| 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 |
| 4-Mar-2025 | A self-data-driven approach for online remaining useful life prediction of machinery using a recursive update strategy | Xu, P; Lei, Y; Wang, Z; Li, N; Cai, X; Feng, K |
| 22-Feb-2020 | A systematic review of unsupervised learning techniques for software defect prediction | Li, N; Shepperd, M; Guo, Y |
