| Intelligent techniques in personalization of learning in e-learning systems U Markowska-Kaczmar, H Kwasnicka, M Paradowski Computational intelligence for technology enhanced learning, 1-23, 2010 | 44 | 2010 |
| Blinking artefact recognition in EEG signal using artificial neural network R Bogacz, U Markowska-Kaczmar, A Kozik Proc. of 4 th Conference on Neural Networks and Their Applications, Zakopane …, 1999 | 37 | 1999 |
| Rule extraction from neural network by genetic algorithm with pareto optimization U Markowska-Kaczmar, P Wnuk-Lipiński International Conference on Artificial Intelligence and Soft Computing, 450-455, 2004 | 35 | 2004 |
| Discovering the mysteries of neural networks U Markowska-Kaczmar, M Chumieja International Journal of Hybrid Intelligent Systems 1 (3-4), 153-163, 2004 | 30 | 2004 |
| Emotion-based image retrieval—An artificial neural network approach KA Olkiewicz, U Markowska-Kaczmar proceedings of the international multiconference on computer science and …, 2010 | 27 | 2010 |
| 3D robotic navigation using a vision-based deep reinforcement learning model P Zieliński, U Markowska-Kaczmar Applied Soft Computing 110, 107602, 2021 | 26 | 2021 |
| Computational methods for resting-state EEG of patients with disorders of consciousness S Corchs, G Chioma, R Dondi, F Gasparini, S Manzoni, ... Frontiers in neuroscience 13, 807, 2019 | 25 | 2019 |
| Learning assistant-personalizing learning paths in e-Learning environments H Kwasnicka, D Szul, U Markowska-Kaczmar, PB Myszkowski 2008 7th Computer Information Systems and Industrial Management Applications …, 2008 | 25 | 2008 |
| Comparison of attention-based deep learning models for eeg classification G Cisotto, A Zanga, J Chlebus, I Zoppis, S Manzoni, ... arXiv preprint arXiv:2012.01074, 2020 | 24 | 2020 |
| Fuzzy logic and evolutionary algorithm—two techniques in rule extraction from neural networks U Markowska-Kaczmar, W Trelak Neurocomputing 63, 359-379, 2005 | 24 | 2005 |
| Capillary abnormalities detection using vessel thickness and curvature analysis M Paradowski, U Markowska-Kaczmar, H Kwasnicka, K Borysewicz Knowledge-Based and Intelligent Information and Engineering Systems: 13th …, 2009 | 21 | 2009 |
| Multi-class iteratively refined negative selection classifier U Markowska-Kaczmar, B Kordas Applied Soft Computing 8 (2), 972-984, 2008 | 21 | 2008 |
| Spiking neural network vs multilayer perceptron: who is the winner in the racing car computer game U Markowska-Kaczmar, M Koldowski Soft Computing 19, 3465-3478, 2015 | 20 | 2015 |
| Data mining techniques in e-learning celgrid system PB Myszkowski, H Kwasnicka, U Markowska-Kaczmar 2008 7th Computer Information Systems and Industrial Management Applications …, 2008 | 17 | 2008 |
| Sieci neuronowe w zastosowaniach: praca zbiorowa U Markowska-Kaczmar, H Kwaśnicka | 17 | 2005 |
| Extraction of fuzzy rules from trained neural network using evolutionary algorithm. U Markowska-Kaczmar, W Trelak ESANN, 149-154, 2003 | 15 | 2003 |
| Deep learning—A new era in bridging the semantic gap U Markowska-Kaczmar, H Kwaśnicka Bridging the Semantic Gap in Image and Video Analysis, 123-159, 2018 | 14 | 2018 |
| American sign language fingerspelling recognition using wide residual networks K Kania, U Markowska-Kaczmar Artificial Intelligence and Soft Computing: 17th International Conference …, 2018 | 13 | 2018 |
| Evolutionary approaches to rule extraction from neural networks U Markowska-Kaczmar Engineering Evolutionary Intelligent Systems, 177-209, 2008 | 13 | 2008 |
| Extreme learning machine versus classical feedforward network: Comparison from the usability perspective U Markowska-Kaczmar, M Kosturek Neural Computing and Applications 33 (22), 15121-15144, 2021 | 12 | 2021 |
