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Peter Drotar
Peter Drotar
Department of Computers and Informatics, Technical University of Kosice
Verified email at tuke.sk
Title
Cited by
Cited by
Year
Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease
P Drotár, J Mekyska, I Rektorová, L Masarová, Z Smékal, ...
Artificial intelligence in medicine 67, 39-46, 2016
4492016
Decision support framework for Parkinson's disease based on novel handwriting markers
P Drotar, J Mekyska, I Rektorova, L Masarova, Z Smekal, M Zanuy
IEEE Transactions on Neural and Rehabilitation Engineering, 2014
2262014
Analysis of in-air movement in handwriting: A novel marker for Parkinson's disease
P Drotár, J Mekyska, I Rektorová, L Masarová, Z Smékal, ...
Computer methods and programs in biomedicine 117 (3), 405-411, 2014
2072014
Convolutional neural network ensemble for Parkinson's disease detection from voice recordings
M Hireš, M Gazda, P Drotár, ND Pah, MA Motin, DK Kumar
Computers in Biology and Medicine, 105021, 2021
1472021
Bankruptcy prediction for small-and medium-sized companies using severely imbalanced datasets
M Zoričák, P Gnip, P Drotár, V Gazda
Economic Modelling, 2019
1392019
Dysgraphia detection through machine learning
P Drotár, M Dobeš
Scientific reports 10 (1), 1-11, 2020
1272020
Ensemble feature selection using election methods and ranker clustering
P Drotár, M Gazda, L Vokorokos
Information Sciences 480, 365-380, 2019
1202019
An experimental comparison of feature selection methods on two-class biomedical datasets
P Drotár, J Gazda, Z Smékal
Computers in biology and medicine 66, 1-10, 2015
1182015
Computerized Analysis of Speech and Voice for Parkinson's Disease: A Systematic Review
QC Ngo, MA Motin, ND Pah, P Drotár, P Kempster, D Kumar
Computer Methods and Programs in Biomedicine, 107133, 2022
1132022
Self-supervised deep convolutional neural network for chest X-ray classification
M Gazda, J Gazda, J Plavka, P Drotar
IEEE Access, 2021
1002021
Multiple-Fine-Tuned Convolutional Neural Networks for Parkinson's Disease Diagnosis From Offline Handwriting
M Gazda, M Hireš, P Drotár
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021
992021
Selective oversampling approach for strongly imbalanced data
P Gnip, L Vokorokos, P Drotár
PeerJ Computer Science 7, e604, 2021
962021
A new modality for quantitative evaluation of Parkinson's disease: In-air movement
P Drotár, J Mekyska, I Rektorova, L Masarova, Z Smékal, ...
Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International …, 2013
902013
On some aspects of minimum redundancy maximum relevance feature selection
P Bugata, P Drotar
Science China Information Sciences 63 (1), 1-15, 2020
802020
Prediction potential of different handwriting tasks for diagnosis of Parkinson's
P Drotar, J Mekyska, Z Smekal, I Rektorova, L Masarova, ...
E-Health and Bioengineering Conference (EHB), 2013, 1-4, 2013
772013
Machine Learning Approach to Dysphonia Detection
Z Dankovičová, D Sovák, P Drotár, L Vokorokos
Applied Sciences 8 (10), 1927, 2018
622018
Contribution of different handwriting modalities to differential diagnosis of Parkinson's Disease
P Drotar, J Mekyska, Z Smékal, I Rektorova, L Masarova, ...
Medical Measurements and Applications (MeMeA), 2015 IEEE International …, 2015
602015
Weighted nearest neighbors feature selection
P Bugata, P Drotár
Knowledge-Based Systems 163, 749-761, 2019
432019
Deep convolutional neural network for detection of pathological speech
L Vavrek, M Hires, D Kumar, P Drotár
2021 IEEE 19th World Symposium on Applied Machine Intelligence and …, 2021
332021
On the inter-dataset generalisation of machine learning approaches to Parkinson's disease detection from voice
M Hireš, P Drotár, ND Pah, QC Ngo, DK Kumar
International Journal of Medical Informatics, 105237, 2023
282023
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Articles 1–20