RISK LEVEL FORECASTING USING ARTIFICIAL INTELLIGENCE

Authors

  • Ulkanova Saidakhon Khayrullo kizi 1st year Master's degree in Occupational Safety and Health of the Andijan State Technical Institute

Keywords:

artificial intelligence, risk, forecasting, technology, prediction

Abstract

 This article explores the role of artificial intelligence technologies in risk prediction. The effectiveness of approaches based on neural networks and statistics was also considered, and it was shown that the accuracy of predictions using artificial intelligence is higher than traditional methods. Therefore, it was noted that artificial intelligence is a reliable tool for identifying risks, and special attention should be paid to artificial intelligence and ethical approaches, which will be interpreted in the future.

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References

Russell, S. J., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.

Dinov, I. D. (2023). Data Science and Predictive Analytics: Biomedical and Health Applications using R (2nd ed.). Springer.

Breiman, L. (2001). Random forests. Machine Learning, 45 (1), 5-32. (https://doi.org/10.1023/A:1010933404324)

Chollet, F. (2018). Deep Learning with Python (2nd ed.). Manning Publications.

Ng, A. (2016). Machine learning yearning: Technical strategy for AI engineers, in the era of deep learning. Deeplearning.ai.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. (https://www.deeplearningbook.org/)

Brownlee, J. (2020). Machine Learning Mastery with Python: Understand your data, create accurate models, and work projects from end to end. Machine Learning Mastery.

Xolboyev, J., & Tadjibayev, M. (2022). Assessment of risk factors in electrical systems based on machine learning. Electronic Scientific Journal of Uzbekistan, 4 (3), 112-118.

Center for Technological Research of the National University of Uzbekistan. (2022). Methodology for predicting natural disasters using artificial intelligence. Science and Innovation, 3 (2), 74-80.

Tashkent University of Information Technologies. (2021). Artificial intelligence models for determining the risk of failure in technical systems. Information Technology Scientific Journal, 5 (4), 88-94.

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Published

2025-06-16

How to Cite

Ulkanova Saidakhon Khayrullo kizi. (2025). RISK LEVEL FORECASTING USING ARTIFICIAL INTELLIGENCE. Journal of Applied Science and Social Science, 15(06), 463–467. Retrieved from https://www.internationaljournal.co.in/index.php/jasass/article/view/1281

Issue

Vol. 15 No. 06 (2025): Volume 15 Issue 06

Section

Articles

License

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