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.
