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Oncolytic viral immunotherapy is gaining considerable prominence in the realm of chronic diseases treatment and rehabilitation. Oncolytic viral therapy is an intriguing therapeutic approach due to its low toxicity and dual function of immune stimulations. This work aims to design a soft computing approach using stupendous knacks of neural networks (NNs) optimized with Bayesian regularization (BR), i.e. NNs-BR, procedure. The constructed NNs-BR technique is exploited in order to determine the approximate numerical treatment of the nonlinear multi-delayed tumor virotherapy (TVT) models in terms of the dynamic interactions between the tumor cells free of viruses, tumor cells infected by viruses, viruses, and cytotoxic T-lymphocytes (CTLs). The strength of state-of-the-art numerical approach is incorporated to develop the reference dataset for the variation in the infection rate for tumor cells, virus-free tumor cell clearance rate by CTLs, CTLs clearance rate for infectious tumor cells, the natural lifecycle of infectious tumor cells, the natural lifecycle of viral cell, the natural lifecycle of CTLs cells, tumor cells free of viruses' maximum proliferation rate, production of tumor cells with an infection, CTLs simulated ratio for infectious tumor cells, CTLs simulated ratios for virus-free cells and delay in time. The dataset is randomly chosen/segmented for training-testing-validation samples to construct the NNs models optimized with backpropagated BR representing the approximate numerical solutions of the dynamic interactions in the TVT model. The performance of the designed NNs-BR technique is accessed/evaluated and outcomes are found in good agreement with the reference solutions having the range of accuracy from 10 − 9 to 10 − 1 6 . The efficacy of NNs-BR paradigm is further substantiated after rigorous analysis on regression metrics, learning curves on MSE, and error histograms for the dynamics of TVT model. [ABSTRACT FROM AUTHOR]

In this article, we analyze the dynamics of the non-linear tumor-immune delayed (TID) model illustrating the interaction among tumor cells and the immune system (cytotoxic T lymphocytes, T helper cells), where the delays portray the times required for molecule formation, cell growth, segregation, and transportation, among other factors by exploiting the knacks of soft computing paradigm utilizing neural networks with back propagation Levenberg Marquardt approach (NNLMA). The governing differential delayed system of non-linear TID, which comprised the densities of the tumor population, cytotoxic T lymphocytes and T helper cells, is represented by non-linear delay ordinary differential equations with three classes. The baseline data is formulated by exploiting the explicit Runge-Kutta method (RKM) by diverting the transmutation rate of Tc to Th of the Tc population, transmutation rate of Tc to Th of the Th population, eradication of tumor cells through Tc cells, eradication of tumor cells through Th cells, Tc cells' natural mortality rate, Th cells' natural mortality rate as well as time delay. The approximated solution of the non-linear TID model is determined by randomly subdividing the formulated data samples for training, testing, as well as validation sets in the network formulation and learning procedures. The strength, reliability, and efficacy of the designed NNLMA for solving non-linear TID model are endorsed by small/negligible absolute errors, error histogram studies, mean squared errors based convergence and close to optimal modeling index for regression measurements. [ABSTRACT FROM AUTHOR]

This paper portrays the exploitation/exploration of artificial intelligence (AI) inspired computing to study the behavior of the multi-delay differential systems that revealed the impact of latent period and the dynamics of the a susceptible, vaccinated, exposed, infectious and recovered (SVEIR) epidemic model involving vaccination by means of the neural networks backpropagation with Levenberg–Marquardt scheme (NNs-BLMS). The reference solutions of the five classes ordinary differential equations (ODEs) model of SVEIR dynamics are calculated by applying the Adams method for variation in delay due to the time spent in preventing the infection and delay due to the duration of recovery immunity in the cured population. The designed NNs-BLMS used the created dataset arbitrarily for training, validation, as well as, testing samples to determine the estimated results of the nonlinear SVEIR epidemic model involving vaccination impact. The achieved accuracy of the designed NNs-BLMS is authenticated/proven by analyzing the fitness function based on mean square error (MSE), regression analysis, and error histogram for sundry scenarios of SVEIR epidemic system with the impact of vaccination. [ABSTRACT FROM AUTHOR]

• A novel design of intelligent computing paradigm using the strength/knacks of AI based procedure via two-layer structure networks of NNs-BBRT is presented to investigate the solution of the nonlinear multiple delays SEIR model for worms propagation in wireless sensor networks. • The fitness function is formulated viably using the mean square error (MSE) index for determination/analyzing the results of NNs-BBRT by utilizing the reference solutions of sundry cases of the SEIR-WSNs model through the numerical data of Adams solver. • Bayesian-regularization backpropagation networks is employed effectively for carrying learning via training, and testing sets to measure/adjust the optimization parameter of networks iteratively with epoch index to solve the SEIR-WSNs model numerically. • Efficiency, convergence, and accuracy of intelligent computing NNs-BBRT procedure to solve the nonlinear SEIR-WSNs model are endorsed by the error histogram illustrations, mean square error and regression analysis. In this study, the dynamics of a nonlinear multiple delays susceptible, exposed, infected, recovered (SEIR) model for worms propagation in wireless sensor networks (WSNs) i.e, (SEIR-WSNs) is analyzed via the design of intelligent numerical computing paradigm by exploiting the neural networks (NNs) backpropagation with the Bayesian-Regularization technique (BBRT) i.e., (NNs-BBRT). The model SEIR-WSNs is mathematically governed with ODEs system that represents the nodes as susceptible, exposed, infectious, and recovered (SEIR) nodes for the description of wireless sensor networks (WSNs). Reference outcomes are produced for the nonlinear SEIR-WSNs system using the Adams method for different scenarios based on the variation in the delays for the latent period, time taken by the antivirus to remove the worms and temporary immunization period. The reference data is used for the execution procedure of NNs-BBRT by segmenting samples into the training and testing sets to approximate the solution for nonlinear SEIR-WSNs system. The precision/accuracy and convergence of designed NNs-BBRT are validated based on the acquired accuracy through the effective fitness attainment on mean squared error (MSE), exhaustive regression analysis and sufficient error histogram illustrations. [ABSTRACT FROM AUTHOR]