Open Access

ARTICLE

Numerical Solutions of a Novel Designed Prevention Class in the HIV Nonlinear Model

Zulqurnain Sabir¹, Muhammad Umar¹, Muhammad Asif Zahoor Raja^2,*, Dumitru Baleanu^3,4
1 Department of Mathematics and Statistics, Hazara University, Mansehra, 21120, Pakistan
2 Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin, 64002, Taiwan
3 Department of Mathematics, Cankaya University, Ankara, 06790, Turkey
4 Institute of Space Science, Magurele, Bucharest, 77125, Romania
* Corresponding Author: Muhammad Asif Zahoor Raja. Email:
(This article belongs to this Special Issue: Mathematical Aspects of Computational Biology and Bioinformatics)

Computer Modeling in Engineering & Sciences 2021, 129(1), 227-251. https://doi.org/10.32604/cmes.2021.016611

Received 11 March 2021; Accepted 10 May 2021; Issue published 24 August 2021

Abstract

The presented research aims to design a new prevention class (P) in the HIV nonlinear system, i.e., the HIPV model. Then numerical treatment of the newly formulated HIPV model is portrayed handled by using the strength of stochastic procedure based numerical computing schemes exploiting the artificial neural networks (ANNs) modeling legacy together with the optimization competence of the hybrid of global and local search schemes via genetic algorithms (GAs) and active-set approach (ASA), i.e., GA-ASA. The optimization performances through GA-ASA are accessed by presenting an error-based fitness function designed for all the classes of the HIPV model and its corresponding initial conditions represented with nonlinear systems of ODEs. To check the exactness of the proposed stochastic scheme, the comparison of the obtained results and Adams numerical results is performed. For the convergence measures, the learning curves are presented based on the different contact rate values. Moreover, the statistical performances through different operators indicate the stability and reliability of the proposed stochastic scheme to solve the novel designed HIPV model.

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Keywords

Prevention class; HIV; supervised neural networks; infection model; artificial neural networks; convergence curves; active-set algorithm; adams results; genetic algorithms

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