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Item type:Наукова стаття, Factors of Influence and Support for Increasing Efficiency of Air Transport Logistics Before and After COVID-19(Jelgava : Latvia University of Life Sciences and Technologies, 2025-05-20) Kryvovyazyuk, Igor; Smerichevskyi, Serhii; Tolstushko, Nataliya; Valetskyi, BohdanSpreading the COVID-19 pandemic caused a profound and long-lasting negative impact on the dynamics of air transportation of passengers and cargo, despite the priority role of aviation companies in the global economy. The pandemic also negatively affected the efficiency of air transport logistics, which necessitated a more in-depth analysis of the factors of influence on changes in its level. The study reveals a methodological approach that allows to make a sound assessment of internal and external factors of influence and, based on its results, to develop a system of organizational and economic solutions for improving the efficiency of air transport logistics. In the context of the above, the advantages of using the integral indicators method for analyzing the efficiency of aviation companies logistics are clarified. Based on the methods of selective observation and calculation of the system of relative and average values of changes in basic indicators, global trends in the development of aviation commerce are summarized and aviation companies that occupy leading positions in the provision of air transportation of passengers and cargo are identified. By synthesizing the methods of integral and point evaluation, the influence of sub-factors of external and internal logistics efficiency of the world's leading aviation companies for passenger transportation in the periods before and after COVID-19 was analyzed. It was found that the aviation industry has fully recovered after the spread of the COVID-19 pandemic in terms of dynamics and efficiency of air transport logistics. As proposals based on the research results, a system of goals, influencing factors and resource provision for increasing the efficiency of air transport logistics in the post-COVID period was specified.Item type:Наукова стаття, Development of a smart personnel security system using machine learning(CEUR Workshop Proceedings, 2025-09-25) Kryvovyazyuk, Igor; Bilychenko, Maksym; Kasianova, Nataliia; Smerichevskyi, Serhii; Lavrynenko, OleksandrInsider threats remain one of the most challenging aspects of organizational security, particularly in the era of digital transformation and widespread remote access to sensitive data. This study proposes a machine learning–based approach to personnel security that combines Isolation Forest and Local Outlier Factor algorithms with behavioral features enhanced through the use of large language models (LLMs). To improve detection accuracy, user web activity was classified using LLM-generated labels derived from website content analysis. Experimental results demonstrate strong model performance in identifying insider activity at the user level, with high detection accuracy and minimal false classifications. In addition, time-to-detection analysis revealed that most insider threats were identified before or shortly after the onset of malicious behavior. The findings suggest that the proposed system is not only effective in capturing behavioral anomalies but also feasible for real-time deployment in enterprise environments.