Application of SVM, FFNNs, k-NN and Their Ensembles for Identifying Functionally Reliable Systems
Active informatization of various spheres of human activity requires increasingly widespread use of information systems. Along with the growing need for their application, the demands on the systems themselves are also rising. Some of these demands can be addressed through technical improvements; ho...
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| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Axioms |
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| Online Access: | https://www.mdpi.com/2075-1680/14/4/237 |
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| author | Oleg Barabash Andriy Makarchuk Pavlo Open’ko Serhii Korotin |
| author_facet | Oleg Barabash Andriy Makarchuk Pavlo Open’ko Serhii Korotin |
| author_sort | Oleg Barabash |
| collection | DOAJ |
| description | Active informatization of various spheres of human activity requires increasingly widespread use of information systems. Along with the growing need for their application, the demands on the systems themselves are also rising. Some of these demands can be addressed through technical improvements; however, there are aspects for which this alone may not suffice. One such requirement is functional stability. While it is technically possible to ensure functional stability, a number of indicators and criteria have been developed for assessing it. However, applying these indicators in real-world conditions requires significant computational resources. Therefore, there is a need to develop more optimized methods to evaluate whether a system is functionally stable or to improve existing ones. Recently, interest in machine learning methods as a means of optimizing various computations has grown significantly. Accordingly, the question arises as to whether machine learning can be applied to assess the functional stability of information systems. In this study, we investigate the application of some popular classification methods—SVM, FFNNs, k-NN and their ensembles—to determine compliance with one of the requirements for the structure of information systems, which helps evaluate whether the system is functionally stable. |
| format | Article |
| id | doaj-art-be5b80c2fa38470da81aea2dda5b929f |
| institution | DOAJ |
| issn | 2075-1680 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj-art-be5b80c2fa38470da81aea2dda5b929f2025-08-20T03:14:20ZengMDPI AGAxioms2075-16802025-03-0114423710.3390/axioms14040237Application of SVM, FFNNs, k-NN and Their Ensembles for Identifying Functionally Reliable SystemsOleg Barabash0Andriy Makarchuk1Pavlo Open’ko2Serhii Korotin3Department of Software Engineering for Power Industry, Institute of Nuclear and Thermal Energy, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 03056 Kyiv, UkraineDepartment of Software Engineering for Power Industry, Institute of Nuclear and Thermal Energy, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 03056 Kyiv, UkraineInstitute of Aviation and Air Defense, The National Defence University of Ukraine, 03049 Kyiv, UkraineInstitute of Aviation and Air Defense, The National Defence University of Ukraine, 03049 Kyiv, UkraineActive informatization of various spheres of human activity requires increasingly widespread use of information systems. Along with the growing need for their application, the demands on the systems themselves are also rising. Some of these demands can be addressed through technical improvements; however, there are aspects for which this alone may not suffice. One such requirement is functional stability. While it is technically possible to ensure functional stability, a number of indicators and criteria have been developed for assessing it. However, applying these indicators in real-world conditions requires significant computational resources. Therefore, there is a need to develop more optimized methods to evaluate whether a system is functionally stable or to improve existing ones. Recently, interest in machine learning methods as a means of optimizing various computations has grown significantly. Accordingly, the question arises as to whether machine learning can be applied to assess the functional stability of information systems. In this study, we investigate the application of some popular classification methods—SVM, FFNNs, k-NN and their ensembles—to determine compliance with one of the requirements for the structure of information systems, which helps evaluate whether the system is functionally stable.https://www.mdpi.com/2075-1680/14/4/237functional stabilityinformation systemsmachine learningneural networkssupport vector machineoptimization |
| spellingShingle | Oleg Barabash Andriy Makarchuk Pavlo Open’ko Serhii Korotin Application of SVM, FFNNs, k-NN and Their Ensembles for Identifying Functionally Reliable Systems Axioms functional stability information systems machine learning neural networks support vector machine optimization |
| title | Application of SVM, FFNNs, k-NN and Their Ensembles for Identifying Functionally Reliable Systems |
| title_full | Application of SVM, FFNNs, k-NN and Their Ensembles for Identifying Functionally Reliable Systems |
| title_fullStr | Application of SVM, FFNNs, k-NN and Their Ensembles for Identifying Functionally Reliable Systems |
| title_full_unstemmed | Application of SVM, FFNNs, k-NN and Their Ensembles for Identifying Functionally Reliable Systems |
| title_short | Application of SVM, FFNNs, k-NN and Their Ensembles for Identifying Functionally Reliable Systems |
| title_sort | application of svm ffnns k nn and their ensembles for identifying functionally reliable systems |
| topic | functional stability information systems machine learning neural networks support vector machine optimization |
| url | https://www.mdpi.com/2075-1680/14/4/237 |
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