SUN’IY INTELLEKT, RAQAMLI TEXNOLOGIYALAR VA ILMIY-TEXNIK INNOVATSIYALAR

Авторы

  • Zufar Abdurashidov TATU. Telekommunikatsiya injiniringi fakulteti, Telekommunikatsiya texnologiylari yo‘nalishi talabasi Автор

DOI:

https://doi.org/10.65164/qvexc961

Ключевые слова:

sun’iy intellekt, Big Data, raqamli texnologiyalar, raqamli ilm-fan, ilmiy-texnik innovatsiyalar, dasturiy injiniring, axborot xavfsizligi, kompyuter injiniringi

Аннотация

Mazkur maqolada sun’iy intellekt, Big Data va raqamli texnologiyalarning zamonaviy ilmiy tadqiqotlardagi o‘rni chuqur tahlil qilinadi. Ilm-fan sohasida raqamli transformatsiya jarayonlari, ma’lumotlarga asoslangan tadqiqot metodologiyalarining shakllanishi hamda ilmiy-texnik innovatsiyalarning fanlararo integratsiyadagi ahamiyati yoritib berilgan. Shuningdek, dasturiy injiniring, axborot xavfsizligi va kompyuter injiniringi kabi IT yo‘nalishlarining sun’iy intellektga asoslangan ilmiy tizimlarni rivojlantirishdagi strategik roli asoslab beriladi. Maqolada kelajak ilm-fani uchun dolzarb bo‘lgan muammolar, istiqbollar va ilmiy chaqiriqlar bo‘yicha mualliflik xulosalari keltirilgan 

Библиографические ссылки

1. Shlezinger, A., & Shlezinger, M. (2020). The New AI: The Future of Artificial Intelligence is

Here. Apress.

2. McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). CommunicationEfficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20th

International Conference on Artificial Intelligence and Statistics (AISTATS).

3. Shor, P. W. (1994). Algorithms for quantum computation: discrete logarithms and factoring.

Proceedings of the 35th Annual Symposium on Foundations of Computer Science.

4. Alagic, G., et al. (2020). Status Report on the Second Round of the NIST Post-Quantum

Cryptography Standardization Process. NIST Internal Report 8309.

5. Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2020). Federated

Optimization in Heterogeneous Networks. Proceedings of the Conference on Machine Learning

and Systems (MLSys).

6. Bhagoji, A. N., Chakraborty, S., Mittal, P., & Calo, S. (2019). Analyzing Federated Learning

through Poisoning Attacks. arXiv preprint arXiv:1911.11835.

7. Zhu, L., Liu, Z., & Han, S. (2019). Deep Leakage from Gradients. Advances in Neural

Information Processing Systems (NeurIPS), 32.

8. Bernstein, D. J., & Lange, T. (2017). Post-quantum cryptography. Nature, 549(7671), 188-194.

9. Yin, X., et al. (2021). A Post-Quantum Secure Federated Learning Framework. 2021 IEEE

International Conference on Blockchain and Cryptocurrency (ICBC).

10. Ducas, L., Lyubashevsky, V., & Prest, T. (2018). CRYSTALS-Kyber: a CCA-secure modulelattice-based KEM. 2018 IEEE European Symposium on Security and Privacy (EuroS&P).

11. Ducas, L., Kiltz, E., Lepoint, T., Lyubashevsky, V., Schwabe, P., Schanck, G., & Stehle, D.

(2018). CRYSTALS-Dilithium: a lattice-based digital signature scheme. IACR Transactions on

Cryptographic Hardware and Embedded Systems, 2018(1), 238-268.

12. Kairouz, P., et al. (2021). Advances and Open Problems in Federated Learning. Foundations and

Trends in Machine Learning, 14(1–2), 1-210

Опубликован

2025-12-29