Title
Solving an Infectious Disease Model considering Its Anatomical Variables with Stochastic Numerical Procedures
Date Issued
01 January 2022
Access level
open access
Resource Type
journal article
Author(s)
sabir, Zulqurnain
Raja M.A.Z.
Guerrero Sánchez Y.
Hazara University Mansehra
Publisher(s)
Hindawi Limited
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.
Volume
2022
Language
English
OCDE Knowledge area
Matemáticas aplicadas Epidemiología
Scopus EID
2-s2.0-85123045661
PubMed ID
Source
Journal of Healthcare Engineering
ISSN of the container
20402295
Sponsor(s)
This paper was partially supported by Ministerio de Ciencia, Innovaci´on y Universidades (No.PGC2018-097198-B-I00) and Fundaci´on S´eneca de la Regi´on de Murcia (No.20783/PI/18).
Sources of information: Directorio de Producción Científica Scopus