TARMOQ MA’LUMOTLARIGA INTELLEKTUAL ISHLOV BERISH MODELLARI VA USULLARI

Mo‘minov Bahodir Boltayevich; Husanov Sherzod Abdimonnonovich

Authors

Mo‘minov Bahodir Boltayevich Tashkent State University of Economics
Husanov Sherzod Abdimonnonovich Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti

Keywords:

tarmoq ma’lumotlari, intellektual ishlov berish, sun’iy intellekt, mashinali o‘qitish, ma’lumotlar mayningi, graf neyron tarmoqlari, anomaliya aniqlash

Abstract

Ushbu ishda tarmoq ma’lumotlariga intellektual ishlov berishning zamonaviy modellari va usullari tahlil qilingan. Tarmoq ma’lumotlarining hajmi va murakkabligi ortib borayotgani sababli, ularni samarali tahlil qilish va qayta ishlash uchun sun’iy intellekt, mashinali o‘qitish hamda ma’lumotlar mayning (data mining) texnologiyalaridan foydalanish zarurligi asoslab berilgan.
Tadqiqotda tarmoq ma’lumotlarining tuzilishi, ularning turlari (ijtimoiy tarmoqlar, kompyuter tarmoqlari, sensor tarmoqlari va boshqalar) hamda ma’lumot oqimlari bilan bog‘liq muammolar yoritilgan. Shuningdek, intellektual ishlov berishda qo‘llaniladigan asosiy usullar — klassifikatsiya, klasterlash, prognozlash, anomaliyalarni aniqlash va graf analizi usullarining afzalliklari va qo‘llanilish sohalari keltirilgan.
Ishda shuningdek, neyron tarmoqlar, graf neyron tarmoqlari (GNN), konvolyutsion va rekurrent modellardan foydalanish imkoniyatlari hamda ularning tarmoq ma’lumotlari tahlilidagi samaradorligi tahlil qilingan. Natijada, intellektual ishlov berish usullari orqali tarmoq ma’lumotlaridan qimmatli ma’lumotlarni avtomatik tarzda ajratib olish, xavfsizlikni ta’minlash va boshqaruv qarorlarini qabul qilishni takomillashtirish mumkinligi ko‘rsatilgan.

References

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer, New York.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Leskovec, J., Rajaraman, A., & Ullman, J. D. (2020). Mining of Massive Datasets (3rd ed.). Cambridge University Press.

Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781.

Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3), 273–297.

Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann Publishers.

Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Representation Learning on Graphs: Methods and Applications. IEEE Data Engineering Bulletin, 40(3), 52–74.

Yang, Z., Cohen, W. W., & Salakhutdinov, R. (2016). Revisiting Semi-Supervised Learning with Graph Embeddings. Proceedings of the 33rd International Conference on Machine Learning (ICML), 40–48.

Zhang, D., Yin, J., Zhu, X., & Zhang, C. (2018). Network Representation Learning: A Survey. IEEE Transactions on Big Data, 6(1), 3–28.

Nishonov A. H., Xusanov Sh. A. (2023). Tarmoq ma’lumotlarini tahlil qilishda mashinali o‘qitish usullarining qo‘llanilishi. Toshkent axborot texnologiyalari universiteti ilmiy axborotlari, №4, 45–53.

Mo‘minov B. B. (2022). Intellektual axborot tizimlarida ma’lumotlar tahlili va optimallashtirish usullari. O‘zbekiston milliy universiteti ilmiy jurnallari, №2, 87–95.

Xusanova M. Sh., & Rahmonov A. S. (2024). Sun’iy intellekt asosida tarmoq xavfsizligini monitoring qilish. Fan va innovatsiyalar jurnali, 1(3), 112–119.

Vaswani, A., et al. (2017). Attention is All You Need. Advances in Neural Information Processing Systems (NeurIPS), 30, 5998–6008.

Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2019). How Powerful Are Graph Neural Networks? International Conference on Learning Representations (ICLR).