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