| Supply chain risk management with machine learning technology: A literature review and future research directions M Yang, MK Lim, Y Qu, D Ni, Z Xiao Computers & Industrial Engineering 175, 108859, 2023 | 77 | 2023 |
| Deep neural networks with L1 and L2 regularization for high dimensional corporate credit risk prediction M Yang, MK Lim, Y Qu, X Li, D Ni Expert Systems with Applications 213, 118873, 2023 | 51 | 2023 |
| Repair missing data to improve corporate credit risk prediction accuracy with multi-layer perceptron M Yang, MK Lim, Y Qu, X Li, D Ni Soft Computing 26 (18), 9167-9178, 2022 | 9 | 2022 |
| Monitoring corporate credit risk with multiple data sources D Ni, MK Lim, X Li, Y Qu, M Yang Industrial Management & Data Systems 123 (2), 434-450, 2023 | 8 | 2023 |
| Using deep learning to interpolate the missing data in time-series for credit risks along supply chain W Zhang, MK Lim, M Yang, X Li, D Ni Industrial Management & Data Systems 123 (5), 1401-1417, 2023 | 7 | 2023 |
| Institutional investor horizons and stock price crash risk F Fu, J Fang, M Yang, S Yao Research in International Business and Finance 72, 102509, 2024 | 4 | 2024 |
| Using random forest to find the discontinuity points for carbon efficiency during COVID-19 Y Qu, MK Lim, M Yang, D Ni, Z Xiao Soft Computing 27 (22), 16537-16549, 2023 | 1 | 2023 |
| Enhancing Stock Prediction ability through News Perspective and Deep Learning with attention mechanisms M Yang, F Fu, D Ni, Z Xiao Soft Computing, 1-10, 2025 | | 2025 |
| Discovering the value of news: Evidence from the stock market M Yang, D Ni, Y Qu, Z Xiao AIP Conference Proceedings 2691 (1), 2023 | | 2023 |
| The Impact of Energy Crisis and Covid-19 Recovery on Carbon Efficiency in China Y Qu, MK Lim, M Yang, D Ni, Z Xiao Available at SSRN 4033438, 2022 | | 2022 |
