Foundations of Intelligent Systems : 27th International Symposium, ISMIS 2024, Poitiers, France, June 17–19, 2024, Proceedings / / edited by Annalisa Appice, Hanane Azzag, Mohand-Said Hacid, Allel Hadjali, Zbigniew Ras

• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
• Edizione: 1st ed. 2024.
• Descrizione fisica: 1 online resource (XIX, 316 p. 80 illus., 61 illus. in color.)
• Disciplina: 006.3
• Soggetto topico: Artificial intelligence; Application software; Data mining; Social sciences - Data processing; Computer vision; Artificial Intelligence; Computer and Information Systems Applications; Data Mining and Knowledge Discovery; Computer Application in Social and Behavioral Sciences; Computer Vision
• Persona (resp. second.): AppiceAnnalisa
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: -- Classification and Clustering. -- Improving the robustness to color perturbations of classification and regression models in the visual evaluation of fruits and vegetables. -- Clustering Under Radius Constraints Using Minimum Dominating Sets. -- Learning Typicality Inclusions in a Probabilistic Description Logic for Concept Combination. -- Neural Network and Natural Language Processing. -- LLMental Classification of mental disorders with large language models. -- CSEPrompts A Benchmark of Introductory Computer Science Prompts. -- Semantically-Informed Domain Adaptation for Named Entity Recognition. -- Token Pruning by Dimensionality Reduction Methods on TCT Colbert for Reranking. -- AI Tools and Models. -- Exploiting microRNA expression data for the diagnosis of disease conditions and the discovery of novel biomarkers. -- HERSE: Handling and Enhancing RDF Summarization through blank node Elimination. -- Rough Sets For a Neuromorphic CMOS System. -- Neural Network and Data Mining. -- Erasing the Shadow Sanitization of Images with Malicious Payloads using Deep Autoencoders. -- Digilog Enhancing Website Embedding on Local Governments - A Comparative Analysis. -- A Stream Data Mining Approach to Handle Concept Drifts in Process Discovery. -- Explainability in AI. -- Enhancing temporal Transformers for financial time series via local surrogate interpretability. -- Explaining commonalities of clusters of RDF resources in natural language. -- Shapley-Based Data Valuation Method for the Machine Learning Data Markets (MLDM). -- Industry Session. -- ScoredKNN: An Efficient KNN Recommender based on Dimensionality Reduction for Big Data. -- Siamese Networks for Unsupervised Failure Detection in Smart Industry. -- Adaptive Forecasting of Extreme Electricity Load. -- Explaining Voltage Control Decisions: A Scenario-Based Approach in Deep Reinforcement Learning. -- Knowledge Graphs for Data Integration in Retail. -- Learning with Complex Data. -- Bayesian Approach for Parameter Estimation in Vehicle Lateral Dynamics. -- Assessing Distance Measures for Change Point Detection in Continual Learning Scenarios. -- SPLindex A Spatial Polygon Learned Index . -- Recommendation Systems and Prediction. -- Action Rules Discovery Leveraging Attributes Correlation Based Vertical Partitioning. -- HalpernSGD A Halpern-inspired Optimizer for Accelerated Neural Network Convergence and Reduced Carbon Footprint. -- Integrating Predictive Process Monitoring Techniques in Smart Agriculture.
• Sommario/riassunto: This book constitutes the proceedings of the 27th International Symposium on Methodologies for Intelligent Systems, ISMIS 2024, held in Poitiers, France, in June 2024. The 18 full papers, 6 short papers and 5 industrial papers presented in this volume were carefully reviewed and selected from 46 submissions. The papers are organized in the following topical sections: Classification and Clustering; Neural Network and Natural Language Processing; AI tools and Models; Neural Network and Data Mining; Explainability in AI; Industry Session; Learning with Complex Data; Recommendation Systems and Prediction.
• Titolo autorizzato: Foundations of Intelligent Systems
• ISBN: 3-031-62700-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910865244503321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Artificial Intelligence, . 2945-9141 ; ; 14670