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AI-Driven Tactical Military Asset Recognition for Enhanced Situational Awareness and Command Decision Support
Contemporary defense systems are increasingly dependent on intelligent technologies to enhance surveillance, reconnaissance, and strategic decision-making capabilities. The rapid growth of visual data generated from satellites and unmanned aerial platforms has made manual image analysis and traditional rule-based approaches inefficient, labor-intensive, and unsuitable for large-scale processing. To overcome these limitations, the proposed system employs advanced Machine Learning and Deep Learning techniques to automate the classification of military imagery. The framework integrates multiple algorithms, including Perceptron, Decision Tree Classifier (DTC), Deep Neural Networks (DNN), and a Hybrid Convolutional Recurrent Model (CRM). The CRM combines Convolutional Neural Networks (CNN) for robust spatial feature extraction with Long Short-Term Memory (LSTM) networks to model complex sequential relationships within the extracted features. These models are trained on well-structured military image datasets to accurately categorize assets into predefined strategic classes. Experimental results demonstrate that DNN and CRM models deliver improved accuracy and reliability, depending on dataset characteristics and training conditions. Additionally, the system is equipped with a user-friendly graphical interface that allows seamless dataset uploading, model training, prediction generation, and visualization of performance metrics. By consolidating multiple models into a single platform and identifying the most effective one for deployment, the proposed solution significantly improves the efficiency, accuracy, and robustness of automated military image analysis, thereby enhancing intelligence operations and supporting informed decision-making.
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