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

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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 [Formula: see text] to [Formula: see text] and a regression of [Formula: see text]. 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.