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1Academic Journal
المؤلفون: Izonin, Ivan, Tkachenko, Roman, Yendyk, Pavlo, Pliss, Iryna, Bodyanskiy, Yevgeniy, Gregus, Michal
المصدر: Computation; Oct2024, Vol. 12 Issue 10, p203, 15p
مصطلحات موضوعية: DATA augmentation, DATA mining, CLASSIFICATION algorithms, ARTIFICIAL intelligence, ELECTRONIC data processing
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2Academic Journal
المؤلفون: Bodyanskiy, Yevgeniy, Pliss, Iryna, Vynokurova, Olena, Peleshko, Dmytro, Rashkevych, Yuriy
المصدر: Information Technology and Management Science; Vol 20, No 1 (2017): Information Technology and Management Science; 6-11 ; 2255-9094 ; 2255-9086
مصطلحات موضوعية: Artificial neural networks, computational intelligence, data compression, machine learning
وصف الملف: application/pdf
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3Academic Journal
المؤلفون: Bodyanskiy, Yevgeniy, Shafronenko, Alina, Pliss, Iryna
المصدر: Системні дослідження та інформаційні технології; № 4 (2022); 79-87 ; Системные исследования и информационные технологии; № 4 (2022); 79-87 ; System research and information technologies; No. 4 (2022); 79-87 ; 2308-8893 ; 1681-6048
مصطلحات موضوعية: комбінована оптимізація, нечітка кластеризація, еволюційні алгоритми, функція щільності, Fish School, combined optimization, fuzzy clustering, evolutionary algorithms, density functions
وصف الملف: application/pdf
Relation: http://journal.iasa.kpi.ua/article/view/275083/270207; http://journal.iasa.kpi.ua/article/view/275083
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4Academic Journal
المؤلفون: Izonin, Ivan, Tkachenko, Roman, Yemets, Kyrylo, Gregus, Michal, Tomashy, Yevhen, Pliss, Iryna
المصدر: Procedia Computer Science; 2024, Vol. 243, p32-39, 8p
مصطلحات موضوعية: DATA augmentation, BIOMEDICAL engineering, DATA analysis, ELECTRONIC data processing, ALGORITHMS
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5Academic Journal
المصدر: Information Technology and Management Science; Vol 18, No 1 (2015): Information Technology and Management Science; 70-77 ; 2255-9094 ; 2255-9086
مصطلحات موضوعية: Computational intelligence, evolutionary computations, fuzzy neural networks, hybrid intelligent systems
وصف الملف: application/pdf
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6Academic Journal
المؤلفون: Bodyanskiy, Yevgeniy, Pliss, Iryna, Vynokurova, Olena
المصدر: Information Technology and Management Science; Vol 16, No 1 (2013): Information Technology and Management Science; 47-52 ; 2255-9094 ; 2255-9086
مصطلحات موضوعية: Flexible activation-membership function, flexible neo-fuzzy neuron, forecasting, identification learning algorithm
وصف الملف: application/pdf
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7Academic Journal
المؤلفون: Bodyanskiy, Yevgeniy, Shafronenko, Alina, Pliss, Iryna
المصدر: System research and information technologies; No. 3 (2021); 110-119 ; Системные исследования и информационные технологии; № 3 (2021); 110-119 ; Системні дослідження та інформаційні технології; № 3 (2021); 110-119 ; 2308-8893 ; 1681-6048
مصطلحات موضوعية: нечітка кластеризація, теорія правдоподібності, еволюційні методи оптимізації, правдоподібна нечітка кластеризація, центроїди-прототипи, котяча зграя, режим трасування, режим пошуку, рівень належності, нечеткая кластеризация, теория правдоподобности, эволюционные методы оптимизации, правдоподобная нечеткая кластеризация, центроиды-прототипы, кошачья стая, режим трассировки, режим поиска, уровень принадлежности, fuzzy clustering, credibility theory, evolutionary methods of optimization, credibilistic fuzzy clustering, centroids-prototypes, cats swarm, tracing mode, seeking mode, membership level
وصف الملف: application/pdf
Relation: http://journal.iasa.kpi.ua/article/view/244607/242423; http://journal.iasa.kpi.ua/article/view/244607
