Visual System for Configuring Machine Learning Models to Support IT Management and Decision-Making | ||
| Journal of Information Technology Management | ||
| دوره 17، شماره 4، 2025، صفحه 117-135 اصل مقاله (1.24 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2025.105486 | ||
| نویسندگان | ||
| Vitalina Babenko* 1؛ Andrii Brazhnykov2؛ Nataliia Gavkalova3؛ Serhii Rudenko4؛ Мarianna Оliskevych5؛ Olena Fridman6؛ Dariia Babenko7 | ||
| 1Prof., Kharkiv National University of Radio Electronics, Kharkiv 61166, Ukraine; Kharkiv National Automobile and Highway University, Kharkiv, 61002, Ukraine. | ||
| 28440, Creekside green dr, apt 5302, Spring, TX, 77389, USA. | ||
| 3Professor, Institute of Production Systems Organization, Warsaw University of Technology, Warsaw, 02-524, Poland. | ||
| 4Associate Professor, State Biotechnological University, Kharkiv, 61002, Ukraine; Kharkiv National University of Internal Affairs, Kharkiv, 61080, Ukraine. | ||
| 5Professor, Ivan Franko National University of Lviv, Lviv, 79000, Ukraine. | ||
| 6Associate Professor, V. N. Karazin Kharkiv National University, Kharkiv, 61002, Ukraine. | ||
| 7Senior Lecturer, O. M. Beketov National University of Urban Economy in Kharkiv, Kharkiv, 61002, Ukraine. | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Visual System؛ StudySupport system؛ Machine Learning؛ Decision-support Model؛ IT Management؛ Decision-Making | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 489 تعداد دریافت فایل اصل مقاله: 309 |
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