DATA ANALYSIS OF FRACTAL-FRACTIONAL CO-INFECTION COVID-TB MODEL WITH THE USE OF ARTIFICIAL INTELLIGENCE

Hasib Khan, Jehad Alzabut, D. K. Almutairi (), Haseena Gulzar () and Wafa Khalaf Alqurashi ()
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Hasib Khan: Department of Mathematics and Sciences, Prince Sultan University, 11586 Riyadh, Saudi Arabia†Department of Mathematics, Shaheed Benazir Bhutto University, Sheringal, Dir Upper 18000, Khyber Pakhtunkhwa, Pakistan
Jehad Alzabut: Department of Mathematics and Sciences, Prince Sultan University, 11586 Riyadh, Saudi Arabia‡Department of Industrial Engineering, OSTİM Technical University, 06374 Ankara, Turkey§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, 11952 Al-Majmaah, Saudi Arabia
Haseena Gulzar: Department of Biotechnology, Shaheed Benazir Bhutto University, Sheringal, Dir Upper, 18000 Khyber Pakhtunkhwa, Pakistan
Wafa Khalaf Alqurashi: Department of Mathematics, Faculty of Science, Umm Al-Qura University, Makkah, Saudi Arabia

FRACTALS (fractals), 2025, vol. 33, issue 04, 1-21

Abstract: With the objective to better understand the dynamics of co-infection concepts for COVID-19 and tuberculosis (TB), this study employs proficient computational approaches, stability analysis, and the search for solutions. First, we verify the theoretical verification of the co-infection model by determining the existence of solutions. Consistency of projections depends on the model’s resilience against disruptions, whose stability analysis is demonstrated. We used artificial intelligence to apply neural networks to the analysis of the model data, and the results show the usefulness of our technique with mean square error performance that varies from 9.0575 × 10−14 to 9.3841 × 10−13 and a regression of R = 1. Complex patterns in time series data are further captured by nonlinear autoregressive (NAR) models. Neural network clustering analysis uncovers complex data structures and improves model understanding. This all-encompassing method integrates data clustering, AI-driven analysis, stability, and solution existence to establish an effective framework for exploring co-infection dynamics.

Keywords: Modeling COVID-TB; Fractal-Fractional Derivative; Neural Networking; Error Estimation; Regression Analysis (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0218348X25400997

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