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Advancing the frontiers of technology through rigorous research, peer-reviewed publications, and collaborative academic endeavors that shape the future of computing.
This paper presents a novel approach to analyzing cryptographic protocols using quantum-enhanced machine learning algorithms, demonstrating significant improvements in detection accuracy and computational efficiency.
We propose a federated learning framework optimized for edge computing environments, addressing privacy concerns while maintaining model performance across distributed nodes.
A comprehensive framework for managing digital identities in IoT ecosystems using blockchain technology, ensuring security, privacy, and scalability.
An automated neural architecture search methodology specifically designed for real-time computer vision applications with resource constraints.
A comprehensive survey of adversarial robustness techniques in deep learning, analyzing current methods and proposing future research directions.
This work addresses the environmental impact of AI by developing energy-efficient deep learning models without compromising performance.
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