Giyosov Ulugbek Eshpulatovich

@sbtsue.uz

Associate Professor, PhD, Faculty of Digital economy, Department of Digital economy and information technologies
Samarkand branch of Tashkent state university of economics



                       

https://researchid.co/bek99989

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Graphics and Computer-Aided Design, Computer Science Applications, Computer Science, Artificial Intelligence

FUTURE PROJECTS

Creating a three-dimensional virtual 3D university software system in a meta-environment using artificial intelligence technologies

This project aims to use artificial intelligence (AI) to create a 3D model of the human face and integrate it with an avatar in a virtual university.


Applications Invited
Programming application
5

Scopus Publications

Scopus Publications

  • Online Machine Learning for Intrusion Detection in Electric Vehicle Charging Systems
    Fazliddin Makhmudov, Dusmurod Kilichev, Ulugbek Giyosov, and Farkhod Akhmedov
    MDPI AG
    Electric vehicle (EV) charging systems are now integral to smart grids, increasing the need for robust and scalable cyberattack detection. This study presents an online intrusion detection system that leverages an Adaptive Random Forest classifier with Adaptive Windowing drift detection to identify real-time and evolving threats in EV charging infrastructures. The system is evaluated using real-world network traffic from the CICEVSE2024 dataset, ensuring practical applicability. For binary intrusion detection, the model achieves 0.9913 accuracy, 0.9999 precision, 0.9914 recall, and an F1-score of 0.9956, demonstrating highly accurate threat detection. It effectively manages concept drift, maintaining an average accuracy of 0.99 during drift events. In multiclass detection, the system attains 0.9840 accuracy, precision, and recall, with an F1-score of 0.9831 and an average drift event accuracy of 0.96. The system is computationally efficient, processing each instance in just 0.0037 s, making it well-suited for real-time deployment. These results confirm that online machine learning methods can effectively secure EV charging infrastructures. The source code is publicly available on GitHub, ensuring reproducibility and fostering further research. This study provides a scalable and efficient cybersecurity solution for protecting EV charging networks from evolving threats.

  • Enhancing Teaching Approach with 3D Primitives in Virtual and Augmented Reality
    F. M. Nuraliev, U. E. Giyosov, and Yoshihiro Okada
    Springer International Publishing

  • Implementation of the R-Function for virtual environment objects of the simple shapes
    Nuraliev Fakhriddin, Giyosov Ulugbek, Usmonov Akbarjon, and Aliyev Azizbek
    IEEE

  • Improve teaching and learning approach 3D primitives with virtual and augmented reality
    Nuraliev Fakhriddin Murodillaevich, Giyosov Ulugbek Eshpulatovich, and Kenjaboev Kuvondik
    IEEE

  • Integration of virtual reality and 3D modeling use of environments in education
    Nuraliyev Faxriddin Murodillayevich, Ulugbek Giyosov Eshpulatovich, and Jiyanov Oybek Pardaboyevich
    IEEE

RECENT SCHOLAR PUBLICATIONS