Neural network procedures for the cholera disease system with public health mediations

Severe gastrointestinal infections and watery diseases like cholera are still a major worldwide medical concern in the developing nations. A mathematical system contains some necessary dynamics based on the cholera spread to investigate the influence of public health education movements along with treatment and vaccination as control policies in restraining the infection. The cholera disease system with public health mediations divide the density of human population into seven categories based on the status of diseases, who are susceptible, educated, vaccinated, quarantined, infected, treated and removed individuals along with the aquatic bacteria population. The motive of current research is to present the numerical performances of the cholera disease system with public health mediations by using a stochastic computing process based on the Bayesian regularization neural network. A data is constructed by using a conventional Adam scheme that reduces the mean square error by distributing the data into training, validation and testing with some reasonable percentages. Twenty-five neurons, and sigmoid fitness function are used in the stochastic process to solve the model. The accuracy is justified by using comparison of the results, absolute error around 10-06 to 10-08 and some statistical operator performances.