ARTIFICIAL INTELLIGENCE AND NEURAL NETWORKING FOR AN ANALYSIS OF FRACTAL–FRACTIONAL ZIKA VIRUS MODEL

Hasib Khan, Wafa F. Alfwzan (), Jehad Alzabut, D. K. Almutairi (), Mohammad Athar Azim () and Rajermani Thinakaran ()
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Hasib Khan: Department of Mathematics and Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia†Department of Mathematics, Shaheed Benazir Bhutto University, Sheringal, Dir Upper 18000, Khyber Pakhtunkhwa, Pakistan
Wafa F. Alfwzan: ��Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
Jehad Alzabut: Department of Mathematics and Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia§Center for Research and Innovation, Asia International University, Yangiobod MFY, G’ijduvon Street, House 74, Bukhara, Uzbekistan
D. K. Almutairi: �Department of Mathematics, College of Science Al-Zulfi, Majmaah University, Al Majma’ah 11952, Saudi Arabia
Mohammad Athar Azim: ��Preparatory Year Program, College of Humanities and Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
Rajermani Thinakaran: *Faculty of Data Science and Information Technology, INTI International University, Malaysia

FRACTALS (fractals), 2025, vol. 33, issue 08, 1-18

Abstract: This study examines the utilization of artificial intelligence (AI) and neural networking approaches for modeling the outbreak of the zoonotic Zika virus transmissions dynamics among hums and mosquitoes by applying the fractal–fractional operators. The theoretical and computational aspects enhance the significance of the study for control and treatment procedures. The theoretical aspect of the paper includes the existence, stability, and uniqueness results which validate the accuracy of the model and leading us to the computational results. The computational results are carried out with a computational scheme which gives us intricate data for the future predictions. The AI is used to validate, train, and test the data driven from the numerical simulations and measure the errors. Our work open doors for the researchers for the applications of the AI tools for the deep analysis of the zoonotic intricate disease dynamics.

Keywords: Linearly Perturbed System; Fractal–Fractional Derivative; Zika Virus Model; Artificial Intelligence; Neural Networking (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0218348X25401437

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