Buch, Englisch, 440 Seiten, Format (B × H): 155 mm x 235 mm
Proceedings of the 2026 Intelligent Systems Conference (IntelliSys), Volume 3
Buch, Englisch, 440 Seiten, Format (B × H): 155 mm x 235 mm
Reihe: Lecture Notes in Networks and Systems
ISBN: 978-3-032-32728-4
Verlag: Springer
This book presents proceedings of the 12th Intelligent Systems Conference 2026 (IntelliSys 2026), held on September 3–4, 2026, in Amsterdam, The Netherlands, and contains selected peer-reviewed papers presenting recent advances, innovative methodologies, and practical applications in the field of intelligent systems and related technologies.
The conference served as an international platform for researchers, academicians, scientists, and industry professionals to exchange ideas, share research findings, and discuss current challenges and future directions in intelligent computing. The accepted contributions cover a wide range of topics, including Artificial Intelligence, Machine Learning, Computer Vision, Internet of Things (IoT), Cyber-Physical Systems, Data Science, and Intelligent Computing. All submissions underwent a rigorous double-blind peer-review process conducted by the Technical Program Committee to ensure high standards of quality, originality, technical merit, and relevance to the conference themes.
These proceedings reflect the multidisciplinary nature and continuing growth of intelligent systems research.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Privacy-Preserving Federated Learning for Conversational AI: A DP-Adapter Framework.- Investigating the Fundamental Limit: A Feasibility Study of Hybrid-Neural Archival.- From Detection to Decision: A RAG-LLM Clinical Digital Assistant for Intracranial Hemorrhage Using a Lightweight CT Model.- A Multi-Model Comparison of RAG Methods for Grant Requirement Extraction via SolicitationWizard.- Executable Intent: Making LLM Workflows Enterprise-Ready with Domain-Specific Languages.- Autonomous Issue Resolver: Towards Zero-Touch Code Maintenance.- Knowledge-Graph Based RAG System Evaluation Framework.- Finding the Efficiency Sweet Spot in Compressed Large Language Models Through Resource and Cost Analysis.
