Optimal control of thermoregulation in the human dermal regions investigated through the stochastic integrated techniques

In this research, stochastic computing techniques based on artificial neural networks are applied to the proposed singular nonlinear differential equation to explain thermoregulation in the human dermal region problem to investigate and predict the effect of bioheat temperature on human skin at different atmospheric temperatures with different cases with considering the impact of both air convection thermal sensitivity and temperature, where sequential quadratic programming, interior point technique, and active set technique are included in the designed techniques. Moreover, thermal dynamics in the epidermal layer that is affected by both cooling and heating treatments, and the temperature profile across space and time have been examined. Numerical convergence analysis has been applied to endorse the nature of precision and convergence of the designed stochastic computing techniques. To preserve thermal balance, this leads to modifications in the human body's temperature regulation. The statistical analysis is provided comprehensively in the form of graphs and tables to further enhance the significance in terms of accuracy, efficiency, and convergence of the current study.