Open Access

ARTICLE

A Design of Predictive Intelligent Networks for the Analysis of Fractional Model of TB-Virus

Muhammad Asif Zahoor Raja¹, Aqsa Zafar Abbasi², Kottakkaran Sooppy Nisar^3,*, Ayesha Rafiq², Muhammad Shoaib⁴

1 Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan
2 Department of Applied Mathematics and Statistics, Institute of Space Technology, Islamabad, 44000, Pakistan
3 Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
4 AI Center, Yuan Ze University, Taoyuan, 320, Taiwan

* Corresponding Author: Kottakkaran Sooppy Nisar. Email:

(This article belongs to the Special Issue: Recent Developments on Computational Biology-II)

Computer Modeling in Engineering & Sciences 2025, 143(2), 2133-2153. https://doi.org/10.32604/cmes.2025.058020

Received 02 September 2024; Accepted 19 March 2025; Issue published 30 May 2025

Abstract

Being a nonlinear operator, fractional derivatives can affect the enforcement of existence at any given time. As a result, the memory effect has an impact on all nonlinear processes modeled by fractional order differential equations (FODEs). The goal of this study is to increase the fractional model of the TB virus’s (FMTBV) accuracy. Stochastic solvers have never been used to solve FMTBV previously. The Bayesian regularized artificial (BRA) method and neural networks (NNs), often referred to as BRA-NNs, were used to solve the FMTBV model. Each scenario features five occurrences that each reflect a different order of derivatives, ranging from 0.8, 0.85, 0.9, 0.95, and 1, as well as five potential rates for different parameters. Training data made up 90% of the data, testing data made up 5%, and validation data made up 5% of the data used to illustrate the FMTBV’s approximations. To verify that the BRA-NNs were correct, the generated simulations were described in the following solutions using the FOLotkaVolterra approach in MATLAB. Comprehensive Simulink results in terms of mean square error, error histogram, and regression analysis investigations further highlight the competence, dependability, and accuracy of the suggested BRA-NNs.

---

Keywords

---