| Real-Time Task Assignment Approach Leveraging Reinforcement Learning with Evolution Strategies for Long-Term Latency Minimization in Fog Computing L Mai, NN Dao, M Park Sensors 18 (9), 2830, 2018 | 50 | 2018 |
| A Hybrid Machine Learning and Schedulability Analysis Method for the Verification of TSN Networks TL Mai, N Navet, J Migge 2019 15th IEEE International Workshop on Factory Communication Systems (WFCS …, 2019 | 34 | 2019 |
| On the use of supervised machine learning for assessing schedulability: application to Ethernet TSN TL Mai, N Navet, J Migge 27th International Conference on Real-Time Networks and Systems (RTNS 2019), 2019 | 27 | 2019 |
| A comparison of clustering algorithms for botnet detection based on network flow L Mai, M Park 2016 Eighth International Conference on Ubiquitous and Future Networks …, 2016 | 26 | 2016 |
| Using machine learning to speed up the design space exploration of Ethernet TSN networks N Navet, TL Mai, J Migge University of Luxembourg, 2019 | 25 | 2019 |
| Improvements to deep-learning-based feasibility prediction of switched ethernet network configurations T Long Mai, N Navet Proceedings of the 29th International Conference on Real-Time Networks and …, 2021 | 18 | 2021 |
| Cluster ensemble with link-based approach for botnet detection L Mai, DK Noh Journal of Network and Systems Management 26, 616-639, 2018 | 18 | 2018 |
| Deep learning to predict the feasibility of priority-based Ethernet network configurations TL Mai, N Navet ACM Transactions on Cyber-Physical Systems (TCPS) 5 (4), 1-26, 2021 | 15 | 2021 |
| Flow-based consensus partitions for botnet detection L Mai, YP Kim, DH Choi, NK Bao, TV Phan, M Park 2016 International Conference on Information and Communication Technology …, 2016 | 2 | 2016 |
| Machine Learning in the Design Space Exploration of TSN Networks TL Mai Unilu-University of Luxembourg, Esch-Sur-Alzette, Luxembourg, 2022 | 1 | 2022 |
| On the use of supervised machine learning for assessing schedulability TL Mai, N Navet, J Migge | | |
