Advanced Rival Combatant Identification with Hybrid Machine Learning Techniques in War Field
- Authors: Charanarur Panem1, Srinivasa Rao Gundu2, S. Satheesh3, Kashinath K. Chandelkar4, J. Vijaylaxmi5
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View Affiliations Hide Affiliations1 Department of Cyber Security and Digital Forensics, National Forensic Sciences University, Tripura Campus, Tripura, India 2 Department of Digital Forensics, School of Sciences, Malla Reddy University, Dhulapally, Hyderabad, Telangana, India 3 Malineni Lakshmaiah Women's Engineering College, Guntur, Andhra Pradesh, India 4 School of Cyber Security and Digital Forensic, National Forensic Sciences University, Goa Campus, Goa, India 5 PVKK Degree & PG College, Anantapur, Andhra Pradesh, India
- Source: Green Industrial Applications of Artificial Intelligence and Internet of Things , pp 1-15
- Publication Date: July 2024
- Language: English
Advanced Rival Combatant Identification with Hybrid Machine Learning Techniques in War Field, Page 1 of 1
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This research shows how Hybrid Machine Learning (HML) techniques may be used in real-time to identify an Army's 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.
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