Open Access

ARTICLE

Stochastic Computational Heuristic for the Fractional Biological Model Based on Leptospirosis

Zulqurnain Sabir¹, Sánchez-Chero Manuel², Muhammad Asif Zahoor Raja³, Gilder-Cieza–Altamirano⁴, María-Verónica Seminario-Morales², Fernández Vásquez José Arquímedes⁵, Purihuamán Leonardo Celso Nazario⁶, Thongchai Botmart^7,*, Wajaree Weera⁷

1 Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan
2 Universidad Nacional de Frontera, Sullana, Piura, Perú
3 Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, 64002, Yunlin, Taiwan
4 Universidad Nacional Autónoma de Chota, Cajamarca, Perú
5 Universidad Cesar Vallejo, Trujillo, La Libertad, Perú
6 Universidad Señor de Sipán, Chiclayo, Perú
7 Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand

* Corresponding Author: Thongchai Botmart. Email:

Computers, Materials & Continua 2023, 74(2), 3455-3470. https://doi.org/10.32604/cmc.2023.033352

Received 14 June 2022; Accepted 16 August 2022; Issue published 31 October 2022

Abstract

The purpose of these investigations is to find the numerical outcomes of the fractional kind of biological system based on Leptospirosis by exploiting the strength of artificial neural networks aided by scale conjugate gradient, called ANNs-SCG. The fractional derivatives have been applied to get more reliable performances of the system. The mathematical form of the biological Leptospirosis system is divided into five categories, and the numerical performances of each model class will be provided by using the ANNs-SCG. The exactness of the ANNs-SCG is performed using the comparison of the reference and obtained results. The reference solutions have been obtained by using the Adams numerical scheme. For these investigations, the data selection is performed at 82% for training, while the statics for both testing and authentication is selected as 9%. The procedures based on the recurrence, mean square error, error histograms, regression, state transitions, and correlation will be accomplished to validate the fitness, accuracy, and reliability of the ANNs-SCG scheme.

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Keywords

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