A Stochastic Intelligent Computing with Neuro-Evolution Heuristics for Nonlinear SITR System of Novel COVID-19 Dynamics

The present study aims to design stochastic intelligent computational heuristics for the numerical treatment of a nonlinear SITR system representing the dynamics of novel coronavirus disease 2019 (COVID-19). The mathematical SITR system using fractal parameters for COVID-19 dynamics is divided into...

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Bibliographic Details
Published in:Symmetry (Basel) Vol. 12; no. 10; p. 1628
Main Authors: Umar, Muhammad, Sabir, Zulqurnain, Raja, Muhammad Asif Zahoor, Shoaib, Muhammad, Gupta, Manoj, Sánchez, Yolanda Guerrero
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2020
Subjects:
Artificial intelligence
Artificial neural networks
Computation
Coronaviruses
COVID-19 vaccines
Dengue fever
Disease transmission
Genetic algorithms
Learning theory
Mathematical models
Neural networks
Optimization techniques
Parameters
Quadratic programming
Viral diseases
China
ISSN:2073-8994, 2073-8994
Online Access:Get full text
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Summary:The present study aims to design stochastic intelligent computational heuristics for the numerical treatment of a nonlinear SITR system representing the dynamics of novel coronavirus disease 2019 (COVID-19). The mathematical SITR system using fractal parameters for COVID-19 dynamics is divided into four classes; that is, susceptible (S), infected (I), treatment (T), and recovered (R). The comprehensive details of each class along with the explanation of every parameter are provided, and the dynamics of novel COVID-19 are represented by calculating the solution of the mathematical SITR system using feed-forward artificial neural networks (FF-ANNs) trained with global search genetic algorithms (GAs) and speedy fine tuning by sequential quadratic programming (SQP)—that is, an FF-ANN-GASQP scheme. In the proposed FF-ANN-GASQP method, the objective function is formulated in the mean squared error sense using the approximate differential mapping of FF-ANNs for the SITR model, and learning of the networks is proficiently conducted with the integrated capabilities of GA and SQP. The correctness, stability, and potential of the proposed FF-ANN-GASQP scheme for the four different cases are established through comparative assessment study from the results of numerical computing with Adams solver for single as well as multiple autonomous trials. The results of statistical evaluations further authenticate the convergence and prospective accuracy of the FF-ANN-GASQP method.
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ISSN:2073-8994
2073-8994
DOI:10.3390/sym12101628