| An in-depth analysis of the software features’ impact on the performance of deep learning-based software defect predictors DL Miholca, VI Tomescu, G Czibula IEEE Access 10, 64801-64818, 2022 | 11 | 2022 |
| COMET: A conceptual coupling based metrics suite for software defect prediction DL Miholca, G Czibula, V Tomescu Procedia Computer Science 176, 31-40, 2020 | 11 | 2020 |
| FoRConvD: An approach for food recognition on mobile devices using convolutional neural networks and depth maps VI Tomescu 2020 IEEE 14th International Symposium on Applied Computational Intelligence …, 2020 | 9 | 2020 |
| A comparative study on using unsupervised learning based data analysis techniques for breast cancer detection Ş Niţică, G Czibula, VI Tomescu 2020 IEEE 14th International Symposium on Applied Computational Intelligence …, 2020 | 9 | 2020 |
| A study on using deep autoencoders for imbalanced binary classification VI Tomescu, G Czibula, Ş Niţică Procedia Computer Science 192, 119-128, 2021 | 5 | 2021 |
| Enhancing the performance of image classification through features automatically learned from depth-maps G Ciubotariu, VI Tomescu, G Czibula International Conference on Computer Vision Systems, 68-81, 2021 | 3 | 2021 |
