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Godło Polski
Politechnika Śląska
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Department of Cybernetics, Nanotechnology and Data Processing

Department of Cybernetics, Nanotechnology and Data Processing

Cybernetics - the scientific study of control and communication in the animal and the machine

Norbert Wiener

Monographs, monograph and book editing, chapters in monographs:

  1. E. Czogała, J. Łęski, ,,Fuzzy implications in approximate reasoning”, in: L.Zadeh, A.Kacprzyk (Eds.), ,,Computing with words in information/intelligent systems. Vol.I: Foundations”, Physica-Verlag, Springer-Verlag Com., Heidelberg, New York, 1999, pp.342–357.
  2. J. Łęski, E. Czogała, ,,A new fuzzy inference system based on artificial neural network and its application”, in: L.Zadeh, A.Kacprzyk (Eds.), ,,Computing with words in information/intelligent systems. Vol.II: Applications”, Physica-Verlag, Springer-Verlag Com., Heidelberg, New York, 1999, pp.75–94.
  3. E. Czogała, J. Łęski, ,,Fuzzy and neuro-fuzzy intelligent systems”, Physica-Verlag, Springer-Verlag Com., Heidelberg, New York, 2000.
  4. E. Czogała, J. Łęski, ,,Entropy and energy measures of fuzziness in ECG signal processing”, in: P.S.Szczepaniak, P.J.G.Lisboa, J.Kacprzyk (Eds.), ,,Fuzzy systems in medicine”, Physica-Verlag, Springer-Verlag Com., Heidelberg, New York, 2000, pp.227–245.
  5. J. Łęski, E. Czogała, ,,A neuro-fuzzy inference system optimized by deterministic annealing”, in: R.Hampel, M.Wagenknecht, N.Chaker (Eds.), ,,Fuzzy control — theory and practice”, Physica-Verlag, Springer-Verlag Com., Heidelberg, New York, 2000, pp.287–293.
  6. E. Czogała, N. Henzel, J. Łęski, ,,The equality of inference results using fuzzy implication and conjunctive interpretations of the if-then rules under defuzzification”, in: R.Hampel, M.Wagenknecht, N.Chaker (Eds.), ,,Fuzzy control — theory and practice”, Physica-Verlag, Springer-Verlag Com., Heidelberg, New York, 2000, pp.98–108.
  7. E. Straszecka, Defining membership functions of fuzzy sets in medical decision support. W: P.S. Szczepaniak, P.J.G. Lisboa, J.Kacprzyk (red.), Fuzzy Systems in Medicine, Physica-Verlag, Springer Verlag Company, Heidelberg, New York, 2000, 32 47.
  8. J. Łęski, N. Henzel, ,,A neuro-fuzzy system based on logical interpretation of if-then rules”, in: M. Russo, L.C. Jain (Eds.), ,,Fuzzy learning and applications,” CRC Press, New York, 2001, pp.359–388.
  9. J. Łęski, ,,Ordered weighted generalized conditional possibilistic clustering” W: J.Chojcan, J.Łęski (Eds.), ,,Zbiory rozmyte i ich zastosowania”, Wydawnictwa Politechniki Śląskiej, Gliwice, 2001, ss.469–479.
  10. E. Straszecka, Measures of uncertainty and imprecision in medical diagnosis support, Wydawnictwo Politechniki Śląskiej, Gliwice 2010.
  11. R. Czabański, M. Jeżewski, J. Łęski, "Introduction to fuzzy systems, in: Theory and applications of ordered fuzzy numbers", A tribute to Professor Witold Kosiński. Eds. Piotr Prokopowicz, Jacek Czerniak, Dariusz Mikołajewski, Łukasz Apiecionek, Dominik Ślęzak. Cham: Springer, 2017, Studies in Fuzziness and Soft Computing, 356, pp. 23-43.
  12. M. Jeżewski, R. Czabański, J. Łęski, "Introduction to fuzzy sets", in: Theory and applications of ordered fuzzy numbers. A tribute to Professor Witold Kosiński. Eds. Piotr Prokopowicz, Jacek Czerniak, Dariusz Mikołajewski, Łukasz Apiecionek, Dominik Ślęzak. Cham: Springer, 2017, Studies in Fuzziness and Soft Computing, 356, pp. 3-2.

