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Zero-Trust Architecture: Principles, Implementation, and Case Studies

Chen Wei, Sarah Mitchell

In: Cybersecurity in the Digital Age

Zero-trust security architectures represent a paradigm shift away from perimeter-based security models. This chapter provides a comprehensive guide to zero-trust principles, microsegmentation, identity verification, and continuous authorization. Real-world implementation case studies from enterprise and critical infrastructure deployments illustrate practical challenges and solutions.

Post-Quantum Cryptography: NIST Standardization and Deployment Considerations

Chen Wei

In: Cybersecurity in the Digital Age

Quantum computers threaten current public-key cryptographic standards. This chapter surveys the NIST post-quantum cryptography standardization process, focusing on CRYSTALS-Kyber (key encapsulation) and CRYSTALS-Dilithium (digital signatures). We analyze performance trade-offs, hybrid deployment strategies, and migration planning for organizations transitioning to quantum-resistant algorithms.

Cybersecurity in the Digital Age

Threats, Defenses, and Future Challenges in Information Security

As digital transformation accelerates, cybersecurity has become a critical concern for individuals, organizations, and governments. This volume examines the current threat landscape, advanced persistent threats, zero-trust architectures, cryptographic protocols, and emerging challenges posed by quantum computing and IoT security.

Vision Transformers for Medical Image Segmentation: A Comprehensive Survey

Chen Wei, Xia Liu

In: Machine Learning and Artificial Intelligence: Trends and Applications

This survey provides a thorough examination of Vision Transformer (ViT) architectures applied to medical image segmentation tasks. We review over 120 recent publications covering fundus photography, CT scan analysis, MRI brain segmentation, and histopathology slide processing. We identify key architectural innovations, benchmark datasets, and open research challenges.

Large Language Models in Code Generation: Capabilities, Limits, and Safety

Chen Wei

In: Machine Learning and Artificial Intelligence: Trends and Applications

We systematically evaluate large language models (LLMs) for automated code generation across multiple programming languages and task types. Our study assesses functional correctness, security vulnerabilities, and alignment with developer intent using a novel benchmark suite. We discuss prompt engineering strategies that improve generation quality and safety filtering mechanisms.

Federated Learning for Privacy-Preserving Healthcare Analytics

Chen Wei, James Okonkwo

In: Machine Learning and Artificial Intelligence: Trends and Applications

Federated learning enables collaborative model training across distributed healthcare institutions without sharing patient data. This chapter presents a privacy-preserving federated framework with differential privacy guarantees, secure aggregation protocols, and personalization mechanisms for non-IID healthcare datasets. Experiments on electronic health records demonstrate superior performance over baseline approaches.

Machine Learning and Artificial Intelligence: Trends and Applications

Deep Learning, Generative Models, and Real-World Deployments

Eds. Marcus Lindqvist, Chen Wei

This volume provides an in-depth exploration of the rapidly evolving landscape of machine learning and AI. Chapters cover transformer architectures, large language models, computer vision, reinforcement learning, and responsible AI practices. Written by leading researchers from top institutions worldwide, the book is essential reading for both researchers and practitioners.

Cloud Computing, Edge Intelligence, and Distributed Systems

Proceedings of CCEDIS 2022 International Conference

This proceedings volume collects peer-reviewed research from the CCEDIS 2022 conference. Papers address scalable cloud architectures, fog computing, containerization technologies, microservices, distributed databases, and consensus algorithms in blockchain systems.