MEYER WAVELET NEURAL NETWORKS PROCEDURES TO INVESTIGATE THE NUMERICAL PERFORMANCES OF THE COMPUTER VIRUS SPREAD WITH KILL SIGNALS

Zulqurnain Sabir, Dumitru Baleanu, Muhammad Asif Zahoor Raja (), Ali S. Alshomrani () and Evren Hincal ()
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Zulqurnain Sabir: Department of Mathematics, Near East University, Nicosia 99138, Cyprus†Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
Dumitru Baleanu: ��Department of Mathematics, Cankaya University, Ankara, Turkey§Institute of Space Sciences, Magurele, Romania¶Department of Medical Research, China Medical, University Hospital, China Medical University, Taichung, Taiwan
Muhammad Asif Zahoor Raja: ��Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, R.O.C.
Ali S. Alshomrani: *Faculty of Science, Department Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Evren Hincal: Department of Mathematics, Near East University, Nicosia 99138, Cyprus

FRACTALS (fractals), 2023, vol. 31, issue 02, 1-19

Abstract: This study shows the design of the Meyer wavelet neural networks (WNNs) to perform the numerical solutions of the spread of computer virus with kill signals, i.e. SEIR-KS system. The optimization of the SEIR-KS system is performed by the Meyer WNNs together with the optimization through the genetic algorithm (GA) and sequential quadratic (SQ) programming, i.e. Meyer WNNs-GASQ programming. A sigmoidal-based log-sigmoid function is implemented as an activation function, while 10 numbers of neurons work with 120 variables throughout this study. The correctness of the proposed Meyer WNNs-GASQP programming is observed through the comparison of the obtained and reference numerical solutions. For the consistency and reliability of the Meyer WNNs-GASQ programming, an analysis based on different statistical procedures is performed using 40 numbers of independent executions. Moreover, the use of different statistical operators like mean, median, minimum, standard deviation and semi-interquartile range further validates the correctness of the Meyer WNNs-GASQ programming for solving the SEIR-KS system.

Keywords: Meyer Wavelet; Neural Networks; SEIR-KS; Computer Virus; Genetic Algorithm; Nonlinear; Sequential Quadratic Programming; Reference Solutions (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218348X2340025X

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