Visual System for Configuring Machine Learning Models to Support IT Management and Decision-Making

Document Type : Research Paper

Authors

1 Prof., Kharkiv National University of Radio Electronics, Kharkiv 61166, Ukraine; Kharkiv National Automobile and Highway University, Kharkiv, 61002, Ukraine.

2 8440, Creekside green dr, apt 5302, Spring, TX, 77389, USA.

3 Professor, Institute of Production Systems Organization, Warsaw University of Technology, Warsaw, 02-524, Poland.

4 Associate Professor, State Biotechnological University, Kharkiv, 61002, Ukraine; Kharkiv National University of Internal Affairs, Kharkiv, 61080, Ukraine.

5 Professor, Ivan Franko National University of Lviv, Lviv, 79000, Ukraine.

6 Associate Professor, V. N. Karazin Kharkiv National University, Kharkiv, 61002, Ukraine.

7 Senior Lecturer, O. M. Beketov National University of Urban Economy in Kharkiv, Kharkiv, 61002, Ukraine.

10.22059/jitm.2025.105486

Abstract

Deep learning models have become indispensable across scientific and business domains, offering new approaches to problem-solving but requiring substantial technical expertise for their implementation. This article presents StudySupport, an open-source visual system for configuring and training machine learning models via a graphical interface rather than traditional coding. The system enables users to manage the entire pipeline - from data preprocessing and model construction to optimization and performance evaluation - while maintaining flexibility for advanced customization. By lowering the technical entry barrier, the StudySupport system facilitates the adoption of machine learning in IT management and organizational decision-making. The proposed framework supports faster integration of data-driven methods into enterprise information systems, reduces implementation costs, and empowers managers, analysts, and educators to leverage artificial intelligence in digital transformation processes. The study contributes to the field of information technology management by bridging the gap between advanced machine learning techniques and their practical application in business, education, and decision-support systems.

Keywords

References

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Journal of Information Technology Management
Volume 17, Issue 4
2025
Pages 117-135

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