Brunel University Research Archive(BURA) preserves and enables easy and open access to all
types of digital content. It showcases Brunel's research outputs.
Research contained within BURA is open access, although some publications may be subject
to publisher imposed embargoes. All awarded PhD theses are also archived on BURA.
Browsing by Author Li, N
Showing results 1 to 7 of 7
| Issue Date | Title | Author(s) |
| 2020 | Applications of machine learning to machine fault diagnosis: A review and roadmap | Lei, Y; Yang, B; Jiang, X; Jia, F; Li, N; Nandi, AK |
| 18-Jun-2024 | Improving classifier-based effort-aware software defect prediction by reducing ranking errors | Guo, Y; Shepperd, M; Li, N |
| 26-May-2025 | Machinery Multimodal Uncertainty-Aware RUL Prediction: A Stochastic Modeling Framework for Uncertainty Quantification and Informed Fusion | Wang, Y; Lei, Y; Li, N; Feng, K; Wang, Z; Tan, Y; Li, H |
| 27-May-2018 | Poster: Bridging effort-Aware prediction and strong classification: A just-in-Time software defect prediction study | Guo, Y; Shepperd, M; Li, N |
| 2019 | The Prevalence of Errors in Machine Learning Experiments | Shepperd, M; Guo, Y; Li, N; Arzoky, M; Capiluppi, A; Counsell, S; Destefanis, G; Swift, S; Tucker, A; Yousefi, L |
| 21-Dec-2021 | Residual Convolution LSTM Network for Machines Remaining Useful Life Prediction and Uncertainty Quantification | Wang, W; Lei, Y; Yan, T; Li, N; Nandi, AK |
| 22-Feb-2020 | A systematic review of unsupervised learning techniques for software defect prediction | Li, N; Shepperd, M; Guo, Y |
