Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/1600 2019 2019 Department of Statistics and Programming, Faculty of Economics, University of Tishreen, Latakia, P.O. Box 2230, Syria El El-Sayed Department of Food and Biotechnology, South Ural State University, 454080 Chelyabinsk Alhumaima Ali Subhi Electronic and Computer Center, University of Diyala, Baqubah MJJ2+R9G, Iraq Hussein Alkattan Department of System Programming, South Ural State University, 454080 Chelyabinsk, Russia Ammar Kadi Department of System Programming, South Ural State University, 454080 Chelyabinsk, Russia Artem .. Department of System Programming, South Ural State University, 454080 Chelyabinsk, Russia Irina .. Electronic and Computer Center, University of Diyala, Baqubah MJJ2+R9G, Iraq Mostafa .. Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt El-Sayed M El El-kenawy The COVID-19 epidemic has in fact placed the whole community in a dire predicament that has led to numerous tragedies, including an economic downturn, political unrest, and job losses. Forecasting and identifying COVID-19 infection cases is crucial for the government at all levels because the pandemic grows exponentially and results in fatalities. Hence, by giving information about the spread of the epidemic, the government can move quickly at multiple levels to establish new policies and modalities in order to minimize the trajectory of the COVID-19 pandemic's effects on both public health and the economic sectors. Forecasting models for COVID-19 infection cases in the Ural region in Russia were developed using two deep Long Short-Term Memory (LSTM) learning-based approaches namely Encoder–Decoder LSTM and Attention LSTM algorithms. The models were evaluated based on five standard performance evaluation metrics which include Mean Square Error (MSE), Mean Absolute Error (MAE), Root MSE (RMSE), Relative RMSE (RRMSE), and coefficient of determination (R2). However, the Encoder–Decoder LSTM deep learning-based forecasting model achieved the best performance results (MSE=32794.09, MAE=168.56, RMSE=181.09, RRMSE=13.46, and R2=0.87) compared to the model developed with Attention LSTM models. 2023 2023 20 33 10.54216/JISIoT.080202 https://www.americaspg.com/articleinfo/18/show/1600