aspg-3-4151-1791417648
20261008000048
American Scientific Publishing Group
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American Scientific Publishing Group
Fusion: Practice and Applications
FPA
2770-0070
2692-4048
2026
21
2
An Efficient Hybrid Approach Model for SARS-CoV-2 Prediction Using an Optimized Deep Learning Recurrent Neural Network and Fuzzy inference
Zaid
Derea
College of Computer Science and Information Technology, Wasit University, Iraq
Ammar
Kazm
Department of Computer, College of Education for Pure Sciences, Wasit University, Iraq
Jasim
Mohammed
College of Islamic Sciences, Department of Islamic Creed and Thought, Samarra University, Iraq
Oday Ali
Hassen
Department of Computer, College of Education for Pure Sciences, Wasit University, Iraq; Ministry of Education, Wasit Education Directorate, Wasit, Iraq
Esraa Saleh
Alomari
Department of Computer, College of Education for Pure Sciences, Wasit University, Iraq
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SARS-CoV2 virus has affected the peoples in worldwide with several issues, like health and economy. Moreover, mathematical definition of fractal dimension affords a method for calculating the non-linear dynamic behaviour difficulty revealed through time series of countries. The fuzzy logic model illustrates and manages the characteristic uncertainty of classification issue. In this paper, an effectual SARS-CoV2model is developed using optimized Deep learning model through time series data. The derived features are derived from the input sequential data for disease forecasting. Moreover, over sampling scheme is exploited for data augmentation, which enhances the prediction process. Fuzzy systems and various distance measures are calculated for choosing most significant features. The Deep Recurrent Neural network (DRNN) is applied for performing SARS-CoV2prediction, in which DRNN is trained through designed Fractional Water Poor and Rich Optimization (FrWPRO) method. Meanwhile, the training process of DRNN using hybrid optimization model from scratch proves that, the designed SARS-CoV2prediction method accomplishes better performance compared to other existing approaches with Mean Square Error (MSE), Root MSE (RMSE), and Mean Absolute Percentage Error (MAPE) of 0.1425, and 0.3775, and 0.3467 respectively.
2026
433
457
4151
https://creativecommons.org/licenses/by/4.0/
10.54216/FPA.210227
https://www.americaspg.com/journal/3/article/4151
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https://www.americaspg.com/storage/41760713813.pdf