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ORCIDArtur Szymanski
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2020 – today
- 2020
[c10]Albert Sledzianowski
, Artur Szymanski, Aldona Drabik, Stanislaw Szlufik, Dariusz M. Koziorowski, Andrzej W. Przybyszewski:
Combining Results of Different Oculometric Tests Improved Prediction of Parkinson's Disease Development. ACIIDS (2) 2020: 517-526- [c9]Artur Chudzik, Artur Szymanski, Jerzy Pawel Nowacki, Andrzej W. Przybyszewski:
Eye-Tracking and Machine Learning Significance in Parkinson's Disease Symptoms Prediction. ACIIDS (2) 2020: 537-547
2010 – 2019
- 2019
- [c8]Albert Sledzianowski, Artur Szymanski, Aldona Drabik, Stanislaw Szlufik, Dariusz M. Koziorowski, Andrzej W. Przybyszewski:
Measurements of Antisaccades Parameters Can Improve the Prediction of Parkinson's Disease Progression. ACIIDS (2) 2019: 602-614 - [c7]Artur Chudzik, Artur Szymanski, Jerzy Pawel Nowacki, Andrzej W. Przybyszewski:
DTI Helps to Predict Parkinson's Patient's Symptoms Using Data Mining Techniques. ACIIDS (2) 2019: 615-623 - 2018
- [c6]Albert Sledzianowski, Artur Szymanski, Stanislaw Szlufik, Dariusz M. Koziorowski:
Rough Set Data Mining Algorithms and Pursuit Eye Movement Measurements Help to Predict Symptom Development in Parkinson's Disease. ACIIDS (2) 2018: 428-435 - 2017
- [c5]Artur Szymanski, Stanislaw Szlufik, Dariusz M. Koziorowski, Andrzej W. Przybyszewski:
Building Classifiers for Parkinson's Disease Using New Eye Tribe Tracking Method. ACIIDS (2) 2017: 351-358 - 2016
- [j9]Andrzej W. Przybyszewski, Mark A. Kon, Stanislaw Szlufik, Artur Szymanski, Piotr Habela, Dariusz M. Koziorowski:
Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson's Patients. Sensors 16(9): 1498 (2016) - [c4]Artur Szymanski, Stanislaw Szlufik, Dariusz M. Koziorowski, Piotr Habela, Andrzej W. Przybyszewski:
Building Intelligent Classifiers for Doctor-Independent Parkinson's Disease Treatments. ITIB (1) 2016: 267-276 - 2015
- [j8]Artur Szymanski:
Data mining and neural network simulations can help to improve Deep Brain Stimulation effects in Parkinson Disease. Comput. Sci. 16(2): 199-218 (2015) - [c3]Artur Szymanski, Stanislaw Szlufik, Justyna Dutkiewicz, Dariusz M. Koziorowski, Marek Cacko, Michal Nieniecki, Andrzej W. Przybyszewski:
Data mining using SPECT can predict neurological symptom development in Parkinson's patients. CYBCONF 2015: 218-223 - [c2]Anna Kubis, Artur Szymanski, Andrzej W. Przybyszewski:
Fuzzy Rough Sets Theory Applied to Parameters of Eye Movements Can Help to Predict Effects of Different Treatments in Parkinson's Patients. PReMI 2015: 325-334 - 2014
- [c1]Artur Szymanski, Andrzej W. Przybyszewski:
Rough Set Rules Help to Optimize Parameters of Deep Brain Stimulation in Parkinson's Patients. Brain Informatics and Health 2014: 345-356 - 2013
- [j7]Shonda Gosselin, Artur Szymanski, Adam Pawel Wojda:
Cyclic partitions of complete nonuniform hypergraphs and complete multipartite hypergraphs. Discret. Math. Theor. Comput. Sci. 15(2): 215-222 (2013) - 2010
- [j6]Artur Szymanski, A. Pawel Wojda:
Cyclic Partitions of Complete Uniform Hypergraphs. Electron. J. Comb. 17(1) (2010)
2000 – 2009
- 2009
- [j5]Artur Szymanski, A. Pawel Wojda:
A note on k-uniform self-complementary hypergraphs of given order. Discuss. Math. Graph Theory 29(1): 199-202 (2009) - [j4]Artur Szymanski, A. Pawel Wojda:
Self-complementing permutations of k-uniform hypergraphs. Discret. Math. Theor. Comput. Sci. 11(1): 117-124 (2009) - [j3]Lech Adamus, Beata Orchel, Artur Szymanski, A. Pawel Wojda, Malgorzata Zwonek:
A note on t-complementing permutations for graphs. Inf. Process. Lett. 110(2): 44-45 (2009) - 2008
- [j2]Aneta Dudek, Artur Szymanski, Malgorzata Zwonek:
(H, k) stable graphs with minimum size. Discuss. Math. Graph Theory 28(1): 137-149 (2008) - 2006
- [j1]Artur Szymanski, A. Pawel Wojda:
Self-complementary hypergraphs and their self-complementing permutations. Electron. Notes Discret. Math. 24: 291-294 (2006)
Coauthor Index
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