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449 results on '"Izonin, Ivan"'

11. I-PNN: An Improved Probabilistic Neural Network for Binary Classification of Imbalanced Medical Data

Author

Izonin, Ivan, Tkachenko, Roman, Greguš, Michal, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Strauss, Christine, editor, Cuzzocrea, Alfredo, editor, Kotsis, Gabriele, editor, Tjoa, A Min, editor, and Khalil, Ismail, editor

Published
2022
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13. Prediction of Magnetic Remanence of Sm-Co Magnets Using Machine Learning Algorithms

Author

Trostianchyn, Andrii, Izonin, Ivan, Tkachenko, Roman, Duriahina, Zoia, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Hu, Zhengbing, editor, Petoukhov, Sergey, editor, Yanovsky, Felix, editor, and He, Matthew, editor

Published
2022
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14. An Approach Towards Vacuum Forming Process Using PostScript for Making Braille

Author

Mayik, Volodymyr, Dudok, Taras, Mayik, Lyudmyla, Lotoshynska, Nataliia, Izonin, Ivan, Kusmierczyk, Jacek, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Hu, Zhengbing, editor, Petoukhov, Sergey, editor, Yanovsky, Felix, editor, and He, Matthew, editor

Published
2022
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15. Investigation of Anomalous Situations in the Machine-Building Industry Using Phase Trajectories Method

Author

Liaskovska, Solomiya, Izonin, Ivan, Martyn, Yevgen, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Hu, Zhengbing, editor, Petoukhov, Sergey, editor, Yanovsky, Felix, editor, and He, Matthew, editor

Published
2022
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16. An Approach Toward Numerical Data Augmentation and Regression Modeling Using Polynomial-Kernel-Based SVR

Author

Izonin, Ivan, Tkachenko, Roman, Horbal, Natalya, Greguš, Michal, Verhun, Volodymyr, Tolstyak, Yaroslav, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Saraswat, Mukesh, editor, Roy, Sarbani, editor, Chowdhury, Chandreyee, editor, and Gandomi, Amir H., editor

Published
2022
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25. A Method for Reducing Training Time of ML-Based Cascade Scheme for Large-Volume Data Analysis.

Author

Izonin, Ivan, Muzyka, Roman, Tkachenko, Roman, Dronyuk, Ivanna, Yemets, Kyrylo, and Mitoulis, Stergios-Aristoteles

Subjects
*PRINCIPAL components analysis, *FEATURE extraction, *DATA analysis, *TRAINING needs, *ALGORITHMS
Abstract

We live in the era of large data analysis, where processing vast datasets has become essential for uncovering valuable insights across various domains of our lives. Machine learning (ML) algorithms offer powerful tools for processing and analyzing this abundance of information. However, the considerable time and computational resources needed for training ML models pose significant challenges, especially within cascade schemes, due to the iterative nature of training algorithms, the complexity of feature extraction and transformation processes, and the large sizes of the datasets involved. This paper proposes a modification to the existing ML-based cascade scheme for analyzing large biomedical datasets by incorporating principal component analysis (PCA) at each level of the cascade. We selected the number of principal components to replace the initial inputs so that it ensured 95% variance retention. Furthermore, we enhanced the training and application algorithms and demonstrated the effectiveness of the modified cascade scheme through comparative analysis, which showcased a significant reduction in training time while improving the generalization properties of the method and the accuracy of the large data analysis. The improved enhanced generalization properties of the scheme stemmed from the reduction in nonsignificant independent attributes in the dataset, which further enhanced its performance in intelligent large data analysis. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Improvement of the ANN-Based Prediction Technology for Extremely Small Biomedical Data Analysis.

Author

Izonin, Ivan, Tkachenko, Roman, Berezsky, Oleh, Krak, Iurii, Kováč, Michal, and Fedorchuk, Maksym

Subjects
ARTIFICIAL neural networks, DATA augmentation, BIOMEDICAL engineering, ARTIFICIAL intelligence, MACHINE learning
Abstract

Today, the field of biomedical engineering spans numerous areas of scientific research that grapple with the challenges of intelligent analysis of small datasets. Analyzing such datasets with existing artificial intelligence tools is a complex task, often complicated by issues like overfitting and other challenges inherent to machine learning methods and artificial neural networks. These challenges impose significant constraints on the practical application of these tools to the problem at hand. While data augmentation can offer some mitigation, existing methods often introduce their own set of limitations, reducing their overall effectiveness in solving the problem. In this paper, the authors present an improved neural network-based technology for predicting outcomes when analyzing small and extremely small datasets. This approach builds on the input doubling method, leveraging response surface linearization principles to improve performance. Detailed flowcharts of the improved technology's operations are provided, alongside descriptions of new preparation and application algorithms for the proposed solution. The modeling, conducted using two biomedical datasets with optimal parameters selected via differential evolution, demonstrated high prediction accuracy. A comparison with several existing methods revealed a significant reduction in various errors, underscoring the advantages of the improved neural network technology, which does not require training, for the analysis of extremely small biomedical datasets. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Contributors

