2020 |
Azzouz, I.; Boussaid, B.; Zouinkhi, A.; Abdelkrim, M. Naceur Multi-faults classification in WSN: A deep learning approach Conférence Institute of Electrical and Electronics Engineers Inc., 2020, ISBN: 9781728188157, (cited By 11). Résumé | Liens | BibTeX | Étiquettes: Artificial intelligence techniques; Correlation coefficient; Detection accuracy; Learning techniques; Machine learning techniques; Multi layer perceptron; Probabilistic neural networks; True positive rates, Automation; Decision trees; Fault detection; Learning systems; Long short-term memory; Multilayer neural networks; Process control; Support vector machines; Wireless sensor networks, Deep learning @conference{Azzouz2020343,Wireless Sensor Networks are deployed in harsh environments. Their key advantage is there flexibility and low cost. But they can face many failures which created the need to improve data accuracy. Many artificial intelligence techniques has demonstrated impressive results in fault detection and faults diagnosis. Lately, machine learning emerged as a powerfull artificial intelligence based technique to solve the problem of failures in WSN. In this paper, a multi-fault classification is evaluated using deep learning technique based on LSTM classifier and then compared with different machine learning techniques such as Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP)and Probabilistic Neural Network (PNN). The performance of this mentioned techniques used for fault detection in WSNs were compared based on four metrics: Detection Accuracy (DA), True Positive Rate (TPR), Matthews Correlation Coefficients (MCC)and False Alarm (FA). © 2020 IEEE. |
Publications
2020 |
Multi-faults classification in WSN: A deep learning approach Conférence Institute of Electrical and Electronics Engineers Inc., 2020, ISBN: 9781728188157, (cited By 11). |