Deep Learning and Optimization Approaches in Hybrid Transformer based Gated Graph Attention Capsule Network Design for Preventing Attack in Radar Target Detection and Energy Efficient Quantum Convolutional Neural Networks with Attention-Based Models for Q

Abstract

Recent advancements in artificial intelligence have enhanced radar signal processing and medical image diagnostics, particularly in adversarial and resource-constrained environments. This review examines hybrid deep learning architectures that integrate Transformers, gated graph attention networks (GAT), capsule networks, and optimization techniques. Transformers provide global contextual learning, GAT captures relational dependencies, and capsule networks preserve hierarchical features, improving robustness against noise and adversarial attacks. In parallel, energy-efficient quantum convolutional neural networks (QCNNs) with attention mechanisms support improved image quality in WSN-assisted IoT medical systems. These hybrid models significantly improve accuracy, adaptability, and computational efficiency across both domains. However, challenges such as high computational complexity, training instability, and scalability remain. Future research should focus on lightweight architectures, explainable AI, quantum optimization, and edge-based deployment for real-time and efficient applications.