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▼aFoundations of Intelligent Systems▼h[electronic resource] :▼b27th International Symposium, ISMIS 2024, Poitiers, France, June 17–19, 2024, Proceedings /▼cedited by Annalisa Appice, Hanane Azzag, Mohand-Said Hacid, Allel Hadjali, Zbigniew Ras. |
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▼a1st ed. 2024. |
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▼aCham :▼bSpringer Nature Switzerland :▼bImprint: Springer,▼c2024. |
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▼aXIX, 316 p. 80 illus., 61 illus. in color.▼bonline resource. |
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▼atext▼btxt▼2rdacontent |
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▼acomputer▼bc▼2rdamedia |
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▼aonline resource▼bcr▼2rdacarrier |
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▼atext file▼bPDF▼2rda |
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▼aLecture Notes in Artificial Intelligence,▼x2945-9141 ;▼v14670 |
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▼a -- 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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▼aThis 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. |
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▼aArtificial intelligence. |
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▼aApplication software. |
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▼aData mining. |
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▼aSocial sciences▼xData processing. |
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▼aComputer vision. |
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▼aArtificial Intelligence. |
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▼aComputer and Information Systems Applications. |
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▼aData Mining and Knowledge Discovery. |
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▼aComputer Application in Social and Behavioral Sciences. |
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▼aComputer Vision. |
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▼aAppice, Annalisa.▼eeditor.▼0(orcid)0000-0001-9840-844X▼1https://orcid.org/0000-0001-9840-844X▼4edt▼4http://id.loc.gov/vocabulary/relators/edt |
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▼aAzzag, Hanane.▼eeditor.▼0(orcid)0000-0001-6876-0688▼1https://orcid.org/0000-0001-6876-0688▼4edt▼4http://id.loc.gov/vocabulary/relators/edt |
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▼aHacid, Mohand-Said.▼eeditor.▼0(orcid)0000-0002-9591-9591▼1https://orcid.org/0000-0002-9591-9591▼4edt▼4http://id.loc.gov/vocabulary/relators/edt |
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▼aHadjali, Allel.▼eeditor.▼0(orcid)0000-0002-4452-1647▼1https://orcid.org/0000-0002-4452-1647▼4edt▼4http://id.loc.gov/vocabulary/relators/edt |
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▼aRas, Zbigniew.▼eeditor.▼0(orcid)0000-0002-8619-914X▼1https://orcid.org/0000-0002-8619-914X▼4edt▼4http://id.loc.gov/vocabulary/relators/edt |
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▼aSpringerLink (Online service) |
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▼tSpringer Nature eBook |
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▼iPrinted edition:▼z9783031626999 |
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▼iPrinted edition:▼z9783031627019 |
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▼aLecture Notes in Artificial Intelligence,▼x2945-9141 ;▼v14670 |
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▼uhttps://doi.org/10.1007/978-3-031-62700-2 |
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▼aComputer Science (SpringerNature-11645) |
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▼aComputer Science (R0) (SpringerNature-43710) |