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

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Date

2022

Authors

Sabir, Zulqurnain
Alnahdi, Abeer S.
Jeelani, Mdi Begum
Abdelkawy, Mohamed A.
Raja, Muhammad Asif Zahoor
Baleanu, Dumitru
Hussain, Muhammad Mubashar

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Abstract

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 boxp

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Active-Set, Artificial Neural Networks, Genetic Algorithm, Morlet Function, Nonlinear SITR Model, Runge-Kutta, Treatment, Treatment

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Citation

Sabir, Zulqurnain;...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 and Sciences, Vol.131, No.2, pp.763-785.

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CMES - Computer Modeling in Engineering and Sciences

Volume

131

Issue

2

Start Page

763

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785