Solving an Infectious Disease Model considering Its Anatomical Variables with Stochastic Numerical Procedures.

Zulqurnain Sabir,Yolanda Guerrero Sánchez,Muhammad Asif Zahoor Raja,Han Wang

doi:10.1155/2022/3774123

Abstract

The aim of the current work is to perform the numerical investigation of the infectious disease based on the nonlinear fractional order prey-predator model using the Levenberg–Marquardt backpropagation (LMB) based on the artificial neuron networks (ANNs), i.e., LMBNNs. The fractional prey-predator model is classified into three categories, the densities of the susceptible, infected prey, and predator populations. The statistics proportions for solving three different variations of the infectious disease based on the fractional prey-predator model are designated for training 80% and 10% for both validation and testing. The numerical actions are performed using the LMBNNs to solve the infectious disease based on the fractional prey-predator model, and comparison is performed using the database Adams–Bashforth–Moulton approach. The infectious disease based on the fractional prey-predator model is solved using the LMBNNs to reduce the mean square error (M.S.E). In order to validate the exactness, capability, consistency, and competence of the proposed LMBNNs, the numerical procedures using the correlation, M.S.E, regression, and error histograms are drawn.

Highlights

Infectious diseases occur when some viruses, fungi, germs, and parasites enter into the human body. ese forms are diffused through infection from one to another human, contaminated food, animals, or contact to any of the ecological factors that are polluted with any type of these bodies
E aim of the current work is to perform the numerical investigation of the infectious disease based on the fractional prey-predator model using the Levenberg–Marquardt backpropagation (LMB) based on the artificial neuron networks (ANNs), i.e., LMBNNs. e LMBNNs are applied on three different variants of authentication, testing, training, and sample information. e statistics proportions for solving three different variations of the infectious disease based on the fractional prey-predator model are designated for training 80% and 10% for both validation and testing
The proposed methodology of LMBNNs is presented for the infectious disease based on the fractionalorder nonlinear prey-predator system. e methodology is categorized in two steps. e necessary trials of the stochastic-based LMBNNs are provided, and the execution

Summary

Introduction

Infectious diseases occur when some viruses, fungi, germs, and parasites enter into the human body. ese forms are diffused through infection from one to another human, contaminated food, animals, or contact to any of the ecological factors that are polluted with any type of these bodies. (i) e design of stochastic computing solvers LMBNNs is presented for the first time to solve the infectious disease spread systems governed with the fractional prey-predator model (ii) e designed procedures of LMBNNs have been implemented effectively to study the behavior of different scenarios of the fractional prey-predator model, and comparative studies are found in decent resemblance with the state-of-the-art Adams–Bashforth–Moulton numerical approach for solving fractional differential equations (iii) e convergence performances on iterative updated of MSE, negligible absolute error (AE) from standard outcomes, correlation/regression index, and error histograms (EHs) further authenticate the efficacy of the designed LMBNN computing platform for fractional prey-predator models e paper is organized as follows: Section 2 shows the methodology based on LMBNNs. Section 3 presents the numerical outcomes through LMBNNs to solve the fractional-order nonlinear prey-predator model.

Methodology

Result

Conclusions

Conflicts of Interest