| Abstract: |
| The paper presents a mathematical analysis of tuberculosis, caused by Mycobacterium TB, which primarily affects the lungs. The bacteria spreads through coughing, sneezing, or speaking, and is classified into five categories: susceptible, infected with DS, infected with MDR, isolated, and recovered. The study uses a novel piecewise derivative approach that considers both singular and non-singular kernels to improve our understanding of rabies spread dynamics. The uniqueness of the solution is established using fixed point and piecewise derivative frameworks. A piecewise numerical iteration scheme based on Newton interpolation polynomials is used to obtain the approximate solution. The study contributes to understanding crossover dynamics and the concept of piecewise derivatives. Additionally, an Artificial Neural Network approach is employed for training, testing, and validating data to investigate the disease problem, ensuring high accuracy in training, testing, and validation. |
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