Publications

@conference{Ibrahim202258,

title = {Fault detection in wheeled mobile robot based Machine Learning},

author = {F. Ibrahim and B. Boussaid and M. N. Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143829283\&doi=10.1109%2fSSD54932.2022.9955871\&partnerID=40\&md5=15010eba650e97082eb31d979c6c10f1},

doi = {10.1109/SSD54932.2022.9955871},

isbn = {9781665471084},

year  = {2022},

date = {2022-01-01},

journal = {2022 19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022},

pages = {58-63},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Robotics gained in importance the attention of researchers nowadays in many fields, in particular monitoring and control. Deployed in harsh environments, Artificial Intelligence has shown a powerful ability to detect and diagnose faults. In this paper, a classification of defects is evaluated using different machines. learning techniques such as Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Recurrent Neural network (RNN). A comparative analysis is carried out among the techniques previously mentioned on the basis of detection accuracy (DA), true Positive rate (TPR), Matthews correlation coefficients (MCC) and false alarm rate (FAR). © 2022 IEEE.},

note = {cited By 4},

keywords = {Classification of defects; Diagnosis faults; Faults detection; Harsh environment; Learning techniques; Machine-learning; Monitoring and control; Random forests; Support vectors machine; Wheeled mobile robot, Fault detection; Intelligent robots; Learning systems; Recurrent neural networks; Support vector machines, Mobile robots},

pubstate = {published},

tppubtype = {conference}

}