Sun’iy intellekt asosida tadqiqotchilik faoliyati ko‘rsatkichlarini baholashning matematik modeli va algoritmini ishlab chiqish

Authors

  • Elmurod Zaylobidinovich Abdullayev

    Andijan State University image/svg+xml

Keywords: artificial intelligence, mathematical model, algorithm, machine learning, data processing, intelligent system, classification, data analysis, evaluation model, theoretical foundations of informatics

Abstract

This article addresses the development of a mathematical model and algorithm for evaluating research activity indicators based on artificial intelligence technologies. During the study, a system of parameters characterizing research activities was established, and their interrelationships were analyzed using mathematical modeling methods. The proposed model includes the stages of data collection, preprocessing, feature extraction, and classification. The use of machine learning algorithms enables the automated evaluation of research activity indicators and improves the objectivity of decision-making processes. Experimental results demonstrate the high accuracy and reliability of the developed model. The proposed approach can be effectively applied in intelligent information systems, expert systems, and decision support systems.

References

1. Russell S., Norvig P. Artificial Intelligence: A Modern Approach. 4th Edition. Pearson Education, 2021.

2. Mitchell T.M. Machine Learning. McGraw-Hill, 1997.

3. Goodfellow I., Bengio Y., Courville A. Deep Learning. MIT Press, 2016.

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

5. Han J., Kamber M., Pei J. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2011.

6. Alpaydin E. Introduction to Machine Learning. MIT Press, 2020.

7. Géron A. Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow. O’Reilly Media, 2022.

8. Nilsson N.J. The Quest for Artificial Intelligence. Cambridge University Press, 2010.

9. Murphy K.P. Machine Learning: A Probabilistic Perspective. MIT Press, 2012.

10. Onwujekwe G., Weistroffer H.R. Intelligent Decision Support Systems: An Analysis of the Literature and a Framework for Development. Information Systems Frontiers, 2025.

11. Explainable Artificial Intelligence-Based Decision Support Systems: A Recent Review. Electronics, 2024.

12. Kelleher J.D., Namee B.M., D’Arcy A. Fundamentals of Machine Learning for Predictive Data Analytics. MIT Press, 2020.

13. Aggarwal C.C. Neural Networks and Deep Learning. Springer, 2018.

14. Shalev-Shwartz S., Ben-David S. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.

15. Witten I.H., Frank E., Hall M.A. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2016.