Advances in Real-Time and Autonomous Systems

Proceedings of the 16th International Conference on Autonomous Systems
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The importance of being fuzzy.- Artificial emotion: The research on making machines more human-like.- Construction and comparison of linkage-graph-based representations with co-occurrence graphs and word2Vec embeddings: A case study on English Wikipedia articles - Efficient generation of sparse distributed representations (SDRS) with singular value decomposition (SVD).- Word embedding through a spring-force system.- Dual graph representation for semantic extraction.- Academic paper recommendation using co-occurrence graphs.- Sentiment analysis in public transport: A comparative study of machine learning and deep learning models.- The GraphLearner as a high order Markov chain simulator.- Edge decisions and N-Gram midpoints with the GraphLearner.- Empirical comparison of different bloom filter variants.- Learn, predict and generate note sequences.- Empowering non-experts with interactive graph visualization in generative AI: The case of GraphLearner.- The psychological background of the emotional machine.- Designing an emotion-based motivation model for adaptive AI agents.- Personalised cloud-intelligence: AI-driven decision making for small businesses.- Mixed-order spatio-temporal representation learning for traffic prediction.- The role of entropy-based features in classifying tor traffic using machine learning.- An adaptive metric-based method for privacy-preserving sharing of anonymized urban mobility data.- Explainable prompt-based image generation: developing transparent generative models.- Enhancing mobile network capacity planning with emerging technologies: A system dynamics and machine learning-based approach.- Counterfeit Thai banknote detection using deep learning.- StepIn: A context-aware decentralized social networking system.- Conceptual design of intelligent services in decentralized social networks.- Enhancing web crawling efficiency with adaptive scheduling algorithms and chatgpt integration.

Produktdetails
  • Erscheinungsdatum: 09.10.2025
  • Autor/Autorin: Herwig Unger
  • Reihe: Springer Nature Proceedings excluding Computer Science
  • Format: E-Book
  • Dateiformat: PDF
  • Kopierschutz: Wasserzeichen
  • Dateigröße: 24.8 MB
  • Verlag: SPRINGER
  • Sprache: Englisch
  • Umfang: 404 Seiten
  • ISBN: 9783031986970
  • Lieferung: Sofort per Download
  • Hinweis: Sofort per Download lieferbar. Kein physischer Versand.
  • Kompatibilität: Lesbar auf Geräten und Apps mit PDF-Unterstützung.
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Advances in Natural Language Processing and Information Retrieval

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This book collects peer-reviewed and presented papers from 2025 9th International Conference on Natural Language Processing and Information Retrieval (NLPIR 2025), held in Kyushu University, Fukuoka, Japan during December 14–16, 2025. Nowadays, advances in computer engineering made it possible that humans may interact with the machines in their natural language either in the written or the spoken manner. At the same time, the amount of (textual) data available in big data bases as well as the World Wide Web is growing in a rapid manner. Both developments setup new challenges for the development of innovative algorithms to recognise speech, to categorise and classify not only textual information in short times as well as to extract knowledge and wisdom from a huge pile of raw, unevaluated data.  Topics discussed in this book include Syntactic Analysis, Language Representation and Machine Translation, Multimodal NLP Recognition, Semantic Analysis and Modelling, Sentiment Analysis and Corpus Construction, Generative Text and Text Classification, Speech Recognition Models and Detection Techniques, Context-Aware Large Language Models and Parameter Adaptation, Language Representation and Information Retrieval in Multimodal Information Systems, AI-Based Generative Dialogue Systems and Intelligent Question Answering Systems, Text Mining and Knowledge Extraction Based on Generative Frameworks, Mechanisms and Applications of Artificial Intelligence in Natural Language Tasks, Cross-Document Information Extraction and Information Detection Based on Pre-trained Language Models. 

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