https://repository.hneu.edu.ua/handle/123456789/19807 2026-02-12T10:07:18Z https://repository.hneu.edu.ua/handle/123456789/38705 Назва: Choice of information tools for learning under modern challenges Автори: Solodovnyk G.; Shapovalova O. Короткий огляд (реферат): The study addresses the problem of selecting information and communication tools for learning under conditions of rapid digital transformation in science and education. It justifies the need for a systematic, multi-criteria decision-making approach and defines a model for rational selection of educational technologies. Based on a comparative analysis of existing methods, the analytic hierarchy process is identified as the most flexible framework for evaluating digital learning tools. A criteria-based decision model and algorithm are developed and implemented as a spreadsheet-based decision support system for selecting online educational platforms. The proposed approach is adaptable, practically applicable across educational institutions, and supports digital transformation while accounting for evolving technologies, formats, and user needs. 2025-01-01T00:00:00Z https://repository.hneu.edu.ua/handle/123456789/38400 Назва: The Data Dissemination Planning Tasks Process Model Into Account the Entities Differentity Автори: Semenov S.; Yenhalychev S.; Lymarenko V.; Gavrilenko S. Короткий огляд (реферат): the main innovation of the article is the development of a scheme for processing entity requests based on their preliminary analysis and adaptation to the capabilities of processor, communication and memory resources of computing facilities. This should make it possible to reduce the time spent on performing applied data processing tasks. The document presents a model for the planning process for data dissemination tasks. A distinctive feature of the model is that it takes into account the heterogeneity of entities by including additional blocks for their analysis and adaptation to the existing capabilities of processor and other resources. A generalized model for scheduling tasks and entities with dependencies has also been developed. As a result, GERT networks of the process of scheduling distribution tasks for a separate n-th set of data type are obtained. The simulation result is presented in the form of an analytical expression for calculating the scheduling time for data dissemination tasks, taking into account the heterogeneity of entities. 2022-01-01T00:00:00Z https://repository.hneu.edu.ua/handle/123456789/38399 Назва: Model of the Dynamics of the State of Educational Content Recommender System Автори: Muchacki M.; Yenhalychev S.; Sitnikova O. Короткий огляд (реферат): The report presents a generalized model of the dynamics of the state of the educational content recommender system. It differs from the known ones by taking into account the factor of changes in user preferences over time and the possibility of adapting to them. The relevance of the report is caused by the need to take into account the dynamic nature of user preferences and changes in interests over time. To solve this problem, the authors propose to apply GERT-networks (Graphical Evaluation and Review Technique). GERT networks allow describing the probability of the system staying in different states over time, which is important for understanding and predicting changes in user interests. As a result of modeling, analytical expressions for calculating the probability-time characteristics of the recommender system dynamics are derived. Experiments were conducted to estimate the probability distribution density function of the formation time of educational content recommendation. The results showed the possibility of estimating the maximum values of the probability distribution density of the task of forming educational content recommendations, and, accordingly, the indicator of the time of forming recommendations. 2023-01-01T00:00:00Z https://repository.hneu.edu.ua/handle/123456789/38369 Назва: Аналіз можливостей використання машинного глибокого навчання для розпізнавання сутностей Автори: Єнгаличев С. О.; Семенов С. Г. Короткий огляд (реферат): У роботі досліджено проблему підвищення точності аналізу та прогнозування характеристик трафіку в гетерогенних хмарних системах, дані яких піддаються впливу гаусівського шуму та аномальних викидів. Запропоновано новий підхід до оцінювання продуктивності мережі (зокрема, втрат пакетів) із використанням алгоритмів глибокого навчання (Deep Learning), модифікованих застосуванням робастних M-оцінок замість традиційної середньоквадратичної помилки (MSE). 2023-01-01T00:00:00Z