DSpace Зібрання:https://repository.hneu.edu.ua/handle/123456789/1782026-06-01T20:01:10Z2026-06-01T20:01:10ZPractical issues of the estimation and choice of machine learning models for the forecasts building in different subject domainsGryzun L.https://repository.hneu.edu.ua/handle/123456789/401652026-05-26T18:43:52Z2025-01-01T00:00:00ZНазва: Practical issues of the estimation and choice of machine learning models for the forecasts building in different subject domains
Автори: Gryzun L.
Короткий огляд (реферат): The paper is devoted to the practical issues of machine learning models estimation for predicting processes in different subject domains. In the progress of work, there were undertaken the number of core steps. An analysis of theoretical and practical scientific sources on the use of traditional and modern models for forecasting in various domains is conducted to identify possible consequences of the use of different models risks. The features of building predictive models in selected domains (medicine, meteorology, finance, and sales) are determined. The criteria and their metrics for the models’ estimation are determined. To perform a comparative analysis and estimation of of machine learning models for forecasting processes in selected subject areas, a web application was developed. A number of predictive models are constructed with the help of the developed web application. The results of forecasting using traditional and modern models in the selected subject domains are analyzed and evaluated according to criteria of accuracy, speed and complexity. Based on the comparative analysis of of machine learning predictive models, the practical recommendations have been formulated for the correct choice of a model for specific domain forecasting tasks. The prospects of the research are outlined in the lines of automatizing the selection of better model.2025-01-01T00:00:00ZDevelopment and Research of Batch Implementation of SQL-queries Based on the Rules of Their Ordering in Cloud EnvironmentsMinukhin S.https://repository.hneu.edu.ua/handle/123456789/400272026-05-14T21:21:50Z2026-01-01T00:00:00ZНазва: Development and Research of Batch Implementation of SQL-queries Based on the Rules of Their Ordering in Cloud Environments
Автори: Minukhin S.
Короткий огляд (реферат): The study proposes strategies for grouping (batching) queries in relational databases based on
random grouping, as well as on prioritizing the values of individual performance metrics –
execution time, DTU usage, and CPU load – and analyses their impact on the performance of the
Azure SQL Database cloud platform service. The research methodology included creating a
database in Azure SQL Database at different Azure service tiers – from S0 to S12 – to model
various configurations of computing resources. To simulate realistic scenarios of working with the
service, a database of a trading company with large sets of test data and several test database
queries of varying complexity was used. Query batching strategies were developed: random
grouping, grouping by ascending/descending query execution time, resource intensity (DTU
consumption), and CPU load. Each strategy was tested across all resource configurations through
multiple test trials, ensuring the relevance of the obtained results for an objective analysis. The
results obtained demonstrated the necessity of using a differentiated approach to selecting query
batching strategies depending on database size, query complexity, and the choice of query
prioritization models in batch mode.2026-01-01T00:00:00ZMutual Information Preference Optimization for Robust Multi- Modal Recipe GenerationShaposhnyk M.Minukhin S.https://repository.hneu.edu.ua/handle/123456789/400262026-05-14T21:17:59Z2026-01-01T00:00:00ZНазва: Mutual Information Preference Optimization for Robust Multi- Modal Recipe Generation
Автори: Shaposhnyk M.; Minukhin S.
Короткий огляд (реферат): This study evaluates the impact of Mutual Information Preference Optimization (MIPO) as a
corrective layer within a hybrid vision-language architecture. Rather than introducing a new
standalone framework, the research modifies an existing multimodal pipeline by integrating MIPO
to bridge the operational gap between a DenseNet-121 ensemble and Llama 3.1 8B. The central
hypothesis—that LLMs can act as autonomous semantic filters—was tested through contrastive
alignment, which synchronizes CNN-derived visual features with the textual latent space.
Experimental results on the Food-101 dataset validate this modification, demonstrating that the
system can successfully suppress false-positive detections without a complete retraining of the
visual backbone. By filtering out incongruous artifacts through preference optimization, the
modified architecture achieved a 60,8% reduction in semantic hallucinations. This confirms the
viability of using LLMs for real-time error correction in specialized domains, such as personalized
dietetics, where output fidelity is a critical requirement.2026-01-01T00:00:00ZArtificial intelligence in automated financial risk management systemsSkorin Yu.Lukyanchuk S.https://repository.hneu.edu.ua/handle/123456789/399762026-05-12T20:31:23Z2026-01-01T00:00:00ZНазва: Artificial intelligence in automated financial risk management systems
Автори: Skorin Yu.; Lukyanchuk S.
Короткий огляд (реферат): The article is aimed at studying the use of artificial in-telligence in automated financial risk management systems to improve the accuracy, efficiency and efficiency of managerial decision-making in the financial sector. The study consists in a comprehensive study of the theoretical and methodological foundations of the use of artificial intelligence in management systems, analysis of modern approaches to the classification and assessment of financial risks using machine learning algorithms, formation of the architecture of the decision support system based on forecasting models, as well as assessment of the effectiveness of the built models based on the results of simulation modeling. Within the framework of the study, a model for forecasting the credit risk of bank customers has been developed, which allows assessing solvency based on historical data and modern machine learning methods. The research method is modeling using machine learning tools, including neural networks and ensemble learning methods (Random Forest), as well as data analysis using platforms to visualize results and evaluate the effectiveness of models. Particular attention is paid to data preparation, selection of relevant features, evaluation of model accuracy, and construction of interpreted visualizations such as SHAP graphs, ROC curves, etc. The result of the study was the creation of an effective model for predicting credit risk, which demonstrates a sufficiently high level of classification accuracy and the ability to adapt to changes in incoming conditions. The practical significance of the study lies in the possibility of implementing the developed model into the existing automated financial risk management systems of banking institutions, which will reduce the level of credit losses, increase financial stability and provide more accurate risk management. This approach contributes to the de-velopment of intelligent financial systems, increasing the level of automation of managerial decisions and strengthening the competitiveness of financial institutions in modern market conditions.2026-01-01T00:00:00Z