Comparison of Hybrid Artificial Neural Networks With GA, PSO, and RSA in Predicting COVID-19 Cases
B. Murali Manohar, Raja Das
Abstract
The objective of the current study is to choose the best model with the highest accuracy rate using three robust hybrid artificial intelligence-based models: the ANN-GA, ANN-PSO and ANN-RSA. To do so, a sample of COVID-19 confirmed cases in India between August 1, 2021, and July 26, 2022, is first compiled. A random allocation of 70% (30%) of the total observation has been chosen as training (testing) data. After that, the LM method is used to train an ANN model. Accordingly, the appropriate number of hidden neurons is determined to be 9 using the R^2 and RMSE criterion. To achieve the highest accuracy rate, ANN-GA, ANN-PSO, and ANN-RSA models are developed using the presented ANN model. The optimized model's R-values during the training and test phases, according to ANN-GA and ANN-PSO, are 0.99 and 0.95, respectively. The R-values for ANN-RSA varied from 0.99 to 0.96. hence, the ANN-RSA demonstrated superior performance in forecasting COVID-19 cases in India.