@Article{csse.2023.037449,
AUTHOR = {Yousif Sufyan Jghef, Mohammed Jasim Mohammed Jasim, Subhi R. M. Zeebaree, Rizgar R. Zebari},
TITLE = {Billiards Optimization with Modified Deep Learning for Fault Detection in Wireless Sensor Network},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {47},
YEAR = {2023},
NUMBER = {2},
PAGES = {1651--1664},
URL = {http://www.techscience.com/csse/v47n2/53683},
ISSN = {},
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%.},
DOI = {10.32604/csse.2023.037449}
}