Machine learning as a key element of modern monitoring systems

DOI: 10.31673/2412-9070.2025.047306

Abstract

The article is dedicated to exploring the possibilities of using machine learning (ML) as an innovative tool to enhance the efficiency of managing organizations and educational institutions. The focus is on the implementation of ML in modern monitoring systems, which enable the optimization of management processes and data-driven decision-making. The study thoroughly analyzes the main types of machine learning, including supervised, unsupervised, and reinforcement learning, as well as their practical applications in tasks such as intelligent data analysis, pattern detection, and predicting the behavior of systems and management entities. Special emphasis is placed on the issues of data collection, cleaning, and preprocessing, which are critically important for developing high quality ML models. The importance of selecting relevant features that directly impact the accuracy and reliability of model results is highlighted. Additionally, the process of selecting and adapting machine learning algorithms to the specifics of management tasks, as well as their integration into digital systems to ensure automated monitoring and real-time data analysis, is examined.
The research covers a wide range of machine learning applications across various sectors, including information technology, industry, ecology, healthcare, security, and transportation. The article also addresses ethical and legal aspects of ML use, particularly issues of data privacy and the need for interdisciplinary research to ensure compliance with modern standards.
The analysis demonstrates that machine learning is a fundamental component of modern monitoring systems, contributing to their adaptability, forecasting accuracy, and process automation.
In the context of Ukraine’s digital transformation, the article emphasizes the prospects for developing intelligent monitoring systems applicable in IT, energy, healthcare, agriculture, and cybersecurity. Special attention is given to the strategic importance of ML in protecting critical infrastructure amid contemporary challenges, as well as the need for government support, data standardization, and training of qualified personnel to ensure the successful implementation of such technologies.

Keywords: machine learning; monitoring systems; data forecasting; data analysis; clustering; anomaly detection; malware; data visualization; digital transformation; cybersecurity.