2024 № 2 Системний аналіз, управління та інформаційні технології
Постійне посилання колекціїhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/84913
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Документ Software development and research for machine learning-based structural errors detection in BPMN models(Національний технічний університет "Харківський політехнічний інститут", 2024) Kopp, Andrii Mykhailovych; Orlovskyi, Dmytro Leonidovych; Gamayun, Igor Petrovych; Sapozhnykov, Illia VitaliiovychThe most important tool for process management is business process modeling. Business process models allow to graphically represent the sequences of events, activities, and decision points that make up business processes. However, models that contain errors in depicting the business process structure can lead to misunderstanding of a business process, errors in its execution, and associated expenses. Thus, the aim of this study is to ensure the comprehensibility of business process models by detecting structural errors in business process models and their subsequent correction. During the analysis of the Business Process Management (BPM) lifecycle, it was found that the created business process models do not have a stage of control for the presence of errors in them. Therefore, the paper analyzes and improves the BPM lifecycle using the proposed approach. In the improved BPM lifecycle, it is proposed to take into account the correctness validation stage of business process models using the developed software.Документ A software solution for real-time collection and processing of medical data for epilepsy patients(Національний технічний університет "Харківський політехнічний інститут", 2024) Kopp, Andrii Mykhailovych; Liutenko, Iryna Viktorivna; Yamburenko, Viktor Viktorovych; Pashniev, Andrii AnatoliovychThe rapid development of computer technologies has significantly impacted various sectors, including healthcare. The ability to collect, process, and visualize medical data in real time is becoming increasingly important, especially for managing chronic conditions such as epilepsy. This paper presents a web-based application designed for real-time monitoring of health indicators, enabling healthcare professionals to track patient data efficiently. The system automates the process of collecting data from fitness trackers, transmitting it via a mobile device to a server, and visualizing it in a web application. Its architecture employs a thin-client model with Node.js for backend logic and React.js for the user interface, ensuring scalability and responsiveness. These results confirm the system’s robustness and suitability for deployment in medical facilities.