Improving a Machine Learning Method for an Automated Control System - Metric Proximal Gradient Approach

Aug 3, 2026

Upload by Levi

The paper focuses on improving an automated learning management system by integrating the metric proximal gradient method. The approach targets greater efficiency, higher educational quality, and improved safety by automating routine administrative workflows and supporting individualized curricula. It uses machine learning for performance analysis and suspicious activity detection to enhance transparency and reliability. The method is designed for efficient optimization via diagonal step sizing and non-monotonic linear search, with event logging through a specialized database, and includes clustering and regression for analytics. Results indicate strong convergence and adaptability, enabling personalized learning routes and anomaly-based security maintenance in dynamic learning environments.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Introduction

Improving automated learning management system

Machine learning for student performance and personalization

Transparency and fairness through automation

Data security and cyber threat mitigation

Improving a machine learning method for an automated control system Viktoriia Zhebka 1,†, Oleksii Ananchenko1,†, Kateryna Osadcha2,3,*,†, Serhii Zhebka1,† and Andrii Aronov1,† 1 State University ofInformation and Communication Technologies, 7 Solomenskaya str., 03110 Kyiv, Ukraine 2 Norwegian University of Science and Technology, 1 Høgskoleringen, 7034 Trondheim, Norway 3 Bogdan Khmelnitsky Melitopol State Pedagogical University, 59 Naukove mistechko str., 69000 Zaporizhzhia, Ukraine Abstract The paper is devoted to the improvement of the automated learning management system by integrating the metric proximal gradient method. Improving the automated learning management system helps to increase the efficiency, quality, and safety of the educational process by automating routine tasks and implementing individualized curricula. The paper discusses the use of machine learning to analyze student performance and detect suspicious user activity, which increases the transparency and reliability of the system. The use of the metric proximal gradient method ensures efficient solutions to optimization problems and increases the adaptability of the model in a dynamic educational environment. Also the paper presents an improved approach to automated learning management systems through the implementation of an advanced machine learning method based on the metric proximal gradient algorithm. The research addresses key challenges in educational process management, including system efficiency, quality assurance, and security enhancement. The proposed method incorporates a specialized database for comprehensive event logging and implements clustering and regression algorithms for student performance analysis. The improved metric proximal gradient algorithm demonstrates effective convergence properties through diagonal step sizing and non-monotonic linear search strategies. Results indicate that this approach provides enhanced optimization capabilities for handling complex data structures and adapting to dynamic educational environments. The implementation shows particular promise in personalizing educational routes, optimizing curricula, and maintaining system security through automated anomaly detection. Keywords machine learning method, automated control system, metric proximal gradient, educational process 1. Introduction Improving an automated learning management system (ACS) is an important step towards increasing the efficiency, quality, and safety of the educational process. Automating routine administrative tasks, such as creating timetables, recording attendance, and generating reports, allows you to optimize resource management and reduce staff workload. This allows administrators to focus on more strategic tasks, such as improving curricula and managing the quality of education [1, 2] . Improving the quality of the educational process is one of the key goals of improving the automated control system. The introduction of machine learning technologies allows for more accurate tracking and analysis of student performance, which helps to identify learning problems on time and provide the necessary support. Such innovations also allow for the creation of individualized curricula, which provide a more personalized CPITS-II 2024: Workshop on Cybersecurity Providing in Information |and Telecommunication Systems II, October 26, 2024, Kyiv, Ukraine ∗ Corresponding author. † These authors contributed equally. [viktoria_zhebka@ukr.net](viktoria_zhebka@ukr.net) (V. Zhebka); [ananchenko.oe@gmail.com](ananchenko.oe@gmail.com) (O. Ananchenko); [katheryna.osadcha@ntnu.no](katheryna.osadcha@ntnu.no) (K. Osadcha); [szhebka@hotmail.com](szhebka@hotmail.com) (S. Zhebka); [webx.ghost@gmail.com](webx.ghost@gmail.com) (A. Aronov) CEUR ~~ ~~[Workshop](Workshop ceur-ws.org)[ ceur-ws.org](Workshop ceur-ws.org)[ ](Workshop ceur-ws.org)[Proceedings](Proceedings ISSN 1613-0073)[ ISSN 1613-0073](Proceedings ISSN 1613-0073) ap

FAQs

How does the proposed method improve an automated learning management system?

It enhances the system by integrating the metric proximal gradient method, automating routine administrative tasks, and supporting individualized curricula based on student data analysis.

What machine learning functions are used for student-related insights and security?

The approach analyzes student performance and detects suspicious user activity, improving transparency and reliability while strengthening system security.

Why is the metric proximal gradient method highlighted in the optimization process?

It provides efficient solutions to optimization problems and improves model adaptability in dynamic educational environments, with effective convergence achieved using diagonal step sizing and non-monotonic linear search.

Aug 3, 2026

Upload by Levi