Self-complementary hypergraphs and their self-complementing permutations. - Artur Szymanski; A. Pawel Wojda (Electron. Notes Discret. Math., 2006)

(H, k) stable graphs with minimum size. - Aneta Dudek; Artur Szymanski; Malgorzata Zwonek (Discuss. Math. Graph Theory, 2008)

A note on k-uniform self-complementary hypergraphs of given order. - Artur Szymanski; A. Pawel Wojda (Discuss. Math. Graph Theory, 2009)

Self-complementing permutations of k-uniform hypergraphs. - Artur Szymanski; A. Pawel Wojda (Discret. Math. Theor. Comput. Sci., 2009)

A note on t-complementing permutations for graphs. - Lech Adamus; Beata Orchel; Artur Szymanski; A. Pawel Wojda; Malgorzata Zwonek (Inf. Process. Lett., 2009)

Cyclic Partitions of Complete Uniform Hypergraphs. - Artur Szymanski; A. Pawel Wojda (Electron. J. Comb., 2010)

Cyclic partitions of complete nonuniform hypergraphs and complete multipartite hypergraphs. - Shonda Gosselin; Artur Szymanski; Adam Pawel Wojda (Discret. Math. Theor. Comput. Sci., 2013)

Rough Set Rules Help to Optimize Parameters of Deep Brain Stimulation in Parkinson's Patients. - Artur Szymanski; Andrzej W. Przybyszewski (Brain Informatics and Health, 2014)

Data mining using SPECT can predict neurological symptom development in Parkinson's patients. - Artur Szymanski; Stanislaw Szlufik; Justyna Dutkiewicz; Dariusz M. Koziorowski; Marek Cacko; Michal Nieniecki; Andrzej W. Przybyszewski (CYBCONF, 2015)

Fuzzy Rough Sets Theory Applied to Parameters of Eye Movements Can Help to Predict Effects of Different Treatments in Parkinson's Patients. - Anna Kubis; Artur Szymanski; Andrzej W. Przybyszewski (PReMI, 2015)

Data mining and neural network simulations can help to improve Deep Brain Stimulation effects in Parkinson Disease. - Artur Szymanski (Comput. Sci., 2015)

Building Intelligent Classifiers for Doctor-Independent Parkinson's Disease Treatments. - Artur Szymanski; Stanislaw Szlufik; Dariusz M. Koziorowski; Piotr Habela; Andrzej W. Przybyszewski (ITIB (1), 2016)

Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson's Patients. - Andrzej W. Przybyszewski; Mark A. Kon; Stanislaw Szlufik; Artur Szymanski; Piotr Habela; Dariusz M. Koziorowski (Sensors, 2016)

Building Classifiers for Parkinson's Disease Using New Eye Tribe Tracking Method. - Artur Szymanski; Stanislaw Szlufik; Dariusz M. Koziorowski; Andrzej W. Przybyszewski (ACIIDS (2), 2017)

Rough Set Data Mining Algorithms and Pursuit Eye Movement Measurements Help to Predict Symptom Development in Parkinson's Disease. - Albert Sledzianowski; Artur Szymanski; Stanislaw Szlufik; Dariusz M. Koziorowski (ACIIDS (2), 2018)

DTI Helps to Predict Parkinson's Patient's Symptoms Using Data Mining Techniques. - Artur Chudzik; Artur Szymanski; Jerzy Pawel Nowacki; Andrzej W. Przybyszewski (ACIIDS (2), 2019)

Measurements of Antisaccades Parameters Can Improve the Prediction of Parkinson's Disease Progression. - Albert Sledzianowski; Artur Szymanski; Aldona Drabik; Stanislaw Szlufik; Dariusz M. Koziorowski; Andrzej W. Przybyszewski (ACIIDS (2), 2019)

Eye-Tracking and Machine Learning Significance in Parkinson's Disease Symptoms Prediction. - Artur Chudzik; Artur Szymanski; Jerzy Pawel Nowacki; Andrzej W. Przybyszewski (ACIIDS (2), 2020)

Combining Results of Different Oculometric Tests Improved Prediction of Parkinson's Disease Development. - Albert Sledzianowski; Artur Szymanski; Aldona Drabik; Stanislaw Szlufik; Dariusz M. Koziorowski; Andrzej W. Przybyszewski (ACIIDS (2), 2020)