A scale conjugate neural network learning process for the nonlinear malaria disease model

Manal Alqhtani; J.F. Gómez-Aguilar; Khaled M. Saad; Zulqurnain Sabir; Eduardo Pérez-Careta; Manal Alqhtani; J.F. Gómez-Aguilar; Khaled M. Saad; Zulqurnain Sabir; Eduardo Pérez-Careta

doi:10.3934/math.20231075

AIMS Mathematics

2023, Volume 8, Issue 9: 21106-21122. doi: 10.3934/math.20231075

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A scale conjugate neural network learning process for the nonlinear malaria disease model

1.
Department of Mathematics, College of Sciences and Arts, Najran University, Najran, Kingdom of Saudi Arabia
2.
CONACyT–Tecnológico Nacional de México/CENIDET. Interior Internado Palmira S/N, Col. Palmira, Cuernavaca Morelos C.P. 62490, México
3.
Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan
4.
Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
5.
Universidad de Guanajuato, Dpto. Electrónica, Carretera Salamanca-Valle de Santiago, Km 3+1.8, Salamanca Gto, México

Received: 15 March 2023 Revised: 25 May 2023 Accepted: 29 May 2023 Published: 03 July 2023
MSC : 68T07, 92B20, 65L06

The purpose of this work is to provide a stochastic framework based on the scale conjugate gradient neural networks (SCJGNNs) for solving the malaria disease model of pesticides and medication (MDMPM). The host and vector populations are divided in the mathematical form of the malaria through the pesticides and medication. The stochastic SCJGNNs procedure has been presented through the supervised neural networks based on the statics of validation (12%), testing (10%), and training (78%) for solving the MDMPM. The optimization is performed through the SCJGNN along with the log-sigmoid transfer function in the hidden layers along with fifteen numbers of neurons to solve the MDMPM. The accurateness and precision of the proposed SCJGNNs is observed through the comparison of obtained and source (Runge-Kutta) results, while the small calculated absolute error indicate the exactitude of designed framework based on the SCJGNNs. The reliability and consistency of the SCJGNNs is observed by using the process of correlation, histogram curves, regression, and function fitness.
- scale conjugate gradient,
- malaria disease,
- neural networks,
- mathematical model,
- numerical solutions
Citation: Manal Alqhtani, J.F. Gómez-Aguilar, Khaled M. Saad, Zulqurnain Sabir, Eduardo Pérez-Careta. A scale conjugate neural network learning process for the nonlinear malaria disease model[J]. AIMS Mathematics, 2023, 8(9): 21106-21122. doi: 10.3934/math.20231075

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Abstract

The purpose of this work is to provide a stochastic framework based on the scale conjugate gradient neural networks (SCJGNNs) for solving the malaria disease model of pesticides and medication (MDMPM). The host and vector populations are divided in the mathematical form of the malaria through the pesticides and medication. The stochastic SCJGNNs procedure has been presented through the supervised neural networks based on the statics of validation (12%), testing (10%), and training (78%) for solving the MDMPM. The optimization is performed through the SCJGNN along with the log-sigmoid transfer function in the hidden layers along with fifteen numbers of neurons to solve the MDMPM. The accurateness and precision of the proposed SCJGNNs is observed through the comparison of obtained and source (Runge-Kutta) results, while the small calculated absolute error indicate the exactitude of designed framework based on the SCJGNNs. The reliability and consistency of the SCJGNNs is observed by using the process of correlation, histogram curves, regression, and function fitness.

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