Peter Drotar
Peter Drotar
Department of Computers and Informatics, Technical University of Kosice
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Cited by
Cited by
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
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
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
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
Bankruptcy prediction for small-and medium-sized companies using severely imbalanced datasets
M Zoričák, P Gnip, P Drotár, V Gazda
Economic Modelling, 2019
Ensemble feature selection using election methods and ranker clustering
P Drotár, M Gazda, L Vokorokos
Information Sciences 480, 365-380, 2019
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
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
Dysgraphia detection through machine learning
P Drotár, M Dobeš
Scientific reports 10 (1), 1-11, 2020
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
Self-supervised deep convolutional neural network for chest X-ray classification
M Gazda, J Gazda, J Plavka, P Drotar
IEEE Access, 2021
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
Selective oversampling approach for strongly imbalanced data
P Gnip, L Vokorokos, P Drotár
PeerJ Computer Science 7, e604, 2021
On some aspects of minimum redundancy maximum relevance feature selection
P Bugata, P Drotar
Science China Information Sciences 63 (1), 1-15, 2020
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
Machine Learning Approach to Dysphonia Detection
Z Dankovičová, D Sovák, P Drotár, L Vokorokos
Applied Sciences 8 (10), 1927, 2018
Weighted nearest neighbors feature selection
P Bugata, P Drotár
Knowledge-Based Systems 163, 749-761, 2019
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
Receiver based compensation of nonlinear distortion in MIMO-OFDM
P Drotar, J Gazda, M Deumal, P Galajda, D Kocur
RF Front-ends for Software Defined and Cognitive Radio Solutions (IMWS …, 2010
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
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