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8Academic Journal
المؤلفون: Bodyanskiy, Yevgeniy, Deineko, Anastasiia, Pliss, Iryna, Chala, Olha
المصدر: The Open Bioinformatics Journal ; volume 14, issue 1, page 123-129 ; ISSN 1875-0362
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9Academic Journal
المصدر: Information Technology and Management Science, Vol 20, Iss 1, Pp 6-11 (2017)
مصطلحات موضوعية: Artificial neural networks, computational intelligence, data compression, machine learning, Information technology, T58.5-58.64
Relation: http://www.degruyter.com/view/j/itms.2017.20.issue-1/itms-2017-0001/itms-2017-0001.xml?format=INT; https://doaj.org/toc/2255-9094; https://doaj.org/article/8ae702f509d34b36b9b1cb156b9a45ff
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10Academic Journal
المؤلفون: Perova, Iryna, Pliss, Iryna
المصدر: International Journal of Intelligent Systems and Applications ; volume 9, issue 7, page 12-21 ; ISSN 2074-904X 2074-9058
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11Academic Journal
المؤلفون: Bodyanskiy Yevgeniy, Vynokurova Olena, Pliss Iryna, Tatarinova Yuliia
المصدر: Information Technology and Management Science, Vol 18, Iss 1, Pp 70-77 (2015)
مصطلحات موضوعية: Computational intelligence, evolutionary computations, fuzzy neural networks, hybrid intelligent systems, Information technology, T58.5-58.64
Relation: http://www.degruyter.com/view/j/itms.2015.18.issue-1/itms-2015-0011/itms-2015-0011.xml?format=INT; https://doaj.org/toc/2255-9094; https://doaj.org/article/a0f31e0bffc44a73aeadb10ae98bd3b3
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12Academic Journal
المؤلفون: Bodyanskiy, Yevgeniy, Chala, Olha, Kasatkina, Natalia, Pliss, Iryna
المصدر: Mathematical Biosciences & Engineering; 2022, Vol. 19 Issue 8, p8003-8018, 16p
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13Book
المؤلفون: Bodyanskiy, Yevgeniy, Deineko, Anastasiia, Pliss, Iryna, Chala, Olha
المصدر: Advances in Intelligent Systems and Computing ; Advances in Intelligent Systems and Computing V ; page 371-382 ; ISSN 2194-5357 2194-5365 ; ISBN 9783030632694 9783030632700
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14
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15Conference
المؤلفون: Bodyanskiy, Yevgeniy, Boiko, Olena, Pliss, Iryna, Volkova, Valentyna
المصدر: 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) ; page 304-307
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16Conference
المؤلفون: Deineko, Anastasiia, Zhernova, Polina, Gordon, Boris, Zayika, Oleksandr, Pliss, Iryna, Pabyrivska, Nelya
المساهمون: Kharkiv National University of Radio Electronics, Lviv Polytechnic National University, Tallinn University of Technology
مصطلحات موضوعية: big data, dynamic data mining, data stream mining, computational intelligence, EM-algorithm, fuzzy clustering, Kohonen’s self-learning, soft clustering
جغرافية الموضوع: 21-25 August 2018, Lviv, Львів
وصف الملف: 171-176; application/pdf; image/png
Relation: Data stream mining and processing : proceedings of the IEEE second international conference, 2018; http://www.ics.uci.edu/~mlearn/MLRepository.html; [1] C. C. Aggarwal, Data Mining. Cham: Springer, Int. Publ., Switzerland, 2015.; [2] M. Bramer, Principles of Data Mining. Springer-Verlag London, 2016.; [3] A. Bifet, R. Gavaldà, G. Holmes, and B. Pfahringer, Machine Learning for Data Streams with Practical Examples in MOA. The MIT Press, 2018.; [4] F. Höppner, F. Klawonn, R. Kruse, and T. Runkler, Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition. John Wiley & Sons. Chichester, 1999.; [5] C. C. Aggarwal and C. K. Reddy, Data Clustering. Algorithms and Application. Boca Raton: CRC Press, 2014.; [6] R. Xu and D. C. Wunsch, Clustering. IEEE Press Series on Computational Intelligence. Hoboken, NJ: John Wiley & Sons, Inc., 2009.; [7] A. Bifet, Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams, IOS Press, 2010.; [8] J. Kacprzyk, and W. Pedrycz, Springer Handbook of Computational Intelligence, Berlin Heidelberg: Springer, Verlag, 2015.; [9] K.