Dynamical analysis of nonlinear combined drug therapy model for HIV infection: Bayesian regularization technique intelligent networks

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The study aims to provide an overview of the design and analysis of the differential system of the Nonlinear Combined Drug Therapy Mathematical (NCDTM) model for HIV Infection. This is achieved by utilizing the artificial Neural Network with Bayesian Regularisation technique intelligent network (ANNs-BRTIN), which is a stochastic method that enhances accuracy, reliability, and efficiency in the dynamics calculation process. The NCDTM model is defined along with experiments employing integer and nonlinear mathematical forms via four classes Where the number of uninfected cells in the blood and represented by A(t) the countable number of free virus particles in the blood is represented by S(t) when RTI therapy is administered denotes the number of infected cells in the Pre-RT phase with η dosage represented by I(t), D(t) represents when RTI therapy is administered using, reflects the number of infected cells in the post-RT phase η dosage. The numerical computing of the NCDTM system is performed with Adams methods, and these results are fed to the proposed ANNs-BRTINs for finding the approximated solution of four distinct instances by incorporating 15% of the data for testing and validation and 85% for training. The presented ANNs-BRTINs accuracy is illustrated by comparing the results from the acquired dataset of the Adam method for sundry scenarios. The use of regression for testing and residual, state transitions, mean square error, error histograms, and correlation in numerical replications of the ANNs-BRTINs is also explored to confirm their capacity, validity, consistency, correctness, and competence.