In recent years, artificial intelligence (AI) technology has been used in most if not all domains and has greatly benefited our lives. While AI can accurately extract critical features and valuable information from large amounts of data to help people complete tasks faster, there are growing concerns about the non-transparency of AI in the decision-making process. The emergence of explainable AI (XAI) has allowed humans to better understand and control AI systems, which is motivated to provide transparent explanations for the decisions made by AI. This article aims to present a comprehensive overview of recent research on XAI approaches from three well-defined taxonomies. We offer an in-depth analysis and summary of the status and prospects of XAI applications in several key areas where reliable explanations are urgently needed to avoid mistakes in decision-making. We conclude by discussing XAI’s limitations and future research directions.

The emergence of Internet protocol suites and packet-switching technologies tend to considerations of security, privacy, scalability, and reliability in layered Internet service architectures. The existing service systems allow us to access big data, but few studies focus on the fundamental security and stability in these systems, especially when they involve large-scale networks with overloaded private information. In this research, we explored the blockchain-based mechanism that aims to improve the critical features of traditional Internet services, including autonomous and decentralized processing, smart contractual enforcement of goals, and traceable trustworthiness in tamper-proof transactions. Furthermore, we provide a comprehensive review to conceptualize the blockchain-based framework to develop decentralized protocols for the extensive number of Internet services. This comprehensive survey aims to address blockchain integration to secure Internet services and identify the critical requirements of developing a decentralized trustworthy Internet service. Additionally, we present a case study of block-chain based IoT for neuro-informatics to illustrate the potential applications of blockchain architectures. Finally, we summarize the trends and challenges of blockchain architectures that benefit a multitude of disciplines across all internet service fields of interest.

Metadata management plays a critical role in data governance, resource discovery, and decision-making in the data-driven era. While traditional metadata approaches have primarily focused on organization, classification, and resource reuse, the integration of modern artificial intelligence (AI) technologies has significantly transformed these processes. This paper investigates both traditional and AI-driven metadata approaches by examining open-source solutions, commercial tools, and research initiatives. A comparative analysis of traditional and AI-driven metadata management methods is provided, highlighting existing challenges and their impact on next-generation datasets. The paper also presents an innovative AI-assisted metadata management framework designed to address these challenges. This framework leverages more advanced modern AI technologies to automate metadata generation, enhance governance, and improve the accessibility and usability of modern datasets. Finally, the paper outlines future directions for research and development, proposing opportunities to further advance metadata management in the context of AI-driven innovation and complex datasets.