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Bartosz Swiderski
Bartosz Swiderski
Zweryfikowany adres z sggw.pl
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Lyapunov exponent of EEG signal for epileptic seizure characterization
B Swiderski, S Osowski, A Rysz
Proceedings of the 2005 European Conference on Circuit Theory and Design …, 2005
572005
Multistage classification by using logistic regression and neural networks for assessment of financial condition of company
B Swiderski, J Kurek, S Osowski
Decision Support Systems 52 (2), 539-547, 2012
502012
Melanoma recognition using extended set of descriptors and classifiers
M Kruk, B Świderski, S Osowski, J Kurek, M Słowińska, I Walecka
EURASIP journal on Image and Video Processing 2015, 1-10, 2015
492015
Deep learning and non-negative matrix factorization in recognition of mammograms
B Swiderski, J Kurek, S Osowski, M Kruk, W Barhoumi
Eighth International Conference on Graphic and Image Processing (ICGIP 2016 …, 2017
442017
Epileptic seizure characterization by Lyapunov exponent of EEG signal
S Osowski, B Swiderski, A Cichocki, A Rysz
COMPEL-The international journal for computation and mathematics in …, 2007
412007
Novel methods of image description and ensemble of classifiers in application to mammogram analysis
B Swiderski, S Osowski, J Kurek, M Kruk, I Lugowska, P Rutkowski, ...
Expert Systems with Applications 81, 67-78, 2017
352017
False-positive reduction in computer-aided mass detection using mammographic texture analysis and classification
S Dhahbi, W Barhoumi, J Kurek, B Swiderski, M Kruk, E Zagrouba
Computer methods and programs in biomedicine 160, 75-83, 2018
302018
Deep learning versus classical neural approach to mammogram recognition
J Kurek, B Świderski, S Osowski, M Kruk, W Barhoumi
Bulletin of the Polish Academy of Sciences. Technical Sciences 66 (6), 831-840, 2018
272018
Single-class SVM and directed transfer function approach to the localization of the region containing epileptic focus
B Swiderski, S Osowski, A Cichocki, A Rysz
Neurocomputing 72 (7-9), 1575-1583, 2009
272009
Deep neural system for supporting tumor recognition of mammograms using modified GAN
B Swiderski, L Gielata, P Olszewski, S Osowski, M Kołodziej
Expert Systems with Applications 164, 113968, 2021
262021
Ensemble of classifiers and wavelet transformation for improved recognition of Fuhrman grading in clear-cell renal carcinoma
M Kruk, J Kurek, S Osowski, R Koktysz, B Swiderski, T Markiewicz
Biocybernetics and Biomedical Engineering 37 (3), 357-364, 2017
262017
Deep learning in assessment of drill condition on the basis of images of drilled holes
J Kurek, B Swiderski, A Jegorowa, M Kruk, S Osowski
Eighth International Conference on Graphic and Image Processing (ICGIP 2016 …, 2017
252017
Epileptic seizure prediction using Lyapunov exponents and support vector machine
B Świderski, S Osowski, A Cichocki, A Rysz
Adaptive and Natural Computing Algorithms: 8th International Conference …, 2007
182007
Texture characterization based on the Kolmogorov–Smirnov distance
B Swiderski, S Osowski, M Kruk, J Kurek
Expert systems with applications 42 (1), 503-509, 2015
172015
Random CNN structure: tool to increase generalization ability in deep learning
B Swiderski, S Osowski, G Gwardys, J Kurek, M Slowinska, I Lugowska
Eurasip journal on image and video processing 2022 (1), 3, 2022
132022
Data augmentation techniques for transfer learning improvement in drill wear classification using convolutional neural network
J Kurek, I Antoniuk, J Górski, A Jegorowa, B Świderski, M Kruk, ...
Machine Graphics and Vision 28, 2019
132019
Aggregation of classifiers ensemble using local discriminatory power and quantiles
B Swiderski, S Osowski, M Kruk, W Barhoumi
Expert Systems with Applications 46, 316-323, 2016
132016
Application of siamese networks to the recognition of the drill wear state based on images of drilled holes
J Kurek, I Antoniuk, B Świderski, A Jegorowa, M Bukowski
Sensors 20 (23), 6978, 2020
122020
Classifiers ensemble of transfer learning for improved drill wear classification using convolutional neural network
J Kurek, I Antoniuk, J Górski, A Jegorowa, B Świderski, M Kruk, ...
Machine Graphics & Vision 28 (1/4), 2019
122019
Prediction of Blueberry (Vaccinium corymbosum L.) Yield Based on Artificial Intelligence Methods
G Niedbała, J Kurek, B Świderski, T Wojciechowski, I Antoniuk, K Bobran
Agriculture 12 (12), 2089, 2022
112022
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