| Deep learning with dynamically weighted loss function for sensor-based prognostics and health management D Rengasamy, M Jafari, B Rothwell, X Chen, GP Figueredo Sensors 20 (3), 723, 2020 | 97 | 2020 |
| Deep learning approaches to aircraft maintenance, repair and overhaul: A review D Rengasamy, HP Morvan, GP Figueredo 2018 21st International Conference on Intelligent Transportation Systems …, 2018 | 46 | 2018 |
| Towards a more reliable interpretation of machine learning outputs for safety-critical systems using feature importance fusion D Rengasamy, BC Rothwell, GP Figueredo Applied Sciences 11 (24), 11854, 2021 | 28 | 2021 |
| Machine learning to determine the main factors affecting creep rates in laser powder bed fusion S Sanchez, D Rengasamy, CJ Hyde, GP Figueredo, B Rothwell Journal of Intelligent Manufacturing 32 (8), 2353-2373, 2021 | 28 | 2021 |
| Feature importance in machine learning models: A fuzzy information fusion approach D Rengasamy, JM Mase, A Kumar, B Rothwell, MT Torres, MR Alexander, ... Neurocomputing 511, 163-174, 2022 | 25 | 2022 |
| Load prediction using support vector regression LW Chong, D Rengasamy, YW Wong, RK Rajkumar TENCON 2017-2017 IEEE Region 10 Conference, 1069-1074, 2017 | 15 | 2017 |
| Anomaly detection for unmanned aerial vehicle sensor data using a stacked recurrent autoencoder method with dynamic thresholding V Bell, D Rengasamy, B Rothwell, GP Figueredo arXiv preprint arXiv:2203.04734, 2022 | 13 | 2022 |
| Asymmetric loss functions for deep learning early predictions of remaining useful life in aerospace gas turbine engines D Rengasamy, B Rothwell, GP Figueredo 2020 International Joint Conference on Neural Networks (IJCNN), 1-7, 2020 | 12 | 2020 |
| An intelligent toolkit for benchmarking data-driven aerospace prognostics D Rengasamy, JM Mase, B Rothwell, GP Figueredo 2019 IEEE intelligent transportation systems conference (ITSC), 4210-4215, 2019 | 5 | 2019 |
| Mechanistic interpretation of machine learning inference: A fuzzy feature importance fusion approach D Rengasamy, JM Mase, MT Torres, B Rothwell, DA Winkler, ... arXiv preprint arXiv:2110.11713, 2021 | 3 | 2021 |
| Towards a more reliable interpretation of machine learning outputs for safety-critical systems using feature importance fusion D Rengasamy, B Rothwell, G Figueredo arXiv preprint arXiv:2009.05501, 2020 | 3 | 2020 |
| System condition monitoring through Bayesian change point detection using pump vibrations E Tochev, D Rengasamy, H Pfifer, S Ratchev 2020 IEEE 16th International Conference on Automation Science and …, 2020 | 3 | 2020 |
| EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python A Kumar, JM Mase, D Rengasamy, B Rothwell, MT Torres, DA Winkler, ... International Conference on Machine Learning, Optimization, and Data Science …, 2022 | 2 | 2022 |
| Deep Learning Approaches to Aircraft Maintenance D Rengasamy, HP Morvan, GP Figueredo Repair and Overhaul: A Review, 150-156, 2018 | 2 | 2018 |
| Machine learning to determine the main factors affecting creep rates in laser powder bed fusion D Rengasamy, CJ Hyde, GP Figueredo, B Rothwell Journal of Intelligent Manufacturing 32 (8), 2021 | | 2021 |
| Embedding and Extracting Domain Knowledge in Machine Learning for Condition-based Maintenance D Rengasamy University of Nottingham, 0 | | |
Grazziela FigueredoAssociate Professor, University of Nottingham在 nottingham.ac.uk 的电子邮件经过验证
