Digital analysis of discrete fractional order worms transmission in wireless sensor systems: performance validation by artificial intelligence

This article deals with a novel non-linear discrete fractional-order mathematical model connected with the spread of worms in the wireless sensor systems (WSSs). The proposed model classified into five classes such as, susceptible individuals, exposed individuals, infectious individuals, recovered i...

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Bibliographic Details
Published in:Modeling earth systems and environment 2025-02, Vol.11 (1), p.25, Article 25
Main Authors: Khan, Aziz, Abdeljawad, Thabet, Alkhawar, Hisham Mohammad
Format: Article
Language:English
Subjects:
Applied mathematics
Artificial intelligence
Chemistry and Earth Sciences
Computer Science
Earth and Environmental Science
Earth Sciences
Earth System Sciences
Ecosystems
Environment
Error analysis
Fixed points (mathematics)
Math. Appl. in Environmental Science
Mathematical Applications in the Physical Sciences
Mathematical models
Network analysis
Neural networks
Nonlinear systems
Original Article
Physics
Regression analysis
Regression models
Sensors
Statistics for Engineering
Training
Viruses
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Summary:This article deals with a novel non-linear discrete fractional-order mathematical model connected with the spread of worms in the wireless sensor systems (WSSs). The proposed model classified into five classes such as, susceptible individuals, exposed individuals, infectious individuals, recovered individuals, vaccinated individuals (software installation). This model provides a complete framework for insight the spread of viruses in vulnerable systems and recommends potential countermeasures. This study shows that the mean squared error (MSE) in the testing phase is minimized, signifying accurate predictions. Levenberg-Marquardt neural network analysis and artificial intelligence technique have been utilized to estimate the model’s performance, incorporating its training status, regression analysis, error distribution, and overall suitability. The model is fractionalized via discrete Caputo operator, while the existence and uniqueness of results are obtained through fixed-point theory. Numerical simulations demonstrate the model’s usefulness in capturing the transmission dynamics of malicious codes. The model data has been divided into specific proportions: 70% for training, 15% for validation, and 15% for testing. Numerical results are achieved to support and justify the results for different fractional order.
ISSN:2363-6203
2363-6211
DOI:10.1007/s40808-024-02237-3