-L. Du and M. N. S. Swamy, Neural Networks and Statistical Learning. London: Springer-Verlag, 2014.; [10] J.-C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, N.Y.: Plenum Press, 1981.; [11] Ye. V. Bodyanskiy, A. O. Deineko, and Y. V. Kutsenko, “On-line kernel clustering based on the general regression neural network and T. Kohonen’s self-organizing map,” Automatic Control and Computer Sciences, 51(1), pp. 55-62, 2017.; [12] J. Keller, J. C. Bezdek, R. Krishnapuram and N. Pal, Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. The Handbook of Fuzzy Sets. Kluwer, Dordrecht, Netherlands: Springer, vol. 4, 1999.; [13] B. Quost, and T. Denœux “Clustering and classification of fuzzy data using the fuzzy EM algorithm,” Fuzzy Sets and Systems. vol. 286, pp. 134-156, March 2016.; [14] J. Yu, Ch. Chaomu, and M. S. Yang, “On convergence and parameter selection of the EM and DA-EM algorithms for Gaussian mixtures,” Pattern Recognition, vol. 77, pp. 188-203, May 2018.; [15] X. L. Meng and D. B. Rubin, “Maximum likelihood estimation via the ECM algorithm:a general framework,” Biometrica, vol. 80, рр. 267-278, 1993.; [16] Ye. Bodyanskiy, “Computational intelligence techniques for data analysis,” Lecture Notes in Informatics, Bonn: GI, pp. 15 – 36, 2005.; [17] Ye. Gorshkov, V. Kolodyaznhiy and Ye., Bodyanskiy, “New recursive learning algorithms for fuzzy Kohonen clustering network,” 17th Int. Workshop on Nonlinear Dynamics of Electronic Systems, Rapperswil, Switzerland, pp. 58-61, 2009.; [18] L. Jain and C. Mumford, Computational Intelligence, Collaboration, Fuzzy and Emergence, Berlin: Springer, Vergal, 2009.; [19] S. Osowski, Sieci neuronowe do przetwarzania informacji, Warszawa: Oficijna Wydawnicza Politechniki Warszawskiej, 2006.; [20] A. B. Geva and I. Gath “Unsupervised optimal fuzzy clustering,” Pattern Analysis and Machine Intelligence, vol. 2, n.7, pp. 773-787, 1989.; [21] T. Kohonen, Self-Organizing Maps. Berlin: Springer-Verlag, 1995.; [22] Ye. Bodyanskiy, A. Deineko, Y. Kutsenko and O. Zayika, “Data streams fast EM-fuzzy clustering based on Kohonen`s self-learning,” 1th IEEE International Conference on Data Stream Mining & Processing (DSMP 2016), Lviv, Ukrane, pp. 309-313, 2016.; [23] A. B. Geva, “Clustering as a basis for evolving neuro-fuzzy modeling,” Evolving Systems, pp. 59-71, 2010.; [24] UCI Repository of machine learning databases. CA: University of California, Department of Information and Computer Science. [Online]. Available: http://www.ics.uci.edu/~mlearn/MLRepository.html; [10] J.-C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, N.Y., Plenum Press, 1981.; [11] Ye. V. Bodyanskiy, A. O. Deineko, and Y. V. Kutsenko, "On-line kernel clustering based on the general regression neural network and T. Kohonen’s self-organizing map," Automatic Control and Computer Sciences, 51(1), pp. 55-62, 2017.; [13] B. Quost, and T. Denœux "Clustering and classification of fuzzy data using the fuzzy EM algorithm," Fuzzy Sets and Systems. vol. 286, pp. 134-156, March 2016.; [14] J. Yu, Ch. Chaomu, and M. S. Yang, "On convergence and parameter selection of the EM and DA-EM algorithms for Gaussian mixtures," Pattern Recognition, vol. 77, pp. 188-203, May 2018.; [15] X. L. Meng and D. B. Rubin, "Maximum likelihood estimation via the ECM algorithm:a general framework," Biometrica, vol. 80, rr. 267-278, 1993.; [16] Ye. Bodyanskiy, "Computational intelligence techniques for data analysis," Lecture Notes in Informatics, Bonn: GI, pp. 15 – 36, 2005.; [17] Ye. Gorshkov, V. Kolodyaznhiy