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Foundations of Intelligent Systems 27th International Symposium, ISMIS 2024, Poitiers, France, June 17-19, 2024, Proceedings
Title
Foundations of Intelligent Systems [electronic resource] : 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.
ISBN
9783031627002
Edition
1st ed. 2024.
Publication
Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Physical Description
1 online resource (XIX, 316 p.) 80 illus., 61 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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.
Variant and related titles
Springer ENIN.
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Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
July 11, 2024
Series
Lecture Notes in Artificial Intelligence, 14670
Contents
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.
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