Work

Intelligent Decision Support System for Evacuation and Replacement of Personnel in Hybrid Threats

Year: 2026

Type: article

Abstract: The article develops an intelligent decision support system for personnel evacuation and replacement in the context of hybrid threats, integrating deep learning and multi-criteria optimisation methods. The main task is to predict personnel’s functional state and determine priorities for their evacuation or replacement in real time, while accounting for simultaneous cybernetic, physical, and information-psychological threats. The study uses a convolutional neural network (CNN) module for classifying personnel status and a long short-term memory (LSTM) module for predicting the probability of functionality loss. The study’s novelty lies in the complex integration of these technologies for human resource management in emergencies, as well as in the development of an adaptive model for dynamic threat assessment and personnel status. An important feature is the use of multi-criteria methods for decision-making on evacuation and replacement with limited resources. The experimental results show an increase in the predictive accuracy of personnel’s functional state to 90% and effective resource optimisation, ensuring loss minimisation in crises. The research contribution is to create a sustainable, scalable system that improves decision-making effectiveness in the face of hybrid threats and reduces risks to national security.

Source: IEEE Access

DOI: 10.1109/access.2026.3676523