Numerical Investigations through ANNs for Solving COVID-19 Model

Muhammad Umar, Zulqurnain Sabir, Muhammad Asif Zahoor Raja, Shumaila Javeed, Hijaz Ahmad, Sayed K. Elagen and Ahmed Khames
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Muhammad Umar: Department of Mathematics and Statistics, Hazara University, Mansehra 21300, Pakistan
Zulqurnain Sabir: Department of Mathematics and Statistics, Hazara University, Mansehra 21300, Pakistan
Muhammad Asif Zahoor Raja: Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou 64002, Taiwan
Shumaila Javeed: Department of Mathematics, Islamabad Campus, COMSATS University Islamabad, Park Road, Islamabad 45550, 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

IJERPH, 2021, vol. 18, issue 22, 1-15

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.

Keywords: COVID-19 spreading model; artificial neural networks; Levenberg-Marquardt backpropagation; reference dataset; numerical results (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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