Article
Intelligent Military Decision Support Using Machine Learning and DataDriven Tactical Analysis
Technological advancements have fundamentally transformed modern defense operations, particularly within surveillance, reconnaissance, and battlefield decision support systems. While military environments generate vast volumes of visual data from satellites and unmanned aerial vehicles (UAVs), conventional manual interpretation remains resource-intensive and prone to human error, hindering real-time operational responses. To address these systemic bottlenecks, this research introduces an intelligent, high-assurance decision support framework for the automated classification of tactical military imagery. The proposed system transitions beyond traditional rule-based software by integrating a suite of soft computing models, including Perceptron, Decision Tree Classifiers (DTC), and Deep Neural Networks (DNN). Central to the framework is a novel Hybrid Convolutional Recurrent Model (CRM), which synergizes Convolutional Neural Networks (CNN) for spatial feature extraction with Long Short-Term Memory (LSTM) networks to capture essential temporal dependencies in dynamic battlefield scenarios. The architecture is encapsulated within a modular graphical interface designed for streamlined data ingestion, model training, and performance visualization. Experimental validation demonstrates that the integrated CRM significantly enhances processing speed and classification reliability, providing a scalable and robust technological solution for modern military intelligence and tactical decision-making.
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