| Evaluation of tree-based ensemble machine learning models in predicting stock price direction of movement EK Ampomah, Z Qin, G Nyame Information 11 (6), 332, 2020 | 202 | 2020 |
| Stock market prediction with gaussian naïve bayes machine learning algorithm EK Ampomah, G Nyame, Z Qin, PC Addo, EO Gyamfi, M Gyan Informatica 45 (2), 2021 | 61 | 2021 |
| Stock market decision support modeling with tree-based AdaBoost ensemble machine learning models EK Ampomah, Z Qin, G Nyame, FE Botchey Informatica 44 (4), 2021 | 23 | 2021 |
| Evaluation of tree-based ensemble machine learning models in predicting stock price direction of movement. Information, 11 (6), 332 EK Ampomah, Z Qin, G Nyame | 12 | 2020 |
| Predicting fraud in mobile money transactions using machine learning: the effects of sampling techniques on the imbalanced dataset FE Botchey, Z Qin, K Hughes-Lartey, EK Ampomah Informatica 45 (7), 2022 | 9 | 2022 |
| Evaluation of tree-based ensemble machine learning models in predicting stock price direction of movement. Information 11 (6), 332 (2020) EK Ampomah, Z Qin, G Nyame | 6 | |
| Evaluation of tree-based ensemble machine learning models in predicting stock price direction of movement. Information. 2020; 11 (6): 332 EK Ampomah, Z Qin, G Nyame Publisher Full Text, 0 | 5 | |
| Knowledge management system implementation success: A social capital perspective G Nyame, EK Ampomah, M Adu-Gyamfi Human Systems Management 41 (1), 27-45, 2022 | 2 | 2022 |
| Stock Market Movement Predictability with Machine Learning Technique: An Evaluation Analysis of Support Vector Machine and Logistic Regression Models G Nyame, PC Addo, EK Ampomah, Z Qin | 1 | |
| Security quality of KMS and KMS adoption: The context of SMEs G Nyame, Z Qin, EK Ampomah Human Systems Management 41 (3), 357-374, 2022 | | 2022 |
