Featured articles

Longo, L., Brcic, M. & al. (2024) Explainable artificial intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions. Information Fusion, vol. 106, doi:10.1016/j.inffus.2024.102301
Doncevic, J., Brcic, M. & al. (2024) Mask–Mediator–Wrapper Architecture as a Data Mesh Driver. IEEE Transactions on Software Engineering, 50 (04), 900-910, doi:10.1109/TSE.2024.3367126
Brcic, M. & Yampolskiy, R. (2023) Impossibility Results in AI: a survey. ACM Computing Surveys, Volume 56, Issue 1, 1-24, doi:10.1145/3603371
Krleza, D., Vrdoljak, B. & Brcic, M. (2021) Statistical hierarchical clustering algorithm for outlier detection in evolving data streams. Machine learning, Volume 110, pp 139–184, doi:10.1007/s10994-020-05905-4
Brcic, M., Katic, M. & Hlupic, N. (2019) Planning horizons based proactive rescheduling for stochastic resource-constrained project scheduling problems. European Journal of Operational Research, 273 (1), 58-66 doi:10.1016/j.ejor.2018.07.037
Krleza, D., Vrdoljak, B. & Brcic, M. (2019) Latent Process Discovery Using Evolving Tokenized Transducer. IEEE access, 7, 169657-169676 doi:10.1109/ACCESS.2019.2955245
Dosilovic, F., Brcic, M. & Hlupic, N. (2018) Explainable Artificial Intelligence: A Survey. U: Skala, K. (ur.) Proceedings of the 41st International Convention on Information and Communication Technology, Electronics and Microelectronics MIPRO 2018. Rijeka, Croatia, Croatian Society for Information and Communication Technology, Electronics and Microelectronics – MIPRO, str. 210-215 doi:10.23919/MIPRO.2018.8400040

Student Theses

Controlling the supply chain network with a multiagent reinforcement learning-based system
Controlling the distribution network with a deep reinforcement learning-based agent
Exact solving machine scheduling problems assisted by graph neural networks
Artificial intelligence alignment using debate
Intelligent agent for Tetris
Self-supervised learning method for recommender systems based on community detection in graphs
Self-supervised learning method for recommender systems based on random walks in graphs
Content-based recommender models for authored textual materials based on deep learning
Content-based recommender models for authored textual materials based on decision trees and forests
Program library of time scheduling methods
A generic framework for automatic dictation correction