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<p>This book serves as both a cutting-edge reference and a practical guide to building AI systems that are transparent, trustworthy, and tuned for real-world impact, featuring contributors from three continents and backed by leading institutions.</p><p>Unlock the next wave of graph-based artificial intelligence, fuzzy logic, and human-centric machine learning with this authoritative Springer proceedings book. Twenty-four rigorously peer-reviewed chapters—spanning semantic similarity in Wikipedia, sparse distributed representations, explainable image generation, privacy-preserving mobility analytics, sentiment mining in public transport, counterfeit-banknote detection, 5G network capacity planning, and mixed-order traffic prediction—provide a panoramic view of state-of-the-art research that turns theory into deployable solutions.</p><p>Readers gain step-by-step methodologies for building restricted Boltzmann machines enhanced with fuzziness, dual-graph semantic extractors, Bloom-filter variants, and the versatile GraphLearner simulator. Each contribution includes reproducible workflows, comparative baselines, and publicly available code or datasets—accelerating adoption in academia and industry alike.</p><p>Highlights include a blueprint for emotion-aware AI agents, a cloud-intelligence framework that empowers SMEs with decision support, and an adaptive metric for privacy-preserving urban-mobility sharing that balances usability and anonymity.</p>

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Advances in Real-Time and Autonomous Systems

  • 会议论文集

  • 发表日期：

    2026年

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摘要

This book serves as both a cutting-edge reference and a practical guide to building AI systems that are transparent, trustworthy, and tuned for real-world impact, featuring contributors from three continents and backed by leading institutions.

Unlock the next wave of graph-based artificial intelligence, fuzzy logic, and human-centric machine learning with this authoritative Springer proceedings book. Twenty-four rigorously peer-reviewed chapters—spanning semantic similarity in Wikipedia, sparse distributed representations, explainable image generation, privacy-preserving mobility analytics, sentiment mining in public transport, counterfeit-banknote detection, 5G network capacity planning, and mixed-order traffic prediction—provide a panoramic view of state-of-the-art research that turns theory into deployable solutions.

Readers gain step-by-step methodologies for building restricted Boltzmann machines enhanced with fuzziness, dual-graph semantic extractors, Bloom-filter variants, and the versatile GraphLearner simulator. Each contribution includes reproducible workflows, comparative baselines, and publicly available code or datasets—accelerating adoption in academia and industry alike.

Highlights include a blueprint for emotion-aware AI agents, a cloud-intelligence framework that empowers SMEs with decision support, and an adaptive metric for privacy-preserving urban-mobility sharing that balances usability and anonymity.