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Dariusz M. Koziorowski
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2020 – today
- 2023
- [j5]Andrzej W. Przybyszewski
, Albert Sledzianowski, Artur Chudzik
Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms. Sensors 23(4): 2145 (2023) - 2021
- [c14]Andrzej W. Przybyszewski
Concept of Parkinson Leading to Understanding Mechanisms of the Disease. ICCCI 2021: 456-466 - 2020
- [j4]Andrzej W. Przybyszewski, Artur Chudzik
Comparison of Different Data Mining Methods to Determine Disease Progression in Dissimilar Groups of Parkinson's Patients. Fundam. Informaticae 176(2): 167-181 (2020) - [j3]Mateusz Szumilas
A Multimodal Approach to the Quantification of Kinetic Tremor in Parkinson's Disease. Sensors 20(1): 184 (2020) - [c13]Albert Sledzianowski
Combining Results of Different Oculometric Tests Improved Prediction of Parkinson's Disease Development. ACIIDS (2) 2020: 517-526 - [c12]Andrzej W. Przybyszewski
IGrC: Cognitive and Motor Changes During Symptoms Development in Parkinson's Disease Patients. ACIIDS (2) 2020: 548-559
2010 – 2019
- 2019
- [c11]Andrzej W. Przybyszewski, Jerzy Pawel Nowacki, Aldona Drabik, Stanislaw Szlufik, Piotr Habela, Dariusz M. Koziorowski:
Granular Computing (GC) Demonstrates Interactions Between Depression and Symptoms Development in Parkinson's Disease Patients. ACIIDS (2) 2019: 591-601 - [c10]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 - [p1]Andrzej W. Przybyszewski, Stanislaw Szlufik, Piotr Habela, Dariusz M. Koziorowski:
Multimodal Learning Determines Rules of Disease Development in Longitudinal Course with Parkinson's Patients. Intelligent Methods and Big Data in Industrial Applications 2019: 235-246 - 2018
- [c9]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 - [c8]Andrzej W. Przybyszewski
Rules Determine Therapy-Dependent Relationship in Symptoms Development of Parkinson's Disease Patients. ACIIDS (2) 2018: 436-445 - 2017
- [j2]Jacek Naruniec
Webcam-based system for video-oculography. IET Comput. Vis. 11(2): 173-180 (2017) - [c7]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 - [c6]Andrzej W. Przybyszewski
Rules Found by Multimodal Learning in One Group of Patients Help to Determine Optimal Treatment to Other Group of Parkinson's Patients. ACIIDS (2) 2017: 359-367 - [c5]Andrzej W. Przybyszewski
Rough Set Rules Determine Disease Progressions in Different Groups of Parkinson's Patients. PReMI 2017: 270-275 - 2016
- [j1]Andrzej W. Przybyszewski
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
- [c3]Andrzej W. Przybyszewski
Machine Learning on the Video Basis of Slow Pursuit Eye Movements Can Predict Symptom Development in Parkinson's Patients. ACIIDS (2) 2015: 268-276 - [c2]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 - 2014
- [c1]Andrzej W. Przybyszewski, Mark A. Kon, Stanislaw Szlufik, Justyna Dutkiewicz, Piotr Habela, Dariusz M. Koziorowski:
Data Mining and Machine Learning on the Basis from Reflexive Eye Movements Can Predict Symptom Development in Individual Parkinson's Patients. MICAI (2) 2014: 499-509
Coauthor Index
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