Author

Adeniyi, Abidemi Emmanuel, primary, Adhikari, Surabhi, additional, Ajagbe, Sunday Adeola, additional, Awotunde, Joseph Bamidele, additional, Barsocchi, Paolo, additional, Bhardwaj, Sachit, additional, Bhoi, Akash Kumar, additional, Huy Cuong, Nguyen Ha, additional, Deepalakshmi, P., additional, Fong, Simon James, additional, Ganeshkumar, M., additional, Ghidoni, Stefano, additional, Ghimire, Awishkar, additional, Bernardo Gois, Francisco Nauber, additional, González-Briones, Alfonso, additional, Gopalakrishnan, E.A., additional, Guleria, Pratiyush, additional, Ijaz, Muhammad Fazal, additional, Izonin, Ivan, additional, Kosana, Vishalteja, additional, Li, Tengyue, additional, Loganathan, D., additional, Lumini, Alessandra, additional, Maguolo, Gianluca, additional, Lôbo Marques, João Alexandre, additional, Nagaraj, P., additional, Nanni, Loris, additional, Rana, Abu ul Hassan S., additional, Satapathy, Santosh, additional, Sharma, Akhilesh Kumar, additional, Nunes da Silveira, Jarbas Aryel, additional, Soman, K.P., additional, Sood, Manu, additional, Sowmya, V., additional, Srivastava, Devesh Kumar, additional, Teeparthi, Kiran, additional, Thapa, Surendrabikram, additional, Tiwari, Shamik, additional, Tkachenko, Roman, additional, and Madeiro, João Paulo do Vale, additional

Published
2022
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31. GRNN Approach Towards Missing Data Recovery Between IoT Systems

Author

Izonin, Ivan, Kryvinska, Natalia, Vitynskyi, Pavlo, Tkachenko, Roman, Zub, Khrystyna, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Nishino, Hiroaki, editor, and Miwa, Hiroyoshi, editor

Published
2020
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32. Missing Data Imputation Through SGTM Neural-Like Structure for Environmental Monitoring Tasks

Author

Mishchuk, Oleksandra, Tkachenko, Roman, Izonin, Ivan, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Hu, Zhengbing, editor, Petoukhov, Sergey, editor, Dychka, Ivan, editor, and He, Matthew, editor

Published
2020
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40. An approach toward improvement of ensemble method's accuracy for biomedical data classification.

Author

Izonin, Ivan, Muzyka, Roman, Tkachenko, Roman, Gregus, Michal, Kustra, Natalya, and Mitoulis, Stergios-Aristoteles

Subjects
MACHINE learning, BIOMEDICAL engineering, INFORMATION processing, DIAGNOSIS, DATA analysis
Abstract

Amidst rapid technological and healthcare advancements, biomedical data classification using machine learning (ML) is pivotal for revolutionizing medical diagnosis, treatment, and research by organizing vast healthcarerelated data. Despite efforts to apply single ML models on clean datasets, satisfactory classification accuracy can still be elusive. In such cases, MLbased ensembles offer a promising solution. This paper explores cascaded ensembles as highly accurate methods. Existing cascade classifiers often partition large datasets into equal unique parts, limiting accuracy due to insufficient amount of useful information processed by weak classifiers of all levels of the cascade ensemble. To address this, we propose an improved cascaded ensemble scheme using a different data sampling approach. Our method forms larger subsamples at each cascade level, enhancing accuracy, and generalization properties during biomedical data analysis. Experimental comparisons demonstrate substantial increases in classification accuracy and generalization properties of the improved cascade ensemble. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Committee of the SGTM Neural-Like Structures with Extended Inputs for Predictive Analytics in Insurance

Author

Tkachenko, Roman, Izonin, Ivan, Greguš ml., Michal, Tkachenko, Pavlo, Dronyuk, Ivanna, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Younas, Muhammad, editor, Awan, Irfan, editor, and Benbernou, Salima, editor

Published
2019
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43. Model and Principles for the Implementation of Neural-Like Structures Based on Geometric Data Transformations

Author

Tkachenko, Roman, Izonin, Ivan, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Hu, Zhengbing, editor, Petoukhov, Sergey, editor, Dychka, Ivan, editor, and He, Matthew, editor

Published
2019
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44. Imbalance Data Classification via Neural-Like Structures of Geometric Transformations Model: Local and Global Approaches

Author

Tkachenko, Roman, Doroshenko, Anastasiya, Izonin, Ivan, Tsymbal, Yurii, Havrysh, Bohdana, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Hu, Zhengbing, editor, Petoukhov, Sergey, editor, Dychka, Ivan, editor, and He, Matthew, editor

Published
2019
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45. Committee of the Combined RBF-SGTM Neural-Like Structures for Prediction Tasks

Author

Tkachenko, Roman, Tkachenko, Pavlo, Izonin, Ivan, Vitynskyi, Pavlo, Kryvinska, Natalia, Tsymbal, Yurii, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Awan, Irfan, editor, Younas, Muhammad, editor, Ünal, Perin, editor, and Aleksy, Markus, editor

Published
2019
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46. Multiple Linear Regression Based on Coefficients Identification Using Non-iterative SGTM Neural-like Structure

Author

Izonin, Ivan, Tkachenko, Roman, Kryvinska, Natalia, Tkachenko, Pavlo, Greguš ml., Michal, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Rojas, Ignacio, editor, Joya, Gonzalo, editor, and Catala, Andreu, editor

Published
2019
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47. SGD-Based Wiener Polynomial Approximation for Missing Data Recovery in Air Pollution Monitoring Dataset

Author

Izonin, Ivan, Greguš ml., Michal, Tkachenko, Roman, Logoyda, Mykola, Mishchuk, Oleksandra, Kynash, Yurii, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Rojas, Ignacio, editor, Joya, Gonzalo, editor, and Catala, Andreu, editor

Published
2019
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