Adaptive Hybrid Deep Learning with Recursive Feature Elimination for Physical Violence Detection

Physical violence among students remains a persistent issue that often goes undetected, especially in school environments without intelligent real-time monitoring systems. Such incidents pose serious risks to student safety and hinder the creation of a secure learning atmosphere. This study aims to develop an adaptive visual-based system for detecting physical violence in educational settings using a deep learning approach. A hybrid architecture was designed by integrating VGG19 for spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) for temporal sequence analysis. To enhance model interpretability and reduce redundancy, Recursive Feature Elimination (RFE) was employed to eliminate irrelevant features and improve overall learning efficiency. The proposed system effectively captures both spatial and temporal cues from classroom surveillance videos, enabling more accurate classification of violent and non-violent behaviors. The model was trained and tested on benchmark datasets containing diverse video samples and achieved an accuracy of 92.4%, outperforming standalone CNN and LSTM models. The integration of RFE contributed to a more compact and computationally efficient framework. This study demonstrates the potential of hybrid deep learning and feature optimization for real-time violence detection, contributing to the advancement of visual intelligence and Educational AI for safer, data-driven learning environments.