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Billiards Optimization with Modified Deep Learning for Fault Detection in Wireless Sensor Network

Yousif Sufyan Jghef1, Mohammed Jasim Mohammed Jasim2, Subhi R. M. Zeebaree3,*, Rizgar R. Zebari4

1 Department of Computer Engineering, College of Engineering, Knowledge University, Erbil, 44001, Iraq
2 Engineering College, Al-Kitab University, Kirkuk, Iraq
3 Energy Eng. Department, Technical College of Engineering, Duhok Polytechnic University, Duhok, Iraq
4 Computer Science Department, College of Science, Nawroz University, Duhok, Iraq

* Corresponding Author: Subhi R. M. Zeebaree. Email: email

Computer Systems Science and Engineering 2023, 47(2), 1651-1664. https://doi.org/10.32604/csse.2023.037449

Abstract

Wireless Sensor Networks (WSNs) gather data in physical environments, which is some type. These ubiquitous sensors face several challenges responsible for corrupting them (mostly sensor failure and intrusions in external agents). WSNs were disposed to error, and effectual fault detection techniques are utilized for detecting faults from WSNs in a timely approach. Machine learning (ML) was extremely utilized for detecting faults in WSNs. Therefore, this study proposes a billiards optimization algorithm with modified deep learning for fault detection (BIOMDL-FD) in WSN. The BIOMDLFD technique mainly concentrates on identifying sensor faults to enhance network efficiency. To do so, the presented BIOMDL-FD technique uses the attention-based bidirectional long short-term memory (ABLSTM) method for fault detection. In the ABLSTM model, the attention mechanism enables us to learn the relationships between the inputs and modify the probability to give more attention to essential features. At the same time, the BIO algorithm is employed for optimal hyperparameter tuning of the ABLSTM model, which is stimulated by billiard games, showing the novelty of the work. Experimental analyses are made to affirm the enhanced fault detection outcomes of the BIOMDL-FD technique. Detailed simulation results demonstrate the improvement of the BIOMDL-FD technique over other models with a maximum classification accuracy of 99.37%.

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APA Style
Jghef, Y.S., Jasim, M.J.M., Zeebaree, S.R.M., Zebari, R.R. (2023). Billiards optimization with modified deep learning for fault detection in wireless sensor network. Computer Systems Science and Engineering, 47(2), 1651-1664. https://doi.org/10.32604/csse.2023.037449
Vancouver Style
Jghef YS, Jasim MJM, Zeebaree SRM, Zebari RR. Billiards optimization with modified deep learning for fault detection in wireless sensor network. Comput Syst Sci Eng. 2023;47(2):1651-1664 https://doi.org/10.32604/csse.2023.037449
IEEE Style
Y.S. Jghef, M.J.M. Jasim, S.R.M. Zeebaree, and R.R. Zebari "Billiards Optimization with Modified Deep Learning for Fault Detection in Wireless Sensor Network," Comput. Syst. Sci. Eng., vol. 47, no. 2, pp. 1651-1664. 2023. https://doi.org/10.32604/csse.2023.037449



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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