Improving a machine learning method for an automated control system

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.