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Urszula Markowska-Kaczmar
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2020 – today
- 2024
- [j18]Michal Karol
, Martin Tabakov
Deep learning for cancer cell detection: do we need dedicated models? Artif. Intell. Rev. 57(3): 53 (2024) - 2021
- [j17]Piotr Zielinski
3D robotic navigation using a vision-based deep reinforcement learning model. Appl. Soft Comput. 110: 107602 (2021) - [j16]Mateusz Zimoch, Urszula Markowska-Kaczmar
Human flow recognition using deep networks and vision methods. Eng. Appl. Artif. Intell. 104: 104346 (2021) - [j15]Urszula Markowska-Kaczmar
Extreme learning machine versus classical feedforward network. Neural Comput. Appl. 33(22): 15121-15144 (2021) - [c47]Ewa Juralewicz, Urszula Markowska-Kaczmar
Capsule Network Versus Convolutional Neural Network in Image Classification - Comparative Analysis. ICCS (5) 2021: 17-30 - [c46]Anna Palczewska
Interpreting Neural Networks Prediction for a Single Instance via Random Forest Feature Contributions. ICCS (2) 2021: 140-153 - [c45]Urszula Markowska-Kaczmar
Creating herd behavior by virtual agents using neural networks. KES 2021: 437-446 - [c44]Daniel Popek, Urszula Markowska-Kaczmar
Utterance Style Transfer Using Deep Models. KES 2021: 2132-2141 - 2020
- [j14]Urszula Markowska-Kaczmar
Markov network versus recurrent neural network in forming herd behavior based on sight and simple sound communication. Appl. Soft Comput. 90: 106177 (2020) - [c43]Urszula Markowska-Kaczmar
Inference Ability Assessment of Modified Differential Neural Computer. ACIIDS (1) 2020: 380-391 - [i1]Giulia Cisotto, Alessio Zanga
Comparison of Attention-based Deep Learning Models for EEG Classification. CoRR abs/2012.01074 (2020)
2010 – 2019
- 2018
- [c42]Kacper Kania, Urszula Markowska-Kaczmar
American Sign Language Fingerspelling Recognition Using Wide Residual Networks. ICAISC (1) 2018: 97-107 - [c41]Urszula Markowska-Kaczmar
Is Convolutional Network Competitive for Vision Method in the Furniture Dowel Quality Control? INISTA 2018: 1-8 - 2016
- [c40]Bartlomiej Józef Dzienkowski, Urszula Markowska-Kaczmar
A* Heuristic Based on a Hierarchical Space Model Extracted from Game Replays. FedCSIS 2016: 21-30 - [c39]Bartlomiej Józef Dzienkowski, Christopher Strode, Urszula Markowska-Kaczmar
Employing Game Theory and Computational Intelligence to Find the Optimal Strategy of an Autonomous Underwater Vehicle against a Submarine. FedCSIS 2016: 31-40 - [c38]Urszula Markowska-Kaczmar
Caption-guided patent image segmentation. FedCSIS 2016: 261-270 - 2015
- [j13]Urszula Markowska-Kaczmar, Agnieszka Szymanska, Lukasz Culer:
Automatic Indication of Heat Loss in Thermograms of Buildings. Int. J. Comput. Sci. Appl. 12(2): 57-69 (2015) - [j12]Grzegorz Kamola, Michal Spytkowski, Mariusz Paradowski, Urszula Markowska-Kaczmar
Image-based logical document structure recognition. Pattern Anal. Appl. 18(3): 651-665 (2015) - [j11]Urszula Markowska-Kaczmar
Spiking neural network vs multilayer perceptron: who is the winner in the racing car computer game. Soft Comput. 19(12): 3465-3478 (2015) - [c37]Urszula Markowska-Kaczmar
The Selected Metaheuristics Efficacy Assessment for a Given Class of Problems. ACIIDS (1) 2015: 418-429 - [c36]Jerzy Sas, Urszula Markowska-Kaczmar
Logical structure recognition of diagram images. FedCSIS 2015: 215-224 - [c35]Katarzyna Nizalowska, Urszula Markowska-Kaczmar
