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Scientific Reports|December 10, 2020
Dysgraphia detection through machine learningPeter Drotár, Marek Dobeš
Peerj. Computer Science|July 9, 2021
Selective oversampling approach for strongly imbalanced dataPeter Gnip, Liberios Vokorokos, Peter Drotár
Data in Brief|August 30, 2019
Small- and medium-enterprises bankruptcy datasetPeter Drotár, Peter Gnip, Martin Zoričak, et al.
Peerj. Computer Science|June 22, 2023
Bankruptcy prediction using ensemble of autoencoders optimized by genetic algorithmRóbert Kanász, Peter Gnip, Martin Zoričák, et al.
Artificial Intelligence in Medicine|February 15, 2016
Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's diseasePeter Drotár, Jiří Mekyska, Irena Rektorová, et al.
Computer Methods and Programs in Biomedicine|September 28, 2014
Analysis of in-air movement in handwriting: A novel marker for Parkinson's diseasePeter Drotár, Jiří Mekyska, Irena Rektorová, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society|September 30, 2014
Decision support framework for Parkinson's disease based on novel handwriting markersPeter Drotár, Jiří Mekyska, Irena Rektorová, et al.
International Journal of Medical Informatics|October 6, 2023
On the inter-dataset generalization of machine learning approaches to Parkinson's disease detection from voiceMáté Hireš, Peter Drotár, Nemuel Daniel Pah, et al.
Computers in Biology and Medicine|November 20, 2021
Convolutional neural network ensemble for Parkinson's disease detection from voice recordingsMáté Hireš, Matej Gazda, Peter Drotár, et al.
Computer Methods and Programs in Biomedicine|October 2, 2022
Computerized analysis of speech and voice for Parkinson's disease: A systematic reviewQuoc Cuong Ngo, Mohammod Abdul Motin, Nemuel Daniel Pah, et al.
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