УДК 004.93:004.89

В.М. ОПАНАСЕНКО
Інститут кібернетики ім. В.М. Глушкова НАН України, Київ, Україна,
 vlopanas@ukr.net

Ш.Х. ФАЗИЛОВ
Науково-дослідний інститут розвитку цифрових технологій та штучного інтелекту, Ташкент, Узбекистан,  sh.fazilov@gmail.com

М.Н. МІРЗАЄВ
Науково-дослідний інститут розвитку цифрових технологій та штучного інтелекту, Ташкент, Узбекистан,  m.n.mirzaev@yahoo.com

Ш.С. КАХАРОВ
Кокандський університет, Коканд, Узбекистан,
 sh.kaxarov93@gmail.com

МОДЕЛЬ АЛГОРИТМІВ РОЗПІЗНАВАННЯ, ЯКА БАЗУЄТЬСЯ
НА ПОРОГОВИХ ФУНКЦІЯХ ТА ОЦІНЦІ БЛИЗЬКОСТІ ОБ’ЄКТІВ

СПИСОК ЛІТЕРАТУРИ

• 1. Jiang Y., Li C. mRMR-based feature selection for classification of cotton foreign matter using hyperspectral imaging. Computers and Electronics in Agriculture. 2015. Vol. 119. P. 191–200.


• 2. Mo D., Lai Z. Robust jointly sparse regression with generalized orthogonal learning for image feature selection. Pattern Recognition. 2019. Vol. 93. P. 164–178.


• 3. Jiang Y., Li C. A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection. Mech. Syst. Signal Process. 2017. Vol. 91. P. 295–312.


• 4. Zini L., Noceti N., Fusco G., Odone F. Structured multi-class feature selection with an application to face recognition. Pattern Recognit. Lett. 2015. Vol. 55. P. 35–41.


• 5. Narzillo M., Bakhtiyor A., Shukrullo K., Bakhodirjon O., Gulbahor A. Peculiarities of face detection and recognition. 2021 International Conference on Information Science and Communications Technologies (ICISCT) (03–05 November 2021, Tashkent, Uzbekistan). IEEE, 2021. P. 1–5. https://doi.org/ 10.1109/ .


• 6. Fazilov Sh.Kh., Mirzaev O.N., Kakharov S.S. Building a local classifier for component-based face recognition. Lecture Notes in Computer Science. Cham: Springer, 2023. Vol. 13741. P. 177–187. https://doi.org/10.1007/ .


• 7. Opanasenko V.M., Fazilov Sh. Kh, Radjabov S.S., Kakharov S.S. Multilevel face recognition system. Cybernetics and Systems Analysis. 2024. Vol. 60, N 1. P. 146–151. https://doi.org/10.1007/ .


• 8. Opanasenko V.N., Fazilov Sh.Kh., Mirzaev O.N., Kakharov Sh.S. An ensemble approach to face recognition in access control systems. Journal of Mobile Multimedia. 2024, Vol. 20, N 3. P. 1–20. https://doi.org/10.13052/ .


• 9. Malakhov K.S. Insight into the digital health system of Ukraine (eHealth): Trends, definitions, standards, and legislative revisions. International Journal of Telerehabilitation. 2023. Vol. 15, N 2. https://doi.org/10.5195/ .


• 10. Opanasenko V., Zavyalov S. The FPGA implementation of pseudorandom word generation algorithms. IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) (07–09 September 2023, Dortmund, Germany). IEEE, 2023. P. 62–67. https://doi.org/10.1109/ .


• 11. Opanasenko V., Palahin A., Zavyalov S. The FPGA-Based problem-oriented on-board processor. 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) (18–21 September 2019, Metz, France). IEEE, 2019. Vol. 1. P. 152–157. https://doi.org/10.1109/ .


• 12. Kryvyi S.L., Opanasenko V.N., Zavyalov S.B. Algebraic operations on fuzzy sets and relations in automata interpretation implemented by logical hardware. Cybernetics and Systems Analysis. 2022. Vol. 58, N 4. P. 649–659. https://doi.org/10.1007/ .