A Language-Independent Method for Detection and Correction of Alignment Errors in Parallel Corpora. NLDB 2015: 335-346 - 2014
- [j10]Bartosz Krawczyk, Urszula Markowska-Kaczmar
Recent Advances in Applied Computational Intelligence. Appl. Artif. Intell. 28(3): 217-219 (2014) - [c34]Katarzyna Nizalowska, Lukasz Burdka, Urszula Markowska-Kaczmar
Indoor head detection and tracking on RGBD images. FedCSIS 2014: 679-686 - [c33]Urszula Markowska-Kaczmar
Automatic information extraction from heatmaps. IISA 2014: 267-272 - [c32]Damian Olczyk, Urszula Markowska-Kaczmar
Fast learning method of interval type-2 fuzzy neural networks. UKCI 2014: 1-6 - 2013
- [c31]Bartosz Buczek, Urszula Markowska-Kaczmar
Recognition of Vehicle Motion Patterns in Video Sequences. ICSS 2013: 337-344 - 2012
- [j9]Jerzy Sas, Urszula Markowska-Kaczmar
Similarity-based training set acquisition for continuous handwriting recognition. Inf. Sci. 191: 226-244 (2012) - [j8]Bartlomiej Józef Dzienkowski, Urszula Markowska-Kaczmar
Agent Cooperation within Adversarial Teams in Dynamic Environment - Key Issues and Development Trends. Trans. Comput. Collect. Intell. 6: 146-169 (2012) - [c30]Bartlomiej Józef Dzienkowski, Urszula Markowska-Kaczmar
Plan and Goal Structure Reconstruction: An Automated and Incremental Method Based on Observation of a Single Agent. CISIM 2012: 290-301 - [c29]Roman Pawlikowski, Krzysztof Ociepa
Information Extraction from Geographical Overview Maps. ICCCI (1) 2012: 94-103 - 2011
- [j7]Halina Kwasnicka
Flocking behaviour in simple ecosystems as a result of artificial evolution. Appl. Soft Comput. 11(1): 982-990 (2011) - [c28]Urszula Markowska-Kaczmar
Graphical Pattern Identification Inspired by Perception. ACIIDS (2) 2011: 222-231 - [c27]Urszula Markowska-Kaczmar
Towards Automatic Image Annotation Supporting Document Understanding. HAIS (1) 2011: 420-427 - [c26]Urszula Markowska-Kaczmar
Similar Image Recognition Inspired by Visual Cortex. MICAI (2) 2011: 386-397 - 2010
- [c25]Urszula Markowska-Kaczmar
The Influence of Using Design Patterns on the Process of Implementing Genetic Algorithms. IEA/AIE (2) 2010: 173-182 - [c24]Katarzyna Agnieszka Olkiewicz, Urszula Markowska-Kaczmar:
Emotion-based Image Retrieval - an Artificial Neural Network Approach. IMCSIT 2010: 89-96 - [c23]Bartlomiej Józef Dzienkowski, Urszula Markowska-Kaczmar
Biologically Inspired Agent System Based on Spiking Neural Network. KES-AMSTA (2) 2010: 110-119 - [p3]Urszula Markowska-Kaczmar
Intelligent Techniques in Personalization of Learning in e-Learning Systems. Computational Intelligence for Technology Enhanced Learning 2010: 1-23
2000 – 2009
- 2009
- [c22]Urszula Markowska-Kaczmar
Kohonen's neural network and evolutionary algorithms in searching for financial investment strategy. IMCSIT 2009: 75-80 - [c21]Urszula Markowska-Kaczmar
Localization of cancerous changes in images of breast tissue. IMCSIT 2009: 413-417 - [c20]Mariusz Paradowski, Urszula Markowska-Kaczmar
Capillary Abnormalities Detection Using Vessel Thickness and Curvature Analysis. KES (2) 2009: 151-158 - [c19]Urszula Markowska-Kaczmar
Combined Unsupervised-Supervised Classification Method. KES (2) 2009: 861-868 - 2008
- [j6]Urszula Markowska-Kaczmar
Multi-class iteratively refined negative selection classifier. Appl. Soft Comput. 8(2): 972-984 (2008) - [c18]Marcin Bartoszewski, Halina Kwasnicka
Extraction of Emotional Content from Music Data. CISIM 2008: 293-299 - [c17]Halina Kwasnicka
