Mashinali o‘qitish algoritmlarini real sohalarda qo‘llash va usullarini tahlil qilish

Authors

  • K.K Seitnazarov

    Nukus State Technical University

  • A.T Sultanbaeva

    Nukus State Technical University

  • Sh.M Jalgasbaeva

Keywords: machine learning, supervised learning, unsupervised learning, reinforcement learning, real-world applica- tions, machine learning in medicine, raud detection, cybersecurity, аlgorithm analysis, аrtificial intelligence

Abstract

This article reviews the applications of machine learning algorithms in various real-world applications. The main types of algorithms - supervised learning, unsupervised learning, and reinforcement learning - are analyzed in practical applications, and the advantages and limitations of the algorithms are discussed, as well as the future prospects of this technology. The effectiveness of machine learning is illustrated based on research results, real-world examples, and statistical data.

References

1. Abdullaeva M.I., Juraev D.B., Ochilov M.M., Rakhimov M.F., Uzbek Speech Synthesis Using Deep Learning Algorithms. The 14th International Conference on Intelligent Human Computer Interaction, Springer, (LNCS,volume 13741). –Tashkent: 2023, -Р. 39-50.

2. Jumaev T., Toirov O., Baburbek B. Obyektlarni tanib olishda neyron tarmoqning oʻrni. Journal of advanced research and stability (JARS) Vol 1, № 06 681-684, 2021.

3. Mamatov N., Jalelova M. Тасвир контрастини ошириш усули ва контраст баҳолаш мезон оптимал жуфтлиги. Digital transformation and artificial intelligence. 2023, 1(2), 158-167.

4. Пилявская И.М. Аналитический обзор применения технологий машинного обучения в финансовых ассистен- тах. // Вестник науки и образования. 2022. № 4-2(124). -С. 29–34. DOI: 10.24411/2312-8089-2022-10402.

5. Сааков Д.В. Применение методов машинного обучения для оптимизации производственных процессов в металлургической промышленности. // Инновации и инвестиции. 2023. № 5. -С. 308–311.

6. Сеитназаров К.К., Туремуратова Б.К. /Применение технологии искусственного интеллекта в системе дистан- ционного образования. / Новости образования: исследование в XXI веке 1 (1), 176-185.

7. Seitnazarov K.K. Dosımbetov A.M., Aytanov A.K. /Strategy for Organization of Computational Experiments of the Functioning of Underground Water Inlets Using a Fuzzy Multiple Approach // International Conference on Information Science and Communications Technologies (ICISCT), -Tashkent, Uzbekistan 2020. -C. 1-4.

8. Usmanov R.N., Seitnazarov К.К. The problem of information model development for the relationship between hydrogeological object and its fuzzy-deterministic model // The Advanced Science Journal. USA. 2014. -C. 67-73.