Abstract
The widening of coronavirus disease (COVID-19) across the globe has put both the government and humanity at risk. The funds of part of the biggest recessions are stressed out due to the severe infectivity rates and highly communicable nature of this disease. Due to the expanding consequence of cases being registered and their successive significance on the civic body administration and health professionals, certain prediction methods are intended to be necessitated to predict the number of cases in the future. In this paper, nonlinear cosine-based time series learning (NCTL) is introduced for the prediction and analysis of COVID-19 in India. First, the nonlinear least squares regressive feature selection (NLS-RFS) model is used for choosing the relevant features by considering both the active cases with less prediction error. Next, the cosine-based neighborhood filter algorithm is applied to attain the optimum filtered features to select relevant features with minimum prediction time. Finally, cosine neighborhood-based LSTM is used for the prediction of the number of COVID-19 cases being registered in India to the fore and consequence of precautionary measures like social distancing, lockdown, and declaring containment zones on the outspread of COVID-19. The existing deep learning methods’ prediction accuracy was not enhanced with lesser time. In order to overcome the issue, the nonlinear cosine-based time series learning (NCTL) method has been introduced. The aim of the proposed NCTL method is to predict the number of COVID-19 cases with less prediction time and prediction error. This helps to enhance the prediction accuracy for considering the time series with accurate prediction results. The experiment of the NCTL method is conducted using metrics such as accuracy, prediction error, prediction accuracy, and prediction time with respect to diverse samples. The simulation result illustrates that the NCTL method increases the prediction accuracy by 8%, reduces the prediction time by 18%, and minimizes the prediction error by 31% compared to state-of-the-art works in a computationally efficient manner.