A stochastic computational scheme for the computer epidemic virus with delay effects

Wajaree Weera; Thongchai Botmart; Teerapong La-inchua; Zulqurnain Sabir; Rafaél Artidoro Sandoval Núñez; Marwan Abukhaled; Juan Luis García Guirao; Wajaree Weera; Thongchai Botmart; Teerapong La-inchua; Zulqurnain Sabir; Rafaél Artidoro Sandoval Núñez; Marwan Abukhaled; Juan Luis García Guirao

doi:10.3934/math.2023007

AIMS Mathematics

2023, Volume 8, Issue 1: 148-163. doi: 10.3934/math.2023007

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Research article

A stochastic computational scheme for the computer epidemic virus with delay effects

1.
Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand
2.
Department of Mathematics, Faculty of Science, University of Phayao, Phayao 56000, Thailand
3.
Department of Mathematical Sciences, United Arab Emirates University, P.O.Box 15551, Al Ain, UAE
4.
Department of Mathematics, Hazara University, Mansehra, Pakistan
5.
Universidad Nacional Autónoma de Chota, Cajamarca, Perú
6.
Department Department of Mathematics and Statistics, American University of Sharjah, Sharjah, United Arab Emirates
7.
Technical University of Cartagena, Applied Mathematics and Statistics Department, Spain

Received: 13 July 2022 Revised: 19 August 2022 Accepted: 01 September 2022 Published: 27 September 2022
MSC : 60H35, 92B20

This work aims to provide the numerical performances of the computer epidemic virus model with the time delay effects using the stochastic Levenberg-Marquardt backpropagation neural networks (LMBP-NNs). The computer epidemic virus model with the time delay effects is categorized into four dynamics, the uninfected S(x) computers, the latently infected L(x) computers, the breaking-out B(x) computers, and the antivirus PC's aptitude R(x). The LMBP-NNs approach has been used to numerically simulate three cases of the computer virus epidemic system with delay effects. The stochastic framework for the computer epidemic virus system with the time delay effects is provided using the selection of data with 11%, 13%, and 76% for testing, training, and verification together with 15 neurons. The proposed and data-based Adam technique is overlapped to execute the LMBP-NNs method's exactness. The constancy, authentication, precision, and capability of the LMBP-NNs scheme are perceived with the analysis of the state transition measures, regression actions, correlation performances, error histograms, and mean square error measures.
- computer epidemic virus model,
- delay,
- neural networks,
- nonlinear,
- Levenberg-Marquardt backpropagation
Citation: Wajaree Weera, Thongchai Botmart, Teerapong La-inchua, Zulqurnain Sabir, Rafaél Artidoro Sandoval Núñez, Marwan Abukhaled, Juan Luis García Guirao. A stochastic computational scheme for the computer epidemic virus with delay effects[J]. AIMS Mathematics, 2023, 8(1): 148-163. doi: 10.3934/math.2023007

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Abstract

This work aims to provide the numerical performances of the computer epidemic virus model with the time delay effects using the stochastic Levenberg-Marquardt backpropagation neural networks (LMBP-NNs). The computer epidemic virus model with the time delay effects is categorized into four dynamics, the uninfected S(x) computers, the latently infected L(x) computers, the breaking-out B(x) computers, and the antivirus PC's aptitude R(x). The LMBP-NNs approach has been used to numerically simulate three cases of the computer virus epidemic system with delay effects. The stochastic framework for the computer epidemic virus system with the time delay effects is provided using the selection of data with 11%, 13%, and 76% for testing, training, and verification together with 15 neurons. The proposed and data-based Adam technique is overlapped to execute the LMBP-NNs method's exactness. The constancy, authentication, precision, and capability of the LMBP-NNs scheme are perceived with the analysis of the state transition measures, regression actions, correlation performances, error histograms, and mean square error measures.

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