ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN MATHEMATICAL MODELING

Authors

  • Aslonov Kodir Ziyodullayevich Asia International University

DOI:

https://doi.org/10.55640/

Keywords:

artificial intelligence, mathematical modeling, machine learning, neural networks, optimization theory, probabilistic models.

Abstract

Mathematical modeling plays a fundamental role in the development, analysis, and practical implementation of artificial intelligence (AI) systems. It provides a rigorous formal framework for representing real-world processes, learning mechanisms, and decision-making strategies. This paper examines the significance of mathematical models in artificial intelligence with particular emphasis on machine learning, neural networks, optimization algorithms, and probabilistic reasoning. The study demonstrates how mathematical formalization improves accuracy, robustness, interpretability, and scalability of AI systems. In addition, existing challenges and prospective research directions in mathematical modeling for artificial intelligence are discussed.

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References

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

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

3.Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach. Pearson.

4.Vapnik, V. N. (1998). Statistical Learning Theory. Wiley.

5.Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.

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Published

2026-02-06

How to Cite

ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN MATHEMATICAL MODELING. (2026). Journal of Multidisciplinary Sciences and Innovations, 5(02), 500-502. https://doi.org/10.55640/

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