Research group on MACHINE LEARNING

Faculty of Mathematics and Computer Science, Babes-Bolyai University, Romania

1. Nistor, S.C., Czibula, G., IntelliSwAS: Optimizing Deep Neural Network Architectures using a Particle Swarm-based Approach, Expert systems with Applications, Volume 187, January 2022, 11594 (WoS, Q1)
2. Diana-Lucia Miholca, Vlad-Ioan Tomescu, Gabriela Czibula, An in-depth analysis of the software features’ impact on the performance of deep learning-based software defect predictors, IEEE Access, Volume 10, 64801 – 64818, 2022 (WoS, Q2)
3. Gabriela Czibula, Mihaiela Lupea, Anamaria Briciu, Enhancing the performance of software authorship attribution using an ensemble of deep autoencoders, Mathematics, Special Issue "Recent Advances in Artificial Intelligence and Machine Learning", 2022, 10(15):2572 (WoS, Q1)
4. Gabriela Czibula, George Ciubotariu, Mariana Maier, Hannelore-Inge Lisei, IntelliDaM: A machine learning based framework for enhancing the performance of decision-making processes. A case study for educational data mining, IEEE Access, Volume 10, 80651-80666, 2022 (WoS, Q2)
5. Albu, A.-I., Czibula G., Mihai, A., Czibula, I.G., Burcea, S. Mezghani, A., NeXtNow: A Convolutional Deep Learning Model for the Prediction of Weather Radar Data for Nowcasting Purposes, Remote Sensing, Special Issue "Artificial Intelligence-Based Learning Approaches for Remote Sensing", 2022, 14(16), 3890 (WoS, Q2)
6. Ciubotariu, G, Czibula, G., MBMT-Net: A multitask learning based convolutional neural network architecture for dense tasks performance improvement, IEEE Access, 2022, Volume 10, pages 125600 - 125615 (WoS, Q2)
7. Mihaiela Lupea, Anamaria Briciu, Istvan-Gergely Czibula, Gabriela Czibula, SoftId: An autoencoder-based one-class classification model for software authorship identification, 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2022), Procedia Computer Science Volume 207, 2022, Pages 716-725 (WoS Proceedings)
8. Albu, Alexandra-Ioana, An Approach for Predicting Protein-Protein Interactions using Supervised Autoencoders, 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2022), Procedia Computer Science, Vol. 207 (2022), pp. 2023-2032 (WoS Proceedings)