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Tetiana Rekunenko

Work place: Department of Education Quality Assurance, Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine

E-mail: rekunenko22@ukr.net

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Biography

Tetiana Rekunenko was born in the city of Tokmak, Zaporizhia region, on December 5, 1988; has higher education; graduated from the National University of the State Tax Service of Ukraine in 2010; has been a candidate of law since March 28, 2013, majoring in “Administrative Law and Process, Financial Law, and Information Law”; and has been an associate professor at the Department of Operational and Investigative Activities since March 20, 2018. Tetiana Rekunenko has a higher education and completed her dissertation for the degree of Doctor of Law on the topic “Financial Security in Ukraine: Administrative and Legal Support” in the speciality “Administrative Law and Process; Financial Law; Information Law.” She is the secretary of the Scientific and Methodological Council of the Kharkiv National University of Internal Affairs. She is an expert in the National Agency for Quality Assurance in Higher Education. She has 118 publications. Author of 2 solo monographs, 4 co-authored monographs, and 7 co-authored scientific and methodological manuals. From November 2006 to December 2010, Tetiana Rekunenko studied at the Faculty of Tax Police of the National University of the State Tax Service of Ukraine. From December 2010 to July 2011, she held the position of operational officer in the operational and investigative department of the tax police in the Zaporizhzhia region. From July 2011 to August 2020, she held scientific and pedagogical positions at the Faculty of Training, Retraining, and Advanced Training of Tax Police Employees of the University of the State Fiscal Service of Ukraine. From October 2020 to July 2021, she held the position of Associate Professor of the Department of Law Enforcement and Police Studies, Faculty No. 1 of the Kryvyi Rih Educational and Scientific Institute of the Donetsk Law Institute of the Ministry of Internal Affairs of Ukraine; from July 2021 to October 2022, she held the position of Dean of Faculty No. 2 of the Kryvyi Rih Educational and Scientific Institute of the Donetsk State University of Internal Affairs; from October 2022 to August 2024, she held the position of Deputy Director of the Kryvyi Rih Educational and Scientific Institute for Educational and Research Activities of the Donetsk State University of Internal Affairs. From September 2024 to the present, she has been the Head of the Department of Education Quality Assurance of the Kharkiv National University of Internal Affairs. She has been awarded departmental awards from the State Tax Service of Ukraine and the Ministry of Internal Affairs of Ukraine. Field of scientific interests: financial law, financial security, financial stability and risks, and financial risk management.

Author Articles
Methods and Tools for Identifying Human Resource Lesions in Emergency Based on Multimodal Analysis and Deep Learning

By Yurii Ushenko Dmytro Uhryn Victoria Vysotska Lyubomyr Chyrun Zhengbing Hu Tetiana Rekunenko

DOI: https://doi.org/10.5815/ijigsp.2026.03.02, Pub. Date: 8 Jun. 2026

Emergencies of natural, technological, and military origin require rapid and accurate assessment of victims' conditions to support effective rescue and medical response. Traditional visual examination methods are often limited by stress, time pressure, and incomplete information, leading to delayed or inaccurate decisions. This study proposes a multimodal deep learning approach for automated identification of human resource lesions in emergency scenarios. The developed framework integrates visual, audio, and text/sensory data using convolutional neural networks, Transformer-based models, and a Transformer Cross-Attention fusion mechanism. The proposed architecture enables effective extraction and integration of heterogeneous features for lesion classification, severity estimation, and automated medical triage. Experimental evaluation was conducted on multimodal datasets containing injury images, audio recordings, and symptom descriptions. The model was trained using a combined loss function and evaluated with classification, regression, and triage metrics. The results demonstrate high system performance, achieving a macro-F1 score of 0.87, validation accuracy of 86–87%, and triage accuracy above 90%, including 95% for the RED category. The regression model for severity prediction achieved an R² value of 0.92, while modality importance analysis confirmed the dominant contribution of visual information. The experiments also showed stable model convergence and strong generalisation ability without significant overfitting. The proposed multimodal framework confirms the effectiveness of deep learning and cross-attention mechanisms for automated lesion identification and emergency medical triage. The developed approach can be applied in decision-support systems for rescue operations, emergency medicine, and intelligent VR/AR training simulators.

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