Сучасні інформаційні системи

Постійне посилання на розділhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/62915

Офіційний сайт http://ais.khpi.edu.ua/

У журналі публікуються результати досліджень з експлуатації та розробки сучасних інформаційних систем у різних проблемних галузях.

Рік заснування: 2017. Періодичність: 4 рази на рік. ISSN 2522-9052 (Print)

Новини

Включений до "Переліку наукових фахових видань України, в яких можуть публікуватися результати дисертаційних робіт на здобуття наукових ступенів доктора і кандидата наук" (технічні науки) наказом Міністерства освіти і науки України від 04.04.2018 № 326 (додаток 9, п. 56).

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Автор: search.filters.author.Laktionov, Oleksandr

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  • Ескіз
    Документ
    Implementation of unsupervised learning models for analyzing the state's security level
    (Національний технічний університет "Харківський політехнічний інститут", 2024) Laktionov, Oleksandr; Shefer, Oleksandr; Laktionova, Iryna; Halai, Vasyl; Podorozhniak, Andrii
    The process of creating unsupervised learning models and their peculiarities in tasks of analyzing the state's security level has been investigated. Techniques for creating the basic k-means model and its improvement through the use of Pearson correlation as a distance metric have been considered. Determining cluster centers was performed by both the basic method and the Cochran's maps method. The optimal quality indicator, according to the results of clustering, was considered to be the model demonstrating the minimum value of the DaviesBouldin index. The proposed model for clustering the state's security level differs from existing ones by using as input estimates derived from a comprehensive indicator based on the principles of interaction and emergent properties. This allows obtaining advantages of the clustering model in terms of the Davies-Bouldin index.
  • Ескіз
    Документ
    Practical principles of integrating artificial intelligence into the technology of regional security predicting
    (Національний технічний університет "Харківський політехнічний інститут", 2024) Shefer, Oleksandr; Laktionov, Oleksandr; Pents, Volodymyr; Hlushko, Alina; Kuchuk, Nina
    Objective. The aim is to enhance the efficiency of diagnostics for determining the level of air attack safety through the practical integration principles of artificial intelligence. Methodology. Models and technologies for safety diagnostics of the region (territorial community) have been explored. The process of building an artificial intelligence model requires differentiation of objects at a level to accumulate assessments-characteristics of aerial vehicles. The practical integration principles of artificial intelligence into the forecasting technology are based on the Region Safety Index, used for constructing machine learning models. The optimal machine learning model of the proposed approach is selected from a list of several models. Results. A technology for predicting the level of regional safety based on the Safety Index has been developed. The recommended optimal model is the Random Forest model ([('max_depth', 13), ('max_features', 'sqrt'), ('min_samples_leaf', 1), ('min_samples_split', 2), ('n_estimators', 79)]), demonstrating the most effective quality indicators of MAE; MAX; RMSE 0.005; 0.083; 0.0139, respectively. Scientific Novelty. The proposed approach is based on a linear model of the Region Safety Index, which, unlike existing ones, takes into account the interaction of factors. This allows for advantages of the proposed method over existing approaches in terms of the root mean square error of 0.496; 0.625, respectively. In turn, this influences the quality of machine learning models. Practical Significance. The proposed solutions are valuable for diagnosing the level of safety in the region of Ukraine, particularly in the context of air attacks.