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Numerical Computational Heuristic Through Morlet Wavelet Neural Network for Solving the Dynamics of Nonlinear SITR COVID-19

Zulqurnain Sabir1, Abeer S. Alnahdi2,*, Mdi Begum Jeelani2, Mohamed A. Abdelkawy2,3,*, Muhammad Asif Zahoor Raja4, Dumitru Baleanu5,6, Muhammad Mubashar Hussain7
1 Department of Mathematics, Hazara University, Mansehra, Pakistan
2 Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
3 Department of Mathematics, Faculty of Science, Beni-Suef University, Beni-Suef, Egypt
4 Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin, Taiwan
5 Department of Mathematics, Cankaya University, Ankara, Turkey
6 Institute of Space Sciences, Magurele-Bucharest, Romania
7 Department of Mathematics, University of Punjab, Jhelum Campus, Jhelum, Pakistan
* Corresponding Authors: Abeer S. Alnahdi. Email: ; Mohamed A. Abdelkawy. Email:
(This article belongs to this Special Issue: Mathematical Aspects of Computational Biology and Bioinformatics)

Computer Modeling in Engineering & Sciences 2022, 131(2), 763-785.

Received 29 July 2021; Accepted 11 November 2021; Issue published 14 March 2022


The present investigations are associated with designing Morlet wavelet neural network (MWNN) for solving a class of susceptible, infected, treatment and recovered (SITR) fractal systems of COVID-19 propagation and control. The structure of an error function is accessible using the SITR differential form and its initial conditions. The optimization is performed using the MWNN together with the global as well as local search heuristics of genetic algorithm (GA) and active-set algorithm (ASA), i.e., MWNN-GA-ASA. The detail of each class of the SITR nonlinear COVID-19 system is also discussed. The obtained outcomes of the SITR system are compared with the Runge-Kutta results to check the perfection of the designed method. The statistical analysis is performed using different measures for 30 independent runs as well as 15 variables to authenticate the consistency of the proposed method. The plots of the absolute error, convergence analysis, histogram, performance measures, and boxplots are also provided to find the exactness, dependability and stability of the MWNN-GA-ASA.


Nonlinear SITR model; morlet function; artificial neural networks; Runge-Kutta; treatment; genetic algorithm; treatment; active-set

Cite This Article

Sabir, Z., Alnahdi, A. S., Jeelani, M. B., Abdelkawy, M. A., Asif, M. et al. (2022). Numerical Computational Heuristic Through Morlet Wavelet Neural Network for Solving the Dynamics of Nonlinear SITR COVID-19. CMES-Computer Modeling in Engineering & Sciences, 131(2), 763–785.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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