Numerical solutions of a novel designed prevention class in the HIV nonlinear model
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Date
2021
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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. © 2021 Tech Science Press. All rights reserved.
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Active-Set Algorithm, Adams Results, Artificial Neural Networks, Convergence Curves, Genetic Algorithms, HIV, Infection Model, Prevention Class, Supervised Neural Networks
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Citation
Sabir, Zulqurnain...et al. (2021). "Numerical solutions of a novel designed prevention class in the HIV nonlinear model", CMES - Computer Modeling in Engineering and Sciences, Vol. 129, No. 1, pp. 227-251.
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CMES - Computer Modeling in Engineering and Sciences
Volume
129
Issue
1
Start Page
227
End Page
251