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Andrzej W. Przybyszewski
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2020 – today
- 2024
- [j18]Artur Chudzik
, Albert Sledzianowski, Andrzej W. Przybyszewski
Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases. Sensors 24(5): 1572 (2024) - [c50]Artur Chudzik, Jerzy Pawel Nowacki, Andrzej W. Przybyszewski:
Recognizing Patterns of Parkinson's Disease Using Online Trail Making Test and Response Dynamics - Preliminary Study. ICPR (2) 2024: 193-207 - 2023
- [j17]Andrzej W. Przybyszewski
Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms. Sensors 23(4): 2145 (2023) - [c49]Andrzej W. Przybyszewski
Granular Computing to Forecast Alzheimer's Disease Distinctive Individual Development. ACIIDS (1) 2023: 70-81 - [c48]Artur Chudzik
Investigating the Impact of Parkinson's Disease on Brain Computations: An Online Study of Healthy Controls and PD Patients. ACIIDS (2) 2023: 235-246 - [c47]Albert Sledzianowski, Jerzy Pawel Nowacki, Konrad Sitarz, Andrzej W. Przybyszewski:
Novel Machine Learning Pipeline for Real-Time Oculometry. ACIIDS (Companion) 2023: 498-509 - [c46]Albert Sledzianowski, Jerzy Pawel Nowacki, Konrad Sitarz, Andrzej W. Przybyszewski:
Universal Machine-Learning Processing Pattern for Computing in the Video-Oculography. ICCS (3) 2023: 200-212 - [c45]Andrzej W. Przybyszewski
Multi-granular Computing Can Predict Prodromal Alzheimer's Disease Indications in Normal Subjects. ICCS (3) 2023: 278-285 - 2022
- [c44]Albert Sledzianowski, Jerzy Pawel Nowacki, Andrzej W. Przybyszewski, Krzysztof Urbanowicz:
Detecting True and Declarative Facial Emotions by Changes in Nonlinear Dynamics of Eye Movements. ACIIDS (1) 2022: 106-116 - [c43]Andrzej W. Przybyszewski
Rough Set Rules (RSR) Predominantly Based on Cognitive Tests Can Predict Alzheimer's Related Dementia. ACIIDS (1) 2022: 129-141 - [c42]Andrzej W. Przybyszewski
AI Classifications Applied to Neuropsychological Trials in Normal Individuals that Predict Progression to Cognitive Decline. ICCS (3) 2022: 150-156 - 2021
- [c41]Andrzej W. Przybyszewski:
Theory of Mind Helps to Predict Neurodegenerative Processes in Parkinson's Disease. ICCS (3) 2021: 542-555 - [c40]Andrzej W. Przybyszewski
Concept of Parkinson Leading to Understanding Mechanisms of the Disease. ICCCI 2021: 456-466 - [c39]Albert Sledzianowski, Krzysztof Urbanowicz, Wojciech Glac, Renata Slota, Maria Wojtowicz, Monika Nowak, Andrzej W. Przybyszewski:
Face emotional responses correlate with chaotic dynamics of eye movements. KES 2021: 2881-2892 - 2020
- [j16]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) - [j15]Andrzej W. Przybyszewski
Parkinson's disease development prediction by c-granule computing compared to different AI methods. J. Inf. Telecommun. 4(4): 425-439 (2020) - [c38]Albert Sledzianowski
Combining Results of Different Oculometric Tests Improved Prediction of Parkinson's Disease Development. ACIIDS (2) 2020: 517-526 - [c37]Artur Chudzik
Eye-Tracking and Machine Learning Significance in Parkinson's Disease Symptoms Prediction. ACIIDS (2) 2020: 537-547 - [c36]Andrzej W. Przybyszewski
IGrC: Cognitive and Motor Changes During Symptoms Development in Parkinson's Disease Patients. ACIIDS (2) 2020: 548-559
2010 – 2019
- 2019
- [j14]Andrzej W. Przybyszewski
SI: SCA Measures - Fuzzy rough set features of cognitive computations in the visual system. J. Intell. Fuzzy Syst. 36(4): 3155-3167 (2019) - [c35]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 - [c34]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 - [c33]Artur Chudzik
DTI Helps to Predict Parkinson's Patient's Symptoms Using Data Mining Techniques. ACIIDS (2) 2019: 615-623 - [c32]Andrzej W. Przybyszewski
Parkinson's Disease Development Prediction by C-Granule Computing. ICCCI (1) 2019: 296-306 - [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
- [c31]Andrzej W. Przybyszewski
Rules Determine Therapy-Dependent Relationship in Symptoms Development of Parkinson's Disease Patients. ACIIDS (2) 2018: 436-445 - [c30]Andrzej W. Przybyszewski
Fuzzy RST and RST Rules Can Predict Effects of Different Therapies in Parkinson's Disease Patients. ISMIS 2018: 409-416 - 2017
- [j13]Andrzej W. Przybyszewski
Theory of Mind and Empathy. Part I - Model of Social Emotional Thinking. Fundam. Informaticae 150(2): 221-230 (2017) - [j12]Jacek Naruniec
Webcam-based system for video-oculography. IET Comput. Vis. 11(2): 173-180 (2017) - [c29]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 - [c28]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 - [c27]Andrzej W. Przybyszewski
Rough Set Rules Determine Disease Progressions in Different Groups of Parkinson's Patients. PReMI 2017: 270-275 - 2016
- [j11]Andrzej W. Przybyszewski
Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson's Patients. Sensors 16(9): 1498 (2016) - [c26]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
- [c25]Marek Kulbacki
Expert Group Collaboration Tool for Collective Diagnosis of Parkinson Disease. ACIIDS (2) 2015: 248-257 - [c24]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 - [c23]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 - [c22]Konrad A. Ciecierski
Frequency Based Mapping of the STN Borders. ISMIS 2015: 386-395 - [c21]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
- [j10]Konrad Ciecierski
Foundations of automatic system for intrasurgical localization of subthalamic nucleus in Parkinson patients. Web Intell. Agent Syst. 12(1): 63-82 (2014) - [c20]Andrzej W. Przybyszewski
Rough Set Based Classifications of Parkinson's Patients Gaits. ACIIDS (2) 2014: 525-534 - [c19]Konrad Ciecierski
Intraoperative Decision Making with Rough Set Rules for STN DBS in Parkinson Disease. Brain Informatics and Health 2014: 323-334 - [c18]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 - [c17]Konrad Ciecierski
Spike Sorting Based upon PCA over DWT Frequency Band Selection. ISMIS 2014: 154-163 - [c16]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 - 2013
- [j9]Krzysztof Kurowski, Ariel Oleksiak
DCworms - A tool for simulation of energy efficiency in distributed computing infrastructures. Simul. Model. Pract. Theory 39: 135-151 (2013) - [c15]Konrad Ciecierski
Discrimination of the Micro Electrode Recordings for STN Localization during DBS Surgery in Parkinson's Patients. FQAS 2013: 328-339 - 2012
- [c14]Magdalena Stawarz, Andrzej Polanski
A System for Analysis of Tremor in Patients with Parkinson's Disease Based on Motion Capture Technique. ICCVG 2012: 618-625 - [c13]Konrad Ciecierski
Foundations of Recommender System for STN Localization during DBS Surgery in Parkinson's Patients. ISMIS 2012: 234-243 - 2011
- [c12]Konrad Ciecierski
Selection of the Optimal Microelectrode during DBS Surgery in Parkinson's Patients. ISMIS 2011: 554-564 - 2010
- [j8]Andrzej W. Przybyszewski
Logical rules of visual brain: From anatomy through neurophysiology to cognition. Cogn. Syst. Res. 11(1): 53-66 (2010) - [j7]Tomasz M. Rutkowski
Emd Approach to Multichannel EEG Data - the amplitude and Phase Components Clustering Analysis. J. Circuits Syst. Comput. 19(1): 215-229 (2010)
2000 – 2009
- 2009
- [c11]Andrzej W. Przybyszewski
Neurological Foundation of Image Processing. ICAPR 2009: 141-144 - 2008
- [j6]Andrzej W. Przybyszewski
The Neurophysiological Bases of Cognitive Computation Using Rough Set Theory. Trans. Rough Sets 9: 287-317 (2008) - [c10]Andrzej W. Przybyszewski:
Brain-Like Approximate Reasoning. BCS Int. Acad. Conf. 2008: 15-28 - [c9]Andrzej W. Przybyszewski
Decision Making Logic of Visual Brain. ICANN (2) 2008: 423-432 - [c8]Tomasz M. Rutkowski
EMD Approach to Multichannel EEG Data - The Amplitude and Phase Synchrony Analysis Technique. ICIC (1) 2008: 122-129 - [c7]Andrzej W. Przybyszewski
Interactions between Rough Parts in Object Perception. ISMIS 2008: 236-245 - 2007
- [j5]Andrzej W. Przybyszewski
Basic Difference Between Brain and Computer: Integration of Asynchronous Processes Implemented as Hardware Model of the Retina. IEEE Trans. Neural Networks 18(1): 70-85 (2007) - [c6]Andrzej W. Przybyszewski:
Rough Set Theory of Shape Perception. ICIC (2) 2007: 738-749 - [c5]Andrzej W. Przybyszewski
Rough Set Theory of Pattern Classification in the Brain. PReMI 2007: 295-303 - [c4]Andrzej W. Przybyszewski:
Checking Brain Expertise Using Rough Set Theory. RSEISP 2007: 746-755 - 2005
- [c3]Mark A. Kon, Leszek Plaskota, Andrzej W. Przybyszewski:
Statistical Likelihood Representations of Prior Knowledge in Machine Learning. Artificial Intelligence and Applications 2005: 467-472 - [c2]Mark A. Kon, Leszek Plaskota, Andrzej W. Przybyszewski
Machine Learning and Statistical MAP Methods. Intelligent Information Systems 2005: 441-445 - 2000
- [c1]Andrzej W. Przybyszewski, Daniel A. Pollen:
Brain Differently Changes its Algorithms in Parallel Processing of Visual Information. Intelligent Information Systems 2000: 267-276
1990 – 1999
- 1998
- [j4]Paolo Gaudiano, Andrzej W. Przybyszewski
Spatial asymmetries in cat retinal ganglion cell responses. Biol. Cybern. 79(2): 151-159 (1998) - 1997
- [j3]Andrzej W. Przybyszewski
Otto-Joachim Grüsser 1932-1995. Biol. Cybern. 76(5): 315 (1997) - [j2]Andrzej W. Przybyszewski
Overview of Otto-Joachim Grüsser's work. Biol. Cybern. 76(5): 317-320 (1997) - 1996
- [j1]Andrzej W. Przybyszewski
On the complex dynamics of intracellular ganglion cell light responses in the cat retina. Biol. Cybern. 74(4): 299-308 (1996)
Coauthor Index
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last updated on 2024-12-13 20:03 CET by the dblp team
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