PROBLEMS AND SOLUTIONS OF INFORMATION EXCHANGE IN INTEGRATED CONTROL SYSTEMS
Keywords:
information exchange, risk management, border control, inter-agency integration, data security, risk analysis, information model.Abstract
This article provides a scientific analysis of the risk management system in information exchange processes between various control authorities at border checkpoints. The study examines issues of ensuring data security in information exchange channels, integrating fragmented systems, and centralizing inter-agency information flows. An automatic information risk classification mechanism based on a multi-criteria assessment model, CART decision tree, and Random Forest algorithm is proposed. The research findings establish a methodological basis for transitioning to proactive management of information exchange processes at border checkpoints.
References
O‘zbekiston Respublikasi Prezidentining 2019 yil 28 martdagi PQ-4254-son qarori. https://lex.uz/docs/4258732
Raxmanov Q.S., Rustamova M.Ya. Veterinariya yagona axborot tizimini ishlab chiqishda raqamli integratsiya. Raqamli Transformatsiya va Sun’iy Intellekt. 2025. 3(3). 147-152 b.
Raxmanov Q.S., Rustamova M.Ya., Tilavova N.O. Digitization of veterinary points and improvement of the control system. Journal of Multidisciplinary Sciences and Innovations. 2026. 5(3). pp. 2546-2552.
Raxmanov Q.S., Rustamova M.Ya., Boboqulov I.X. VIS-CHEGARA yagona axborot tizimi orqali veterinariya nazoratini raqamlashtirish samaradorligi. Raqamli Transformatsiya va Sun'iy Intellekt. 2026. 4(1). 166-170 b.
Rakhmanov K.S., Rustamova M.Y. Studying the experience of foreign countries in developing a unified veterinary information system in Uzbekistan. American Journal of Technology and Applied Sciences. 2025. 36. pp. 17-23.
Aven T., Seif A., Karatzoudi K. A new perspective for the integration of intelligence and risk management in a customs and border control context. Journal of Risk Research. 2023. 26(4). P. 433-449. https://doi.org/10.1080/13669877.2023.2176912
Thomann B., Kuntzer T., Schupbach-Regula G., Rieder S. Investigating the use of machine learning algorithms to support risk-based animal welfare inspections. Frontiers in Veterinary Science. 2024. Vol. 11. P. 1401007. https://doi.org/10.3389/fvets.2024.1401007
Wu L.-Y., Liu F.-M., Weng S.-S., Lin W.-C. EL V.2 Model for Predicting Food Safety Risks at Taiwan Border Using the Voting-Based Ensemble Method. Foods. 2023. 12(11). P. 2118. https://doi.org/10.3390/foods12112118
Oldroyd R.A., Morris M.A., Birkin M. Predicting Food Safety Compliance for Informed Food Outlet Inspections: A Machine Learning Approach. International Journal of Environmental Research and Public Health. 2021. 18(23). P. 12635. https://doi.org/10.3390/ijerph182312635
Selim A., Megahed A., Kandeel S. et al. Determination of Seroprevalence and Risk Factors Using Classification and Regression Tree. Animals. 2021. 11(4). P. 1165. https://doi.org/10.3390/ani11041165
Zhang Q., Lu Z., Liu Z. et al. Application of Machine Learning in Food Safety Risk Assessment. Foods. 2025. 14(23). P. 4005. https://doi.org/10.3390/foods14234005
Wang J. et al. Application of machine learning to the monitoring and prediction of food safety: A review. Comprehensive Reviews in Food Science and Food Safety. 2022. 21. P. 416-434. https://doi.org/10.1111/1541-4337.12868
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