and Ye., Bodyanskiy, "New recursive learning algorithms for fuzzy Kohonen clustering network," 17th Int. Workshop on Nonlinear Dynamics of Electronic Systems, Rapperswil, Switzerland, pp. 58-61, 2009.; [20] A. B. Geva and I. Gath "Unsupervised optimal fuzzy clustering," Pattern Analysis and Machine Intelligence, vol. 2, n.7, pp. 773-787, 1989.; [22] Ye. Bodyanskiy, A. Deineko, Y. Kutsenko and O. Zayika, "Data streams fast EM-fuzzy clustering based on Kohonen`s self-learning," 1th IEEE International Conference on Data Stream Mining & Processing (DSMP 2016), Lviv, Ukrane, pp. 309-313, 2016.; [23] A. B. Geva, "Clustering as a basis for evolving neuro-fuzzy modeling," Evolving Systems, pp. 59-71, 2010.; Data Stream Online Clustering Based on Fuzzy Expectation-Maximization Approach / Anastasiia Deineko, Polina Zhernova, Boris Gordon, Oleksandr Zayika, Iryna Pliss, Nelya Pabyrivska // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 171–176. — (Dynamic Data Mining & Data Stream Mining).; © Національний університет „Львівська політехніка“, 2018; https://ena.lpnu.ua/handle/ntb/52484; Data Stream Online Clustering Based on Fuzzy Expectation-Maximization Approach / Anastasiia Deineko, Polina Zhernova, Boris Gordon, Oleksandr Zayika, Iryna Pliss, Nelya Pabyrivska // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 171–176. — (Dynamic Data Mining & Data Stream Mining).
الاتاحة: https://ena.lpnu.ua/handle/ntb/52484
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17Conference
المؤلفون: Bodyanskiy, Yevgeniy, Pliss, Iryna, Kopaliani, Daria, Boiko, Olena
المساهمون: Kharkiv National University of Radio Electronics
مصطلحات موضوعية: deep learning, artificial neural networks, image processing, 2D-neural networks, multilayer networks
جغرافية الموضوع: 21-25 August 2018, Lviv, Львів
وصف الملف: 519-523; application/pdf; image/png
Relation: Data stream mining and processing : proceedings of the IEEE second international conference, 2018; http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits; [1] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, pp. 436-444, 2015.; [2] J. Schmidhuber, “Deep Learning in neural networks: An overview,” Neural Networks, vol. 61, pp. 85-117, 2015.; [3] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.; [4] Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, “Deep learning for visual understanding: A review,” Neurocomputing, vol. 187, pp. 27-48, 2016.; [5] P. Stubberud, “A vector matrix real time backpropagation algorithm for recurrent neural networks that approximate multi-valued periodic functions,” Int. J. on Computational Intelligence and Application, 8(4), pp. 395-411, 2009.; [6] P. Daniušis and P. Vaitkus, “Neural networks with matrix inputs,” Informatica, 19, №4, pp. 477-486, 2008.; [7] M. Mohamadian, H. Afarideh, and F. Babapour, “New 2D MatrixBased Neural Network for Image Processing Applications,” IAENG International Journal of Computer Science, 42(3), pp. 265-274, 2015.; [8] J. Gao, Y. Guo, and Z. Wang, “Matrix neural networks,” in Proceedings of the14th International Symposium on Neural Networks (ISNN), Part II, Sapporo, Sapporo, Japan, pp. 1–10, 2017.; [9] V. M. Kuntsevych and M. M. Lychak, Synthesis of optimal and adaptive control systems. The game approach (В. М. Кунцевич, М. М. Лычак, Синтез оптимальных и адаптивных систем управления. Игровой подход). Kyiv : Naukova dumka, 1985.; [10] V. M. Kuntsevych, “On a solving of the problem of two-dimensional discrete filtration (synthesis of matrix filters),” Automatica i Telemekhanika, no. 6, pp. 68-78, 1987.; [11] Ye. V. Bodyanskiy, and I. P. Pliss, “On a solving of the problem of a matrix object controlling under uncertainty conditions,” Automatika i Telemekhanika, no. 2, pp. 175-178, 1990.; [12] Ye. Bodyanskiy, I. Pliss, and