| Synthesizing rolling bearing fault samples in new conditions: A framework based on a modified CGAN M Ahang, M Jalayer, A Shojaeinasab, O Ogunfowora, T Charter, ... Sensors 22 (14), 5413, 2022 | 25 | 2022 |
| Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions M Khadivi, T Charter, M Yaghoubi, M Jalayer, M Ahang, A Shojaeinasab, ... Computers & Industrial Engineering, 110856, 2025 | 14 | 2025 |
| Intelligent condition monitoring of industrial plants: An overview of methodologies and uncertainty management strategies M Ahang, T Charter, O Ogunfowora, M Khadivi, M Abbasi, H Najjaran arXiv preprint arXiv:2401.10266, 2024 | 4 | 2024 |
| Condition Monitoring with Incomplete Data: An Integrated Variational Autoencoder and Distance Metric Framework M Ahang, M Abbasi, T Charter, H Najjaran arXiv preprint arXiv:2404.05891, 2024 | 2 | 2024 |
| FORLAPS: An Innovative Data-Driven Reinforcement Learning Approach for Prescriptive Process Monitoring M Abbasi, M Khadivi, M Ahang, P Lasserre, Y Lucet, H Najjaran arXiv preprint arXiv:2501.10543, 2025 | | 2025 |
| An Innovative Data-Driven Reinforcement Learning Approach for Prescriptive Process Monitoring M Abbasi, M Khadivi, M Ahang, P Lasserre, Y Lucet, H Najjaran Available at SSRN 5125541, 0 | | |
Homayoun NajjaranUniversity of VictoriaAdresse e-mail validée de uvic.ca