| Machine learning and integrative analysis of biomedical big data B Mirza, W Wang, J Wang, H Choi, NC Chung, P Ping Genes 10 (2), 87, 2019 | 307 | 2019 |
| Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift B Mirza, Z Lin, N Liu Neurocomputing 149, 316-329, 2015 | 185 | 2015 |
| Weighted online sequential extreme learning machine for class imbalance learning B Mirza, Z Lin, KA Toh Neural processing letters 38, 465-486, 2013 | 127 | 2013 |
| Meta-cognitive online sequential extreme learning machine for imbalanced and concept-drifting data classification B Mirza, Z Lin Neural Networks 80, 79-94, 2016 | 93 | 2016 |
| Kernel based online learning for imbalance multiclass classification S Ding, B Mirza, Z Lin, J Cao, X Lai, TV Nguyen, J Sepulveda Neurocomputing 277, 139-148, 2018 | 80 | 2018 |
| Risk scoring for prediction of acute cardiac complications from imbalanced clinical data N Liu, ZX Koh, ECP Chua, LML Tan, Z Lin, B Mirza, MEH Ong IEEE journal of biomedical and health informatics 18 (6), 1894-1902, 2014 | 57 | 2014 |
| Unsupervised classification of multi-omics data during cardiac remodeling using deep learning NC Chung, B Mirza, H Choi, J Wang, D Wang, P Ping, W Wang Methods 166, 66-73, 2019 | 56 | 2019 |
| Dual-layer kernel extreme learning machine for action recognition TV Nguyen, B Mirza Neurocomputing 260, 123-130, 2017 | 39 | 2017 |
| Voting based weighted online sequential extreme learning machine for imbalance multi-class classification B Mirza, Z Lin, J Cao, X Lai 2015 ieee international symposium on circuits and systems (iscas), 565-568, 2015 | 37 | 2015 |
| Multi-layer online sequential extreme learning machine for image classification B Mirza, S Kok, F Dong Proceedings of ELM-2015 Volume 1: Theory, Algorithms and Applications (I), 39-49, 2016 | 28 | 2016 |
| Integrated dissection of cysteine oxidative post-translational modification proteome during cardiac hypertrophy J Wang, H Choi, NC Chung, Q Cao, DCM Ng, B Mirza, SB Scruggs, ... Journal of proteome research 17 (12), 4243-4257, 2018 | 19 | 2018 |
| Efficient representation learning for high-dimensional imbalance data B Mirza, S Kok, Z Lin, YK Yeo, X Lai, J Cao, J Sepulveda 2016 IEEE International Conference on Digital Signal Processing (DSP), 511-515, 2016 | 12 | 2016 |
| MARIM: mobile augmented reality for interactive manuals TV Nguyen, D Tan, B Mirza, J Sepulveda Proceedings of the 24th ACM international conference on Multimedia, 689-690, 2016 | 12 | 2016 |
| ASMIM: augmented reality authoring system for mobile interactive manuals TV Nguyen, B Mirza, D Tan, J Sepulveda Proceedings of the 12th International Conference on Ubiquitous Information …, 2018 | 11 | 2018 |
| One-vs-all for class imbalance learning B Mirza, Z Lin 2013 9th International Conference on Information, Communications & Signal …, 2013 | 8 | 2013 |
| Identifying temporal molecular signatures underlying cardiovascular diseases: A data science platform NC Chung, H Choi, D Wang, B Mirza, AR Pelletier, D Sigdel, W Wang, ... Journal of molecular and cellular cardiology 145, 54-58, 2020 | 7 | 2020 |
| Landmark recognition via sparse representation J Cao, Y Zhao, X Lai, T Chen, N Liu, B Mirza, Z Lin 2015 IEEE International Conference on Digital Signal Processing (DSP), 1030-1034, 2015 | 6 | 2015 |
| A clinical site workload prediction model with machine learning lifecycle B Mirza, X Li, K Lauwers, B Reddy, A Muller, C Wozniak, S Djali Healthcare Analytics 3, 100159, 2023 | 4 | 2023 |
| Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17) S Wang, LL Minku, N Chawla, X Yao arXiv preprint arXiv:1707.09425, 2017 | 2 | 2017 |
| The research of ELM ensemble learning on multi-class resampling imbalanced data X Wang, S Xing 2015 IEEE Advanced Information Technology, Electronic and Automation Control …, 2015 | 1 | 2015 |
