Advanced Rival Combatant Identification with Hybrid Machine Learning Techniques in war field

Abstract This research shows how Hybrid Machine Learning (HML) techniques may be used in real time to identify an Army personal fighting zone or any other specified location in order to reduce safety risks via the detection of an invasion or enemies. Deep Learning (DL) techniques, such as Faster R-CNN, YOLO, and DenseNet, were used to find employees, categorize objects, and detect subtle characteristics in a variety of datasets. Testing showed that a 95% recall rate and a 90% precision rate were possible. This indicates high detection. A cleanness of 85 percent and a correctness of 80 percent were achieved in a real-world construction site application. To some things up: The recommended approach may enhance current safety management methods in conflict zones, borders, and beyond.