate of virus and future safety measures in the field of medicine. To avoid the death rate and to
arrange medical safety equipment, an accurate forecasting model is needed. It is very critical to conduct forecasting research on
the improvement and spread of the epidemic. Therefore, the proposed work targeted at introducing a new COVID-19 forecasting
model by using a Plasma generation optimization (PGO) optimized Long Short-Term Memory (LSTM) network. LSTM is a
recurrent neural network that used forecasting frameworks. To achieve a higher prediction accuracy in COVID-19, the
hyperparameters of the LSTM network tuned by using PGO optimization. The proposed model is applied in the temporal data of
coronavirus spread from world health organization (WHO). The accuracy of the proposed PGO-LSTM model was better than
other forecasting models. The proposed PGO-LSTM based predicting network is talented for a higher dataset.