Numerical Investigations through ANNs for Solving COVID-19 Model.

Journal: International journal of environmental research and public health
Published Date: Nov 20, 2021

Abstract

The current investigations of the COVID-19 spreading model are presented through the artificial neuron networks (ANNs) with training of the Levenberg-Marquardt backpropagation (LMB), i.e., ANNs-LMB. The ANNs-LMB scheme is used in different variations of the sample data for training, validation, and testing with 80%, 10%, and 10%, respectively. The approximate numerical solutions of the COVID-19 spreading model have been calculated using the ANNs-LMB and compared viably using the reference dataset based on the Runge-Kutta scheme. The obtained performance of the solution dynamics of the COVID-19 spreading model are presented based on the ANNs-LMB to minimize the values of fitness on mean square error (M.S.E), along with error histograms, regression, and correlation analysis.

Authors

  • Muhammad Umar
    Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan.
  • Zulqurnain Sabir
    Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan.
  • Muhammad Asif Zahoor Raja
    Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin, Taiwan, R.O.C.
  • Shumaila Javeed
    Department of Mathematics, COMSATS Institute of Information Technology, Park Road, Chak Shahzad, Islamabad, Pakistan.
  • Hijaz Ahmad
    Department of Computer Engineering, Biruni University, Istanbul 34025, Turkey.
  • Sayed K Elagen
    Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
  • Ahmed Khames
    Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.

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