Original papers in journals from Journal Citation Reports:

  1. E. Czogała, J. Łęski, ,,Application of entropy and energy measure of fuzziness to processing of ECG signal”, Fuzzy Sets and Systems, Vol.97, No.1, 1998, pp.9–18.
  2. J. Łęski, E. Czogała, ,,A new artificial neural network based fuzzy inference system with moving consequents in if-then rules and its applications”, Fuzzy Sets and Systems, Vol.108, No.3, 1999 , pp.289–297.
  3. E. Czogała, J. Łęski, ,,On equivalence of approximate reasoning results using different interpretations of fuzzy if-then rules”, Fuzzy Sets and Systems, Vol.117, No.2, 2001, pp.279–296.
  4. M. Bałaziński, E. Czogała, K. Jemialniak, J. Łęski, ,,Tool condition monitoring using artificial intelligence methods”, Engineering Applications of Artificial Intelligence, Vol.15, 2002, pp.73–80.
  5. R. Czabański, Context evaluation for fuzzy conditional clustering, Bulletin of Polish Academy of Science, Technical Sciences, 2002, vol. 50, No. 1, pp. 71-78.
  6. J. Łęski, ,,Towards a robust fuzzy clustering”, Fuzzy Sets and Systems, Vol.137, No.2, 2003, pp.215–233.
  7. J. Łęski, ,,Ho-Kashyap classifier with generalization control”, Pattern Recognition Letters, Vol.24, 2003, pp.2281–2290.
  8. J. Łęski, ,,Neuro-fuzzy system with learning tolerant to imprecision” Fuzzy Sets and Systems, Vol. 138, No.2, 2003, pp. 427–439.
  9. J. Łęski, ,,Generalized weighted conditional fuzzy clustering”, IEEE Trans. Fuzzy Systems, Vol.11, No.6, 2003, pp.709–715.
  10. J. Łęski, ,,Fuzzy c-varieties/elliptotypes clustering in reproducing kernel Hilbert space” Fuzzy Sets and Systems, Vol. 141, No.2, 2004, pp. 259–280.
  11. J. Łęski, ,,ε-insensitive fuzzy c-regression models: Introduction to ε-insensitive fuzzy modeling”, IEEE Trans. Systems, Man and Cybernetics – Part B: Cybernetics, Vol.34, No.1, 2004, pp.4–15.
  12. J. Łęski, ,,An ε-margin Nonlinear Classifier based on if-then rules”, IEEE Trans. Systems, Man and Cybernetics – Part B: Cybernetics, Vol.34, No.1, 2004, pp.68–76.
  13. J. Łęski, A. Gacek, ,,Computationally Effective Algorithm to Robust Weighted Averaging”, IEEE Trans. Biomed. Eng., Vol.51, No.7, 2004, pp.1280–1284.
  14. J. Łęski, ,,TSK-fuzzy modeling based on ε-insensitive Learning”, IEEE Trans. Fuzzy Systems, Vol.13, No.2, 2005, pp.181–193.
  15. J. Łęski, A. Owczarek, ,,A time-domain-constrained fuzzy clustering method and its application to signal analysis”, Fuzzy Sets and Systems, Vol. 155, No.2, 2005, pp. 165–190.
  16. J. Łęski, ,,On support vector regression machines with linguistic interpretation of the kernel matrix” Fuzzy Sets and Systems, Vol. 157, No.3, 2006, pp. 1092–1113.
  17. J. Łęski, ,,Iteratively reweighted least squares classifier and its l2- and l1-regularized kernel versions”, ”, Bull. Pol. Ac.: Tech. Vol. 58, No. 1, 2010, pp.171-182.
  18. R. Czabański, M. Jeżewski, J. Wróbel, J. Jeżewski: Predicting the risk of low-fetal birth weight from cardiotocographic signals using ANBLIR system with deterministic annealing and e-insensitive learning, IEEE Transactions on Information Technology in Biomedicine, 2010, Vol. 14, No. 4, 1062-1074.
  19. M. Jeżewski, R. Czabański, J. Wróbel, K. Horoba: „Analysis of extracted cardiotocographic signal features to improve automated prediction of fetal outcome”, Biocybernetics and Biomedical Engineering, Vol. 30(4), 2010, 29-47.
  20. R. Czabański, J. Jeżewski, A. Matonia, M. Jeżewski: ”Computerized Analysis of Fetal Heart Rate Signals as the Predictor of Neonatal Acidemia”, Expert Systems with Applications, 2012, Volume 39, Issue 15, 1 November 2012, pp. 11846–11860.
  21. J. Łęski, N. Henzel, ,,Generalized ordered linear regression with regularization.” Bull. Pol. Ac.: Tech. Vol. 60, No. 3, 2012, pp.481-489.
  22. R. Czabański, J. Jeżewski, J. Wróbel, J. Sikora, M. Jeżewski: Application of fuzzy inference system for classification of fetal heart rate tracings in relation to neonatal outcome, Ginekologia Polska, 2013, Vol. 84(1), 38-43.
  23. R. Czabański, J. Jeżewski, K. Horoba, M. Jeżewski, Fetal state assessment using fuzzy analysis of fetal heart rate signals—Agreement with the neonatal outcome, Biocybernetics and Biomedical Engineering, Vol. 33(3), 2013, 145-155.
  24. R. Czabanski, J.Wrobel, J. Jezewski, J. Leski, M. Jezewski: “Efficient evaluation of fetal wellbeing during pregnancy using methods based on statistical learning principles”, Journal of Medical Imaging and Health Informatics, 2015, Vol. 5(6).
  25. M. Jeżewski, R. Czabański, J. Łęski: "An Attempt to Optimize the Cardiotocographic Signal Feature Set for Fetal State Assessment”, Journal of Medical Imaging and Health Informatics, 2015, Vol. 5(6), IF = 0,877, 1364-1373.
  26. M. Jeżewski, R. Czabański, J. Łęski, K. Horoba, "Clustering with pairs of prototypes to support automated assessment of the fetal state", International Journal of Applied Artificial Intelligence, 2016, 30(6), pp. 572-589.
  27. R. Czabański, M. Jeżewski, K. Horoba, J. Jeżewski, J. Łęski, "Fuzzy analysis of delivery outcome attributes for improving the automated fetal state assessment", International Journal of Applied Artificial Intelligence, 2016, 30(6), 556-571.
  28. 6. Jeżewski M., Czabański R., Łęski J., Jeżewski J., "Fuzzy classifier based on clustering with pairs of ε-hyperballs and its application to support fetal state assessment", Expert Systems with Applications, 2019, 118, pp. 109–126.
  29. Łęski J., Czabański R., Jeżewski M., Jeżewski J., Fuzzy ordered c-means clustering and least angle regression for fuzzy rule-based classifier: study for imbalanced data. IEEE Trans. Fuzzy Syst., 2019, s. 1-15.

