ANALYSIS OF INFORMATION TECHNOLOGY IN DETERMINING THE EFFECTIVENESS OF TALENTED REQUIREMENTS

Zamira Yusupova; Baxtiyor Yusupov

Авторы

Zamira Yusupova Автор
Baxtiyor Yusupov Автор

Ключевые слова:

Artificial Intelligence, machine learning, cloud, blockchain, analysis, efficiency, information, technology

Аннотация

With the help of information technology, it is possible to quickly and accurately collect information about talented students. Information technology helps to identify the strengths and weaknesses of each student in the educational process. Through this, each student will be able to draw up an individual program, choose the most suitable methods for developing his talent. This article incorporates blockchain, cloud, machine learning, Artificial Intelligence and information technology. These technologies have been cited in detail in determining their talent requirements.

Биографии авторов

Zamira Yusupova

Tashkent University of Information Technology named after Mukhammad al-Khwarazmi
Baxtiyor Yusupov

Tashkent State Technical University Named After Islam Karimov

Библиографические ссылки

1. Sen J., Security and privacy issues in cloud computing //Cloud technology: concepts, methodologies, tools, and applications. – IGI global, 2022. – С. 1585-1630.

2. Modi C., et al. A survey on security issues and solutions at different layers of Cloud computing //The journal of supercomputing. – 2021. – Т. 63. – №. 2. – С. 561-592.

3. Khudoykulov Z., et al. Blockchain Based E-Voting System: Open Issues and Challenges //2021 International Conference on Information Science and Communications Technologies (ICISCT). – IEEE, 2021. – С. 1-5.

4. J. Guo, X. Ding, and W. Wu, “A blockchain-enabled ecosystem for distributed electricity trading in smart city, ”IEEE Internet of Things Journal, vol. 8, no. 3, pp. 2040–2050, 2021.

5. Csaky, R. (2019). Deep learning based chat bot models. arxiv preprint arxiv:1908.08835.

6. Karri, S. P. R., & Kumar, B. S. (2020, January). Deep learning techniques for implementation of chat bots. In 2020 International conference on computer communication and informatics (ICCCI) (pp. 1–5). IEEE.

7. Yoo, J., & Cho, Y. (2022). ICSA: Intelligent chat bot security assistant using Text-CNN and multi-phase real-time defence against SNS phishing attacks. Expert Systems with Applications, 207, 117893.

8. Sahoo, S. R., & Gupta, B. B. (2019). Hybrid approach for detection of malicious profiles in twitter. Computers & Electrical Engineering, 76, 65–81.

9. Gupta, B. B., Yadav, K., Razzak, I., Psannis, K., Castiglione, A., & Chang, X. (2021). A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment. Computer Communications, 175, 47–57.