Статьи

AUTOMATED DECISION-MAKING SYSTEM BASED ON THE ANALYSIS OF BIG DATA OF NETWORK OBJECTS

Том 1 № 21 (2023): Наука и инновация 39-47

DOI: 10.5281/zenodo.8330789 2023-09-08 Статьи Open Access

Авторы

  • Narzullo Rajabov Candidate of physical and mathematical sciences, chief researcher of the scientific department of "Perspective team" LLC
  • Temur Azamov Independent researcher of Tashkent University of Information Technologies
  • Olimboy Shavkatov Master of the Urganch branch of the Tashkent University of Information Technologies

Аннотация

This article presents an overview of an automated decision-making system that utilizes big data analysis of network objects. The system collects a vast amount of data from network devices, preprocesses it, and applies various analytical techniques to extract insights and make automated decisions. The article discusses the importance of data quality, validation, and preprocessing, as well as model validation and evaluation. It emphasizes the role of domain expertise and human oversight in the decision-making process. Continuous monitoring, feedback loops, and regular system audits are highlighted as essential practices to ensure accuracy and reliability. The article concludes by emphasizing the significance of user training and awareness for effective utilization of the system.

Ключевые слова:

Библиографические ссылки

Chen, M., Mao, S., and Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209.

Zhang, Y., Chen, M., and Song, H. (2018). Big Data Analytics for Network Intrusion Detection: Challenges and Opportunities. IEEE Network, 32(5), 70-75.

Gandomi, A., and Haider, M. (2015). Beyond the Hype: Big Data Concepts, Methods, and Analytics. International Journal of Information Management, 35(2), 137-144.

Raghupathi, W., and Raghupathi, V. (2014). Big Data Analytics in Healthcare: Promise and Potential. Health Information Science and Systems, 2(1), 3.

Davenport, T. H., and Patil, D. J. (2012). Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, 90(10), 70-76.

Zhang, Z., and Patel, V. M. (2018). Deep Learning in Mobile and Wireless Networking: A Survey. IEEE Communications Surveys & Tutorials, 20(3), 2224-2287.

LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444.

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

Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media.

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S.,... Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211-252.

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

Sutton, R. S., and Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.

Goodfellow, I. J., Shlens, J., and Szegedy, C. (2015). Explaining and Harnessing Adversarial Examples. In International Conference on Learning Representations (ICLR).

Li, Y., Li, Y., and Zhang, Y. (2020). Explainable Artificial Intelligence: A Review. IEEE/CAA Journal of Automatica Sinica, 7(4), 858-876.

Carreira-Perpinán, M. Á. (2018). Neural Networks and Deep Learning. University of California, Merced.

OECD. (2019). Artificial Intelligence in Society. Organisation for Economic Co-operation and Development.

European Commission. (2018). Ethics Guidelines for Trustworthy AI. European Commission High-Level Expert Group on Artificial Intelligence.

Wu, X., Zhu, X., Wu, G. Q., and Ding, W. (2014). Data Mining with Big Data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97-107.

Chen, J., and Zhang, C. (2014). Data-Intensive Applications, Challenges, Techniques and Technologies: A Survey on Big Data. Information Sciences, 275, 314-347.

Zikopoulos, P., Eaton, C., deRoos, D., Deutsch, T., and Lapis, G. (2011). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill.

География читателей

2 Просмотры
0 Загрузки PDF
2 Стран

    Опубликован

    2023-09-08

    Выпуск

    Раздел

    Статьи

    Как цитировать

    Rajabov , N. ., Azamov, T. ., & Shavkatov , O. . (2023). AUTOMATED DECISION-MAKING SYSTEM BASED ON THE ANALYSIS OF BIG DATA OF NETWORK OBJECTS. Наука и инновации, 1(21), 39-47. https://doi.org/10.5281/zenodo.8330789
    Innovative Academy RSC
    Article metrics Views and PDF downloads
    0 Views
    0 Downloads