Approximate numerical algorithms and artificial neural networks for analyzing a fractal-fractional mathematical model

In this paper, an analysis of a mathematical model of the coronavirus is carried out by using two fractal-fractional parameters. This dangerous virus infects a person through the mouth, eyes, nose or hands. This makes it so dangerous that no one can get rid of it. One of the main factors contributin...

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Published inAIMS mathematics Vol. 8; no. 12; pp. 28280 - 28307
Main Authors Najafi, Hashem, Bensayah, Abdallah, Tellab, Brahim, Etemad, Sina, Ntouyas, Sotiris K., Rezapour, Shahram, Tariboon, Jessada
Format Journal Article
LanguageEnglish
Published AIMS Press 01.01.2023
Subjects
artificial neural networks
fixed-point theorem
fractal-fractional derivative
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ISSN2473-6988
2473-6988
DOI10.3934/math.20231447

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Abstract In this paper, an analysis of a mathematical model of the coronavirus is carried out by using two fractal-fractional parameters. This dangerous virus infects a person through the mouth, eyes, nose or hands. This makes it so dangerous that no one can get rid of it. One of the main factors contributing to increasing infections of this deadly virus is crowding. We believe that it is necessary to model this effect mathematically to predict the possible outcomes. Hence, the study of neural network-based models related to the spread of this virus can yield new results. This paper also introduces the use of artificial neural networks (ANNs) to approximate the solutions, which is a significant contribution in this regard. We suggest employing this new method to solve a system of integral equations that explain the dynamics of infectious diseases instead of the classical numerical methods. Our study shows that, compared to the Adams-Bashforth algorithm, the ANN is a reliable candidate for solving the problems.
AbstractList In this paper, an analysis of a mathematical model of the coronavirus is carried out by using two fractal-fractional parameters. This dangerous virus infects a person through the mouth, eyes, nose or hands. This makes it so dangerous that no one can get rid of it. One of the main factors contributing to increasing infections of this deadly virus is crowding. We believe that it is necessary to model this effect mathematically to predict the possible outcomes. Hence, the study of neural network-based models related to the spread of this virus can yield new results. This paper also introduces the use of artificial neural networks (ANNs) to approximate the solutions, which is a significant contribution in this regard. We suggest employing this new method to solve a system of integral equations that explain the dynamics of infectious diseases instead of the classical numerical methods. Our study shows that, compared to the Adams-Bashforth algorithm, the ANN is a reliable candidate for solving the problems.
Author Tariboon, Jessada
Etemad, Sina
Bensayah, Abdallah
Rezapour, Shahram
Tellab, Brahim
Ntouyas, Sotiris K.
Najafi, Hashem
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SubjectTerms artificial neural networks
fixed-point theorem
fractal-fractional derivative
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Title Approximate numerical algorithms and artificial neural networks for analyzing a fractal-fractional mathematical model
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