| Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor J Kurek, S Osowski Neural Computing and Applications 19, 557-564, 2010 | 77 | 2010 |
| 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 | 50 | 2012 |
| 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 | 49 | 2015 |
| 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 | 44 | 2017 |
| 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 | 35 | 2017 |
| Developing automatic recognition system of drill wear in standard laminated chipboard drilling process S Osowski, J Kurek, M Kruk, J Górski, P Hoser, G Wieczorek, A Jegorowa, ... Bulletin of the Polish Academy of Sciences Technical Sciences, 633-640-633-640, 2016 | 32 | 2016 |
| 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 | 30 | 2018 |
| Use of nearest neighbors (k-NN) algorithm in tool condition identification in the case of drilling in melamine faced particleboard A Jegorowa, J Górski, J Kurek, M Kruk Maderas. Ciencia y tecnología 22 (2), 189-196, 2020 | 28 | 2020 |
| 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 | 28 | 2018 |
| Transfer learning in recognition of drill wear using convolutional neural network J Kurek, G Wieczorek, BSM Kruk, A Jegorowa, S Osowski 2017 18th International Conference on Computational Problems of Electrical …, 2017 | 27 | 2017 |
| Initial study on the use of support vector machine (SVM) in tool condition monitoring in chipboard drilling A Jegorowa, J Górski, J Kurek, M Kruk European journal of wood and wood products 77, 957-959, 2019 | 26 | 2019 |
| 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 | 26 | 2017 |
| 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 | 25 | 2017 |
| 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 | 17 | 2015 |
| Deep learning methods for drill wear classification based on images of holes drilled in melamine faced chipboard A Jegorowa, J Kurek, I Antoniuk, W Dołowa, M Bukowski, P Czarniak Wood Science and Technology 55, 271-293, 2021 | 15 | 2021 |
| 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 | 14 | 2022 |
| 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 | 13 | 2020 |
| 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 | 13 | 2019 |
| 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 | 12 | 2022 |
| 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 | 12 | 2019 |
Michał KruksggwVerified email at iem.pw.edu.pl
