Publications

@conference{Aloui2023b,

title = {Estimating and enlarging the domain of attraction for polynomial systems using a deep learning tool},

author = {Messaoud Aloui and Faical Hamidi and Houssem Jerbi and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182927297\&doi=10.1109%2fIW_MSS59200.2023.10369522\&partnerID=40\&md5=244fee6b04ee656e55d354fe1fac648e},

doi = {10.1109/IW_MSS59200.2023.10369522},

year  = {2023},

date = {2023-01-01},

journal = {2023 IEEE International Workshop on Mechatronics Systems Supervision, IW_MSS 2023},

abstract = {This Paper deals with the topic of non linear polynomial systems. It explains a way to estimate and enlarge the region of attraction of nonlinear polynomial systems. It provides a deep learning method for estimating the domain of attraction and uses the Particle Swarm Optimization Algorithm to enlarge this domain. Based on an analytic method found in literature, a dataset is generated, used then to train an artificial neural network, which will be an objective function of an optimization algorithm. This method dives an imitation to a previous complicated method, with less complexity and les elapsed time. The benchmark examples show the efficiency of the method and compare results with those obtained with the one using linear matrix inequalities. © 2023 IEEE.},

keywords = {Deep learning, Domain of attraction, Learning systems, Learning tool, Linear matrix inequalities, Linear polynomials, Lyapunov functions, Lyapunov's functions, Neural networks, Non linear, Particle swarm, Particle swarm optimization, Particle swarm optimization (PSO), Polynomial systems, Polynomials, Swarm intelligence, Swarm optimization},

pubstate = {published},

tppubtype = {conference}

}

@conference{Aloui2023,

title = {Estimating and enlarging the domain of attraction for polynomial systems using a deep learning tool},

author = {Messaoud Aloui and Faical Hamidi and Houssem Jerbi and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182927297\&doi=10.1109%2fIW_MSS59200.2023.10369522\&partnerID=40\&md5=244fee6b04ee656e55d354fe1fac648e},

doi = {10.1109/IW_MSS59200.2023.10369522},

year  = {2023},

date = {2023-01-01},

journal = {2023 IEEE International Workshop on Mechatronics Systems Supervision, IW_MSS 2023},

abstract = {This Paper deals with the topic of non linear polynomial systems. It explains a way to estimate and enlarge the region of attraction of nonlinear polynomial systems. It provides a deep learning method for estimating the domain of attraction and uses the Particle Swarm Optimization Algorithm to enlarge this domain. Based on an analytic method found in literature, a dataset is generated, used then to train an artificial neural network, which will be an objective function of an optimization algorithm. This method dives an imitation to a previous complicated method, with less complexity and les elapsed time. The benchmark examples show the efficiency of the method and compare results with those obtained with the one using linear matrix inequalities. © 2023 IEEE.},

keywords = {Deep learning, Domain of attraction, Learning systems, Learning tool, Linear matrix inequalities, Linear polynomials, Lyapunov functions, Lyapunov's functions, Neural networks, Non linear, Particle swarm, Particle swarm optimization, Particle swarm optimization (PSO), Polynomial systems, Polynomials, Swarm intelligence, Swarm optimization},

pubstate = {published},

tppubtype = {conference}

}

@conference{Azzouz2020343,

title = {Multi-faults classification in WSN: A deep learning approach},

author = {I. Azzouz and B. Boussaid and A. Zouinkhi and M. Naceur Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100457622\&doi=10.1109%2fSTA50679.2020.9329325\&partnerID=40\&md5=2bfe3a1c47a0a67841399605dc4889ff},

doi = {10.1109/STA50679.2020.9329325},

isbn = {9781728188157},

year  = {2020},

date = {2020-01-01},

journal = {Proceedings - STA 2020: 2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering},

pages = {343-348},

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

abstract = {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.},

note = {cited By 11},

keywords = {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},

pubstate = {published},

tppubtype = {conference}

}