A design of predictive computational network for the analysis of fractional epidemical predictor-prey model

Studying the dynamical system that represents the transmission of an outbreak from prey to predatory is crucial and significant because the results may be used to highlight a variety of real-world issues. Infections and predator-prey interplay have been linked to provide a complicated cumulative influence as regulators of prey and predator size range in predator-prey ecology. This paper presents a novel implementation of intelligent computation to analyze the dynamics of a fractional epidemiological model with disease infection in both the population (FEM-DIBP) using supervised machine learning (SMLs) optimized with the Bayesian Regularization methodology (BRM). The FOLotkaVoltera solver based on Grunwald-Letnikov is used to build the dataset for the FEM-DIBP for observation approach of the SML model of the system. Also state variables trajectories and Lorenz curves are constructed using FOLorenz to analyze the dynamics of the epidemical model. The BRM learned SMLs models are used in the train, test, and validating procedures to find the FEM-DIBP solutions to various situations depending on changing epidemical factors. The effectiveness of SML in solving FEMDIBP is demonstrated by estimated regression measurements, error histogram visualizations, and mean-squared error analyses. The suitability of the created BRMNNs for such a simulated situation is demonstrated by the most appealing math-ematical findings for A.E within the range of 10- 2 to 10-6.

Automated summary

Studying the dynamics of the transmission of an outbreak from prey to predatory is crucial and significant. This paper presents a novel implementation of intelligent computation using supervised machine learning to analyze a fractional epidemiological model. The effectiveness of the model is demonstrated through various evaluations.