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

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 artifici...

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Bibliographic Details
Published in:Computer modeling in engineering & sciences 2021, Vol.129 (1), p.227-251
Main Authors: Sabir, Zulqurnain, Umar, Muhammad, Asif Zahoor Raja, Muhammad, Baleanu, Dumitru
Format: Article
Language:English
Subjects:
Artificial neural networks
Dengue fever
Design
Genetic algorithms
HIV
Human immunodeficiency virus
Infections
Initial conditions
Learning curves
Mathematical models
Neural networks
Nonlinear systems
Optimization
Pregnancy
Prevention
Viruses
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Summary: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.
ISSN:1526-1506
1526-1492
1526-1506
DOI:10.32604/cmes.2021.016611