An Efficient Hybrid Approach Model for SARS-CoV-2 Prediction Using an Optimized Deep Learning Recurrent Neural Network and Fuzzy inference

 

 

 

Zaid Derea1,*, Ammar Kazm2, Jasim Mohammed Atyiah3, Oday Ali Hassen2,* ,
Esraa Saleh Alomari2,4

 

1College of Computer Science and Information Technology, Wasit University, Iraq

 

2Department of Computer, College of Education for Pure Sciences, Wasit University, Iraq

 

3College of Islamic Sciences, Department of Islamic Creed and Thought, Samarra University, Iraq

 

4Ministry of Education, Wasit Education Directorate, Wasit, Iraq

 

Emails: zabdulameer@uowasit.edu.iq; aawaad@uowasit.edu.iq; Presidency@uosamarra.edu.iq;   odayali@uowasit.edu.iq; ealomari@uowasit.edu.iq

 

 

 

 

 

 

 

Abstract

 

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.

 

Keywords: Fuzzy inference; Deep learning; Weighted Moving Average; Time series data; Lee distance