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

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 m...

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Autores principales: Sabir, Zulqurnain, Raja, Muhammad Asif Zahoor, Guerrero Sánchez, Yolanda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8759843/
https://www.ncbi.nlm.nih.gov/pubmed/35035828
http://dx.doi.org/10.1155/2022/3774123
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author Sabir, Zulqurnain
Raja, Muhammad Asif Zahoor
Guerrero Sánchez, Yolanda
author_facet Sabir, Zulqurnain
Raja, Muhammad Asif Zahoor
Guerrero Sánchez, Yolanda
author_sort Sabir, Zulqurnain
collection PubMed
description 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.
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spelling pubmed-87598432022-01-15 Solving an Infectious Disease Model considering Its Anatomical Variables with Stochastic Numerical Procedures Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Guerrero Sánchez, Yolanda J Healthc Eng Research Article 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. Hindawi 2022-01-07 /pmc/articles/PMC8759843/ /pubmed/35035828 http://dx.doi.org/10.1155/2022/3774123 Text en Copyright © 2022 Zulqurnain Sabir et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Sabir, Zulqurnain
Raja, Muhammad Asif Zahoor
Guerrero Sánchez, Yolanda
Solving an Infectious Disease Model considering Its Anatomical Variables with Stochastic Numerical Procedures
title Solving an Infectious Disease Model considering Its Anatomical Variables with Stochastic Numerical Procedures
title_full Solving an Infectious Disease Model considering Its Anatomical Variables with Stochastic Numerical Procedures
title_fullStr Solving an Infectious Disease Model considering Its Anatomical Variables with Stochastic Numerical Procedures
title_full_unstemmed Solving an Infectious Disease Model considering Its Anatomical Variables with Stochastic Numerical Procedures
title_short Solving an Infectious Disease Model considering Its Anatomical Variables with Stochastic Numerical Procedures
title_sort solving an infectious disease model considering its anatomical variables with stochastic numerical procedures
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8759843/
https://www.ncbi.nlm.nih.gov/pubmed/35035828
http://dx.doi.org/10.1155/2022/3774123
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