A hybridized LSTM-ANN-RSA based deep learning models for prediction of COVID-19 cases in Eastern European countries

Deep learning models have become essential for managing time-series data across various applications in recent years. COVID-19 case data presents complex time-series patterns with high nonlinearities and dynamic fluctuations. Long short-term memory (LSTM) networks offer a suitable framework for developing prediction models to handle such complexities. However, conventional LSTM networks often have predefined, extensive structures, leading to overfitting issues and difficulties in determining the optimal number of hidden neurons. To address these challenges, we propose Regularized Self-Organizing LSTM (LSTM-ANN-RSA). This method optimizes both the structure and parameters of the network. This study introduces an adaptive learning algorithm with L2-norm regularization to adjust parameters, ensuring prediction accuracy and mitigating overfitting. Additionally, a growing strategy based on hidden neuronal sensitivity automatically determines the LSTM-ANN structure, enhancing compactness and efficiency. The proposed model's efficiency is demonstrated through comparisons with multiple deep learning models (ANN-GD, ANN-LM, ANN-RSA, GRU-ADAM and LSTM-ADAM). The results show that LSTM-ANN-RSA significantly outperformed others in predicting COVID-19 in five countries (Croatia, Greece, Italy, Poland, and Russia), with lower MAPE (2285.018, 64.4903, 205.70, 1611.19, 572.98) and higher R2 (0.99824, 0.99726, 0.99786, 0.9962, 0.99252) values. These findings highlight the proposed model's substantial improvements in predicting the COVID-19 pandemic, enhancing the region's ability to plan for and respond to future epidemics.