Original papers in other journals:

  1. J. Łęski, E. Czogała, ,,A new artificial neural network based fuzzy inference system with moving consequents in if-then rules”, BUSEFAL, Vol.71, 1997, pp.72–81.
  2. E. Czogała, J. Fodor, J.Łęski, ,,The Fodor fuzzy implication in approximate reasoning”, Systems Science, Vol.23, No.2, 1997, pp.17–28.
  3. E. Czogała, J. Łęski, ,,A new fuzzy inference system with moving consequents in if-then rules. Applications to pattern recognition”, Bull. Pol. Ac.: Tech., Vol.45, No.4, 1997, pp.643–655.
  4. E. Czogała, J. Łęski, ,,An equivalence of approximate reasoning under defuzzification”, BUSEFAL, Vol.74, 1998, pp.83–92.
  5. J. Łęski, N. Henzel, ,,A neuro-fuzzy system using logical interpretation of if-then rules and its application to diabetes mellitus forecasting”, Archives of Control Sciences, Vol.9, No.1–2, 1999, pp.107–122.
  6. E. Czogała, J. Łęski, Y. Hayashi, ,,A classifier based on neuro-fuzzy inference system”, Journal of Advanced Computational Intelligence, Vol.3, No.4, 2000, pp.282–288.
  7. J. Łęski: ,,A new generalized weighted conditional fuzzy clustering”, BUSEFAL, Vol.81, 2000, pp.8–16.
  8. J. Łęski, ,,Robust possibilistic clustering”, Archives of Control Sciences, Vol.10, No.3–4, 2000, pp.141–155.
  9. J. Łęski, N. Henzel, ,,A neuro-fuzzy system based on logical interpretation of if-then rules”, International Journal Applied Mathematics and Computer Sciences, Vol.10, No.4, 2000, pp.703–722.
  10. J. Łęski, ,,An ε-insensitive approach to fuzzy clustering”, International Journal Applied Mathematics and Computer Sciences, Vol.11, No.4, 2001, pp.993–1007.
  11. J. Łęski, ,,Improving generalization ability of neuro-fuzzy system by ε-insensitive learning”, International Journal Applied Mathematics and Computer Sciences, Vol.12, No.3, 2002, pp.437–447.
  12. J. Łęski, ,,ε-insensitive fuzzy c-medians clustering”, Bull. Pol. Ac.: Tech., Vol.50, No.4, 2002, pp.361–374.
  13. J. Łęski, H. Henzel, ,,Minimum hypervolume clustering algorithm”, Machine Graphics and Vision, Vol.11, No.1, 2002, pp.123–132.
  14. J. Łęski, ,,Minimum absolute error classifier design with generalization control”, Archives of Control Sciences, Vol.12, No.3, 2002, pp.289–299.
  15. J. Łęski, ,,Computationally effective algorithm to the ε-insensitive fuzzy clustering, System Science, Vol.28, No.3, 2002, pp.31–50.
  16. J. Łęski, ,,ε-insensitive learning techniques for approximate reasoning systems (Invited Paper)”, International Journal of Computational Cognition, Vol.1, No.1, 2002, pp.21–77.
  17. J. Łęski: ,,An ε–insensitive fuzzy c-means clustering”, BUSEFAL, Vol.86, 2003, pp. 61–70.
  18. J. Łęski, ,,Fuzzy if-then rule-based nonlinear classifier”, International Journal of Applied Mathematics and Computer Sciences, Vol.13, No.2, 2003, pp.215–224.
  19. J. Łęski, ,,Kernel Ho-Kashyap classifier with generalization control” International Journal of Applied Mathematics and Computer Science. Vol.14, No.1, 2004, pp.53–62.
  20. J. Łęski, T. Czogała, ,,A fuzzy system with ε-insensitive learning of premises and consequences of if-then rules” International Journal of Applied Mathematics and Computer Sciences. Vol.15, No.2, 2005, pp.257–273.
  21. R. Czabański: Neuro-fuzzy modelling based on a deterministic annealing approach, Applied Mathematics and Computer Science, 2005, Vol. 15, No. 4, pp. 561-576.
