Open Access

ARTICLE

Computational Analysis for Computer Network Model with Fuzziness

by Wafa F. Alfwzan¹, Dumitru Baleanu^2,3,4, Fazal Dayan^5,*, Sami Ullah⁵, Nauman Ahmed^4,6, Muhammad Rafiq^7,8, Ali Raza^4,9

1 Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of Mathematics, Cankaya University, Balgat, Ankara, 06530, Turkey
3 Institute of Space Sciences, Magurele-Bucharest, 077125, Romania
4 Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
5 Department of Mathematics, School of Science, University of Management and Technology, Lahore, 54000, Pakistan
6 Department of Mathematics and Statistics, University of Lahore, Lahore, 54590, Pakistan
7 Department of Mathematics, Faculty of Science and Technology, University of Central Punjab, Lahore, 54000, Pakistan
8 Near East University, Mathematics Research Center, Department of Mathematics, Near East Boulevard, PC: 99138, Nicosia/Mersin 10, Turkey
9 Department of Mathematics, Govt. Maulana Zafar Ali Khan Graduate College Wazirabad, Punjab Higher Education Department (PHED), Lahore, 54000, Pakistan

* Corresponding Author: Fazal Dayan. Email:

(This article belongs to the Special Issue: Human Behaviour Analysis using Fuzzy Neural Networks)

Intelligent Automation & Soft Computing 2023, 37(2), 1909-1924. https://doi.org/10.32604/iasc.2023.039249

Received 17 January 2023; Accepted 13 April 2023; Issue published 21 June 2023

Abstract

A susceptible, exposed, infectious, quarantined and recovered (SEIQR) model with fuzzy parameters is studied in this work. Fuzziness in the model arises due to the different degrees of susceptibility, exposure, infectivity, quarantine and recovery among the computers under consideration due to the different sizes, models, spare parts, the surrounding environments of these PCs and many other factors like the resistance capacity of the individual PC against the virus, etc. Each individual PC has a different degree of infectivity and resistance against infection. In this scenario, the fuzzy model has richer dynamics than its classical counterpart in epidemiology. The reproduction number of the developed model is studied and the equilibrium analysis is performed. Two different techniques are employed to solve the model numerically. Numerical simulations are performed and the obtained results are compared. Positivity and convergence are maintained by the suggested technique which are the main features of the epidemic models.

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Keywords

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