V. A. Timofeev, “Discrete adaptive identification and extrapolation of two-dimensional fields,” Pattern Recognition and Image Analysis, vol. 5, no. 3, pp. 410-416, 1995.; [13] S. Haykin, Neural Networks: A Comprehensive Foundation. Upper Saddle River, N. J. : Prentice Hall, Inc., 1999.; [14] C. M. Bishop, Neural Networks for Pattern Recognition. Oxford : Clarendon Press, 1995.; [15] http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits; [1] Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, pp. 436-444, 2015.; [2] J. Schmidhuber, "Deep Learning in neural networks: An overview," Neural Networks, vol. 61, pp. 85-117, 2015.; [4] Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, "Deep learning for visual understanding: A review," Neurocomputing, vol. 187, pp. 27-48, 2016.; [5] P. Stubberud, "A vector matrix real time backpropagation algorithm for recurrent neural networks that approximate multi-valued periodic functions," Int. J. on Computational Intelligence and Application, 8(4), pp. 395-411, 2009.; [6] P. Daniušis and P. Vaitkus, "Neural networks with matrix inputs," Informatica, 19, No 4, pp. 477-486, 2008.; [7] M. Mohamadian, H. Afarideh, and F. Babapour, "New 2D MatrixBased Neural Network for Image Processing Applications," IAENG International Journal of Computer Science, 42(3), pp. 265-274, 2015.; [8] J. Gao, Y. Guo, and Z. Wang, "Matrix neural networks," in Proceedings of the14th International Symposium on Neural Networks (ISNN), Part II, Sapporo, Sapporo, Japan, pp. 1–10, 2017.; [9] V. M. Kuntsevych and M. M. Lychak, Synthesis of optimal and adaptive control systems. The game approach (V. M. Kuntsevich, M. M. Lychak, Sintez optimalnykh i adaptivnykh sistem upravleniia. Ihrovoi podkhod). Kyiv : Naukova dumka, 1985.; [10] V. M. Kuntsevych, "On a solving of the problem of two-dimensional discrete filtration (synthesis of matrix filters)," Automatica i Telemekhanika, no. 6, pp. 68-78, 1987.; [11] Ye. V. Bodyanskiy, and I. P. Pliss, "On a solving of the problem of a matrix object controlling under uncertainty conditions," Automatika i Telemekhanika, no. 2, pp. 175-178, 1990.; [12] Ye. Bodyanskiy, I. Pliss, and V. A. Timofeev, "Discrete adaptive identification and extrapolation of two-dimensional fields," Pattern Recognition and Image Analysis, vol. 5, no. 3, pp. 410-416, 1995.; Deep 2D-Neural Network and its Fast Learning / Yevgeniy Bodyanskiy, Iryna Pliss, Daria Kopaliani, Olena Boiko // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 519–523. — (Machine Vision and Pattern Recognition).; © Національний університет „Львівська політехніка“, 2018; https://ena.lpnu.ua/handle/ntb/52436; Deep 2D-Neural Network and its Fast Learning / Yevgeniy Bodyanskiy, Iryna Pliss, Daria Kopaliani, Olena Boiko // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 519–523. — (Machine Vision and Pattern Recognition).
الاتاحة: https://ena.lpnu.ua/handle/ntb/52436
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18Book
المؤلفون: Bodyanskiy, Yevgeniy, Vynokurova, Olena, Pliss, Iryna, Peleshko, Dmytro, Rashkevych, Yuriy
المصدر: Advances in Dependability Engineering of Complex Systems ; Advances in Intelligent Systems and Computing ; page 49-59 ; ISSN 2194-5357 2194-5365 ; ISBN 9783319594149 9783319594156
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19Conference
المؤلفون: Perova, Iryna, Pliss, Iryna, Churyumov, Gennadiy, Eze, Franklin M., Mahmoud, Samer Mohamed Kanaan
المصدر: 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP) ; page 34-37
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20Book
المصدر: Green IT Engineering: Concepts, Models, Complex Systems Architectures ; Studies in Systems, Decision and Control ; page 229-244 ; ISSN 2198-4182 2198-4190 ; ISBN 9783319441610 9783319441627
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