  22. R. Czabański: Fuzzy if-then rules extraction by means of e-insensitive learning techniques integrated with deterministic annealing optimization method, International Journal of Computational Cognition, 2005, Vol. 3, No. 4, pp. 80-89.
  23. R. Czabański, "Extraction of Fuzzy Rules Using Deterministic Annealing Integrated with eps-insensitive Learning", Applied Mathematics and Computer Science, 2006, Vol.16, No. pp. 357-372.
  24. R. Czabański, T. Przybyła, "Median Fuzzy Conditional Clustering", International Journal of Information Technology and Intelligent Computing, 2006, Vol. 1, No. 1, pp. 79-89.
  25. R. Czabański, T. Pander, "Parameters Estimation for Digital Non-Linear Filters Using Neuro Fuzzy System", Journal of Medical Informatics and Technologies, 2006, Vol. 10, pp. 139-144.
  26. R. Czabański, M. Jeżewski, J. Wróbel. T. Kupka, J. Łęski, J. Jeżewski: „The prediction of the low fetal birth weight based on quantitative description of cardiotocographic signals”, Journal of Medical Informatics and Technologies, Vol. 12, 2008, pp.97-102.
  27. M. Jeżewski, R. Czabański, K. Horoba, J. Wróbel, J. Łęski, J. Jeżewski: „Influence of gestational age on neural networks interpretation of fetal monitoring signals”, Journal of Medical Informatics and Technologies, Vol. 12, 2008, pp.137-142.
  28. R. Czabański, M. Jeżewski, J. Wróbel. T. Kupka, J. Łęski, J. Jeżewski: The prediction of the low fetal birth weight based on quantitative description of cardiotocographic signals, Journal of Medical Informatics and Technologies, 2008, Vol. 12, 97-102.
  29. R. Czabański, M. Jeżewski, J. Wróbel, J. Jeżewski, K. Horoba: Fuzzy system for evaluation of fetal hart rate signals using FIGO criteria, Journal of Medical Informatics and Technologies, 2009, Vol. 13, 189-194.
  30. R. Czabański, M. Jeżewski, D. Roj, Z. Szaszkowski, T. Kupka, J. Wróbel: Evaluation of predictive capabilities of quantitative cardiotocographic signal features, Journal of Medical Informatics and Technologies, 2010, Vol. 16, 11-17.
  31. R. Czabański, D. Roj, J. Jeżewski, K. Horoba, M. Jeżewski, Fuzzy prediction of fetal acidemia, Journal Of Medical Informatics & Technologies vol. 17, 2011, 81-87.
  32. J. Łęski, M. Jeżewski: “Clustering Algorithms For Classification Methods”, Journal of Medical Informatics and Technologies, Vol. 20, 2012, pp.11-18.
  33. R. Czabański, M. Jeżewski, J. Jeżewski, J. Wróbel, K. Horoba, Robust extraction of fuzzy rules with artificial neural network based on fuzzy inference system. Int. J. Intelligent Information and Database Systems, Vol. 6(1), 2012, 77-92.
  34. R. Czabański, D. Roj, J. Jeżewski, K. Horoba, M. Jeżewski, Fuzzy prediction of fetal acidemia, Journal Of Medical Informatics & Technologies vol. 17, 2011, 81-87.
  35. R. Czabanski, J. Wrobel, J. Jezewski, M. Jezewski: ”Two-Step Analysis of the Fetal Heart Rate Signal as a Predictor of Distress” in “Intelligent Information and Database Systems”, Editors: J. S. Pan, S. M. Chen, N. T. Nguyen, LNAI 7197(II)/Lecture Notes in Computer Science, Springer Verlag, 2012, 431-438.
  36. T. Przybyła, J. Wróbel, R. Czabański, K. Horoba, T. Pander, M. Momot: “Segmentation of Biomedical Signals Using an Unsupervised Approach”, Journal of Medical Informatics and Technologies, 2012, Vol. 19, 124-131.

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