Stochastic intelligent computing solvers for the SIR dynamical prototype epidemic model using the impacts of the hospital bed

The present investigations are related to design a stochastic intelligent solver using the infrastructure of artificial neural networks (ANNs) and scaled conjugate gradient (SCG), i.e. ANNs-SCG for the numerical simulations of SIR dynamical prototype system based impacts of hospital bed. The SIR dynamical model is defined into three classes, susceptible patients in the hospital, infected population and recovered people. The proposed results are obtained through the sample statics of verification, testing and training of the dataset. The selection of the statics for training, testing and validation is chosen as 80%, 8% and 12%. A dataset is proposed based on the Adams scheme for the comparison of dynamical SIR prototype using the impacts of hospital bed. The numerical solutions are presented through the ANNs-SCG in order to reduce the values of the mean square error. To achieve the reliability, capability, accuracy, and competence of ANNs-SCG, the mathematical solutions are presented in the form of error histograms (EHs), regression, state transitions (STs) and correlation.