A Stochastic Bayesian Neural Network for the Mosquito Dispersal Mathematical System

The objective of this study is to examine numerical evaluations the mosquito dispersal mathematical system (MDMS) in a heterogeneous atmosphere through artificial intelligence (AI) techniques via Bayesian regularization neural networks (BSR-NNs). MDMS constructed with six classes, i.e., eggs, larvae, pupae, host, resting mosquito, and ovipositional site densities-based ODEs system. computing BSR-NNs scheme applied for three different performances using data training, testing verification, which divided as 75%, 15%, 10% twelve hidden neurons. result comparisons are provided check authenticity designed AI method portrayed by BSR-NNs. based procedure executed reduce mean square error (MSE) MDMS. achieved also presented validate efficiency process MSE, correlation, histograms regression.