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Tarek BERGHOUT
Tarek BERGHOUT
Laboratory of Automation and Manufacturing Engineering University of Batna2 Batna, 05000, Algeria
Adresse e-mail validée de univ-batna2.dz - Page d'accueil
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Année
Aircraft engines remaining useful life prediction with an adaptive denoising online sequential extreme learning machine
T Berghout, LH Mouss, O Kadri, L Saïdi, M Benbouzid
Engineering Applications of Artificial Intelligence 96, 103936, 2020
692020
Machine learning for cybersecurity in smart grids: A comprehensive review-based study on methods, solutions, and prospects
T Berghout, M Benbouzid, SM Muyeen
International Journal of Critical Infrastructure Protection 38, 100547, 2022
492022
A systematic guide for predicting remaining useful life with machine learning
T Berghout, M Benbouzid
Electronics 11 (7), 1125, 2022
472022
Aircraft engines remaining useful life prediction with an improved online sequential extreme learning machine
T Berghout, LH Mouss, O Kadri, L Saïdi, M Benbouzid
Applied Sciences 10 (3), 1062, 2020
372020
Machine learning-based condition monitoring for PV systems: State of the art and future prospects
T Berghout, M Benbouzid, T Bentrcia, X Ma, S Djurović, LH Mouss
Energies 14 (19), 6316, 2021
322021
2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT
O Friha, MA Ferrag, M Benbouzid, T Berghout, B Kantarci, KKR Choo
Computers & Security 127, 103097, 2023
302023
Intelligent condition monitoring of wind power systems: State of the art review
M Benbouzid, T Berghout, N Sarma, S Djurović, Y Wu, X Ma
Energies 14 (18), 5967, 2021
282021
A semi-supervised deep transfer learning approach for rolling-element bearing remaining useful life prediction
T Berghout, LH Mouss, T Bentrcia, M Benbouzid
IEEE Transactions on Energy Conversion 37 (2), 1200-1210, 2021
252021
Leveraging label information in a knowledge-driven approach for rolling-element bearings remaining useful life prediction
T Berghout, M Benbouzid, LH Mouss
Energies 14 (8), 2163, 2021
232021
A deep supervised learning approach for condition-based maintenance of naval propulsion systems
T Berghout, LH Mouss, T Bentrcia, E Elbouchikhi, M Benbouzid
Ocean Engineering 221, 108525, 2021
202021
Cybersecurity enhancement of smart grid: Attacks, methods, and prospects
U Inayat, MF Zia, S Mahmood, T Berghout, M Benbouzid
Electronics 11 (23), 3854, 2022
172022
Auto-NAHL: A neural network approach for condition-based maintenance of complex industrial systems
T Berghout, M Benbouzid, SM Muyeen, T Bentrcia, LH Mouss
IEEE Access 9, 152829-152840, 2021
142021
ProgNet: a transferable deep network for aircraft engine damage propagation prognosis under real flight conditions
T Berghout, MD Mouss, LH Mouss, M Benbouzid
Aerospace 10 (1), 10, 2022
132022
EL-NAHL: Exploring labels autoencoding in augmented hidden layers of feedforward neural networks for cybersecurity in smart grids
T Berghout, M Benbouzid
Reliability Engineering & System Safety 226, 108680, 2022
112022
Machine learning for photovoltaic systems condition monitoring: A review
T Berghout, M Benbouzid, X Ma, S Djurović, LH Mouss
IECON 2021–47th Annual Conference of the IEEE Industrial Electronics Society …, 2021
92021
Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering Approach
T Berghout, M Benbouzid, Y Amirat
Electronics 12 (12), 2554, 2023
62023
Federated learning for condition monitoring of industrial processes: a review on fault diagnosis methods, challenges, and prospects
T Berghout, M Benbouzid, T Bentrcia, WH Lim, Y Amirat
Electronics 12 (1), 158, 2022
62022
A heterogeneous federated transfer learning approach with extreme aggregation and speed
T Berghout, T Bentrcia, MA Ferrag, M Benbouzid
Mathematics 10 (19), 3528, 2022
62022
Lithium-ion battery state of health prediction with a robust collaborative augmented hidden layer feedforward neural network approach
T Berghout, M Benbouzid, Y Amirat, G Yao
IEEE Transactions on Transportation Electrification, 2023
52023
Exposing deep representations to a recurrent expansion with multiple repeats for fuel cells time series prognosis
T Berghout, M Benbouzid, T Bentrcia, Y Amirat, LH Mouss
Entropy 24 (7), 1009, 2022
52022
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