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Homeland Security

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Research Frontiers in Deep Learning for Radiological Threat Detection

Application of convolutional neural networks to identify radioactive material signatures in X-ray imagery and radiation detector data at ports and airports.

Neural Architectures for Sparse Radiation Anomaly Recognition

Adversarial Robustness in Radiological Signature Classification

Multi-Modal Fusion for Concealed Isotope Detection

Domain Adaptation Across Heterogeneous Detector Architectures

Explainability in High-Stakes Radiological Decision Systems

Real-Time Isotope Speciation Under Signal Degradation

Transfer Learning from Simulated to Field Radiation Spectra

Temporal Pattern Recognition in Dynamic Threat Scenarios

Few-Shot Learning for Emerging Radionuclide Threats

Uncertainty Quantification in Mobile Radiation Detection Networks

All Homeland Security PhD categories