• 13. Ablameyko S.V., Biryukov A.S., Dokukin A.A., D’yakonov A.G., Zhuravlev Y.I., Krasnoproshin V.V., Ryazanov V.V.E. Practical algorithms for algebraic and logical correction in precedent-based recognition problems. Computational Mathematics and Mathematical Physics. 2014. Vol. 54. P. 1915–1928.


• 14. Schlesinger M., Hlavac V. Ten lectures on statistical and structural pattern recognition. Part of the book series: Computational Imaging and Vision. Dordrecht: Springer, 2002. Vol. 24. 520 p. https://doi.org/10.1007/.


• 15. Guyon I., Elisseeff A. An introduction to variable and feature selection. J. Mach Learn Res. 2003. Vol. 3. P. 1157–1182.


• 16. Liu H., Motoda H. Feature selection for knowledge discovery and data mining. Norwell, MA: Kluwer Academic Publishers, 1998.


• 17. Saeys Y., Inza I., Larranaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007. Vol. 23, Iss. 19. P. 2507–2517. https://doi.org/10.1093/ .


• 18. Tang J., Alelyani S., Liu H. Feature selection for classification: A review. In: Data Classification: Algorithms and Applications. CRC Press, 2014. P. 37–64.


• 19. Li Y., Yang Y., Li G., Xu M., Huang W. A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection. Mechanical Systems and Signal Processing. 2017. Vol. 91. P. 295–312. https://doi.org/10.1016/ .


• 20. Senliol B., Gulgezen G., Yu L., Cataltepe Z. Fast Correlation Based Filter (FCBF) with a different search strategy. 23rd International Symposium on Computer and Information Sciences (27–29 October 2008, Istanbul, Turkey). IEEE, 2008. P. 1–4. https://doi.org/10.1109/ .


• 21. M., Okur M.C. A comprehensive review of feature selection and feature selection stability in machine learning. Gazi University Journal of Science. 2023. Vol. 36, N 4. P. 1506–1520. https://doi.org/10.35378/ .


• 22. Weston J., Elisseeff A., Schlkopf B., Tipping M. Use of the zero-norm with linear models and kernel methods. J. Mach. Learn. Res. 2003. Vol. 3. P. 1439–1461.


• 23. Viharos Z.J., Kis K.B., Fodor A., Buki M.I. Adaptive, Hybrid Feature Selection (AHFS). Pattern Recognition. 2021. Vol. 116. Article number 107932. https://doi.org/10.1016/ .


• 24. Shang R., Meng Y., Wang W., Shang F., Jiao L. Local discriminative based sparse subspace learning for feature selection. Pattern Recognition. 2019. Vol. 92. P. 219–230. https://doi.org/ 10.1016/ .


• 25. Zhang Y., Wang Q., Gong D.W., Song X.F. Nonnegative laplacian embedding guided subspace learning for unsupervised feature selection. Pattern Recognition. 2019. Vol. 93. P. 337–352. https://doi.org/10.1016/ .


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• 27. Fazilov Sh.Kh., Mamatov N., Samijonov A., Abdullaev Sh. Reducing the dimensionality of feature space in pattern recognition tasks. Journal of Physics: Conference Series. 2020. Vol. 1441. Article number 012139. https://doi.org/10.1088/ .


• 28. Fazilov Sh.Kh., Mirzaev N.M., Radjabov S.S., Mirzaeva G.R. Determination of representative features when building an extreme recognition algorithm. Journal of Physics: Conference Series. 2019. Vol. 1260, N 10. Article number 102003. https://doi.org/10.1088/ .


• 29. Nuritdinov J.T., Kakharov S.S., Dagur A. A new algorithm for finding the Minkowski difference of some sets. In: Artificial Intelligence and Information Technologies. CRC Press, 2024. P. 142–147.


• 30. Dermatology dataset (Multi-class classification). URL: https://www.kaggle.com/datasets/ .


• 31. Microbes dataset. Identify the ten different micro-forms of life. URL: https://www.kaggle.com/ datasets/ .


• 32. Dry Bean Classification. URL: https://www.kaggle.com/datasets/ .