Learning Assistant - Personalizing Learning Paths in e-Learning Environments. CISIM 2008: 308-314 - [c16]Pawel B. Myszkowski
Data Mining Techniques in e-Learning CelGrid System. CISIM 2008: 315-319 - [c15]Urszula Markowska-Kaczmar
Evaluation of Subspace Clustering Quality. HAIS 2008: 400-407 - [c14]Urszula Markowska-Kaczmar
Genetic Algorithm as a Tool for Stock Market Modelling. ICAISC 2008: 450-459 - [c13]Urszula Markowska-Kaczmar
Discovery of technical analysis patterns. IMCSIT 2008: 195-200 - [p2]Urszula Markowska-Kaczmar
Evolutionary Approaches to Rule Extraction from Neural Networks. Engineering Evolutionary Intelligent Systems 2008: 177-209 - 2007
- [c12]Jerzy Sas, Urszula Markowska-Kaczmar
Semi-automatic Training Sets Acquisition for Handwriting Recognition. CAIP 2007: 531-538 - [c11]Jerzy Sas, Urszula Markowska-Kaczmar
Semi-Supervised Handwritten Word Segmentation Using Character Samples Similarity Maximization and Evolutionary Algorithm. CISIM 2007: 316-321 - 2006
- [j5]Halina Kwasnicka, Urszula Markowska-Kaczmar, Jacek Tomasiak:
Visualization as a method for relationship discovery in data Halina. Ann. UMCS Informatica 4(1): 198-203 (2006) - [j4]Urszula Markowska-Kaczmar, Bartosz Kordas:
Mining of an electrocardiogram. Ann. UMCS Informatica 4(1): 218-229 (2006) - [j3]Urszula Markowska-Kaczmar, Krystyna Mularczyk:
Rule extraction from a neural network by hierarchical multiobjective genetic algorithm. Ann. UMCS Informatica 4(1): 230-243 (2006) - [c10]Urszula Markowska-Kaczmar
Creativity of Neural Networks. ICAISC 2006: 86-93 - [c9]Urszula Markowska-Kaczmar
Experimental Study of Evolutionary Based Method of Rule Extraction from Neural Networks in Medical Data. ICDM 2006: 76-90 - [c8]Michal Glomba, Urszula Markowska-Kaczmar
IBUSCA: A Grid-based Bottom-up Subspace Clustering Algorithm. ISDA (1) 2006: 671-676 - [c7]Urszula Markowska-Kaczmar
Negative Selection based method for Multi-Class problem classification. ISDA (2) 2006: 1165-1170 - [c6]Jan Kwiatkowski, Marcin Pawlik, Urszula Markowska-Kaczmar
Performance Evaluation of Different Kohenen Network Parallelization Techniques. PARELEC 2006: 331-336 - [p1]Urszula Markowska-Kaczmar, Krystyna Mularczyk:
GA-Based Pareto Optimization for Rule Extraction from Neural Networks. Multi-Objective Machine Learning 2006: 313-338 - 2005
- [j2]Urszula Markowska-Kaczmar
Fuzzy logic and evolutionary algorithm - two techniques in rule extraction from neural networks. Neurocomputing 63: 359-379 (2005) - [c5]Urszula Markowska-Kaczmar
The Influence of Parameters in Evolutionary Based Rule Extraction Method from Neural Network. ISDA 2005: 106-111 - [c4]Urszula Markowska-Kaczmar
Support Vector Machines in Handwritten Digits Classification. ISDA 2005: 406-411 - 2004
- [j1]Urszula Markowska-Kaczmar, Marcin Chumieja:
Discovering the Mysteries of Neural Networks. Int. J. Hybrid Intell. Syst. 1(3-4): 153-163 (2004) - [c3]Urszula Markowska-Kaczmar
Rule Extraction from Neural Network by Genetic Algorithm with Pareto Optimization. ICAISC 2004: 450-455 - 2003
- [c2]Urszula Markowska-Kaczmar, Wojciech Trelak:
Extraction of fuzzy rules from trained neural network using evolutionary algorithm. ESANN 2003: 149-154 - [c1]Urszula Markowska-Kaczmar, Marcin Chumieja:
Opening Neural Network Black Box by Evolutionary Approach. HIS 2003: 147-156
Coauthor Index
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last updated on 2024-10-07 22:23 CEST by the dblp team
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