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 Subject XGBoost
Showing results 1 to 5 of 5
| Issue Date | Title | Author(s) |
| 13-Jun-2023 | Complete revascularization is associated with higher mortality in patients with ST-elevation myocardial infarction, multi-vessel disease and shock defined by hyperlactataemia: results from the Harefield Shock Registry incorporating explainable machine learning | Tindale, A; Cretu, I; Meng, H; Panoulas, V |
| 10-Dec-2024 | Evaluating the Impact of Multi-Layer Data on Machine Learning Classifiers for Predicting Student Academic Performance | Alshaikh-Hasan, M; Ghinea, G |
| 2-Feb-2023 | Industry 4.0-Based Framework for Real-Time Prediction of Output Power of Multi-Emitter Laser Modules during the Assembly Process | Markatos, NG; Mousavi, A; Pippione, G; Paoletti, R |
| 1-Oct-2025 | A machine learning method for predicting the elongation to failure of Al–Si alloy in high pressure die casting combining experiment and modeling | Wu, H; Song, X; Lu, J; Dou, K; Wang, W; Zhang, Y; Fan, Z |
| 16-Aug-2025 | Runoff Prediction in the Xiangxi River Basin Under Climate Change: The Application of the HBV-XGBoost Coupled Model | Guo, J; Zhang, F; Li, W; Yang, A; Fan, Y; Li, J |
