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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Vydáno v:	Symmetry (Basel) Ročník 12; číslo 10; s. 1628
Hlavní autoři:	Umar, Muhammad, Sabir, Zulqurnain, Raja, Muhammad Asif Zahoor, Shoaib, Muhammad, Gupta, Manoj, Sánchez, Yolanda Guerrero
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.10.2020
Témata:	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
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Abstract	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.
AbstractList	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.
Author	Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Shoaib, Muhammad Sánchez, Yolanda Guerrero Gupta, Manoj Umar, Muhammad
Author_xml	– sequence: 1 givenname: Muhammad surname: Umar fullname: Umar, Muhammad – sequence: 2 givenname: Zulqurnain surname: Sabir fullname: Sabir, Zulqurnain – sequence: 3 givenname: Muhammad Asif Zahoor orcidid: 0000-0001-9953-822X surname: Raja fullname: Raja, Muhammad Asif Zahoor – sequence: 4 givenname: Muhammad orcidid: 0000-0001-8295-9861 surname: Shoaib fullname: Shoaib, Muhammad – sequence: 5 givenname: Manoj orcidid: 0000-0002-4274-4927 surname: Gupta fullname: Gupta, Manoj – sequence: 6 givenname: Yolanda Guerrero orcidid: 0000-0002-4228-2355 surname: Sánchez fullname: Sánchez, Yolanda Guerrero
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Snippet	The present study aims to design stochastic intelligent computational heuristics for the numerical treatment of a nonlinear SITR system representing the...
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SubjectTerms	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
Title	A Stochastic Intelligent Computing with Neuro-Evolution Heuristics for Nonlinear SITR System of Novel COVID-19 Dynamics
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