Search

Your search keyword '"Izonin, Ivan"' showing total 452 results
Start Over Author "Izonin, Ivan" Publication Year Range Last 50 years
452 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
Full Text
View/download PDF

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
Full Text
View/download PDF

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
Full Text
View/download PDF

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
Full Text
View/download PDF

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
Full Text
View/download PDF

24. Enhanced ANN-based ensemble method for bridge damage characterization using limited dataset.

Author

Izonin, Ivan, Nesterenko, Illia, Kazantzi, Athanasia K., Tkachenko, Roman, Muzyka, Roman, and Mitoulis, Stergios Aristoteles

Subjects
*ARTIFICIAL neural networks, *DATA augmentation, *CANTILEVER bridges, *INFRASTRUCTURE (Economics), *TENDONS, *PRESTRESSED concrete bridges
Abstract

Bridges are vital assets of transport infrastructure, systems, and communities. Damage characterization is critical in ensuring safety and planning adaptation measures. Nondestructive methods offer an efficient means towards assessing the condition of bridges, without causing harm or disruption to transport services, and these can deploy measurable evidence of bridge deterioration, e.g., deflections due to tendon loss. This paper presents an enhanced input-doubling technique and the Artificial Neural Network (ANN)-based cascade ensemble method for bridge damage state identification and is exclusively relying on small datasets, that are common in structural assessments. A new data augmentation scheme rooted in the principles of linearizing response surfaces is introduced, which significantly boosts the efficiency of intelligent data analysis when faced with limited volumes of data. Furthermore, improvements to a two-step ANN-based ensemble method, designed for solving the stated task, are presented. By adding the improved input-doubling methods as simple predictors in the first part of the cascade ensemble and optimizing it, we significantly boost accuracy (7%, 0.5%, and 8% based on R2 in predicting tendon losses for three critical zones that were defined across the deck of a real deteriorated prestressed balanced cantilever bridge). This improvement is strong evidence of the accuracy of the proposed method for the task at hand that is proven to be more accurate than other methods available in the international literature. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

25. 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
Full Text
View/download PDF

26. Enhanced Input-Doubling Method Leveraging Response Surface Linearization to Improve Classification Accuracy in Small Medical Data Processing.

Author

Izonin, Ivan, Tkachenko, Roman, Yendyk, Pavlo, Pliss, Iryna, Bodyanskiy, Yevgeniy, and Gregus, Michal

Subjects
DATA augmentation, DATA mining, CLASSIFICATION algorithms, ARTIFICIAL intelligence, ELECTRONIC data processing
Abstract

Currently, the tasks of intelligent data analysis in medicine are becoming increasingly common. Existing artificial intelligence tools provide high effectiveness in solving these tasks when analyzing sufficiently large datasets. However, when there is very little training data available, current machine learning methods do not ensure adequate classification accuracy or may even produce inadequate results. This paper presents an enhanced input-doubling method for classification tasks in the case of limited data analysis, achieved via expanding the number of independent attributes in the augmented dataset with probabilities of belonging to each class of the task. The authors have developed an algorithmic implementation of the improved method using two Naïve Bayes classifiers. The method was modeled on a small dataset for cardiovascular risk assessment. The authors explored two options for the combined use of Naïve Bayes classifiers at both stages of the method. It was found that using different methods at both stages potentially enhances the accuracy of the classification task. The results of the improved method were compared with a range of existing methods used for solving the task. It was demonstrated that the improved input-doubling method achieved the highest classification accuracy based on various performance indicators. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

29. 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
Full Text
View/download PDF

30. 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
Full Text
View/download PDF

33. 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
Full Text
View/download PDF

41. 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
Full Text
View/download PDF

42. 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
Full Text
View/download PDF

43. 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
Full Text
View/download PDF

44. 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
Full Text
View/download PDF

45. 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
Full Text
View/download PDF

46. 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
Full Text
View/download PDF

47. 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
Full Text
View/download PDF

48. 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
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results