Foundations of Intelligent Systems 27th International Symposium, ISMIS 2024, Poitiers, France, June 17–19, 2024, Proceedings /
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 sub...
I tiakina i:
| Kaituhi rangatōpū: | |
|---|---|
| Ētahi atu kaituhi: | , , , , |
| Hōputu: | Tāhiko īPukapuka |
| Reo: | Ingarihi |
| I whakaputaina: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
|
| Putanga: | 1st ed. 2024. |
| Rangatū: | Lecture Notes in Artificial Intelligence,
14670 |
| Ngā marau: | |
| Urunga tuihono: | https://doi.org/10.1007/978-3-031-62700-2 |
| Ngā Tūtohu: |
Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
|
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-62700-2 | ||
| 003 | DE-He213 | ||
| 005 | 20251225195039.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 240617s2024 sz | s |||| 0|eng d | ||
| 020 | |a 9783031627002 |9 978-3-031-62700-2 | ||
| 024 | 7 | |a 10.1007/978-3-031-62700-2 |2 doi | |
| 050 | 4 | |a Q334-342 | |
| 050 | 4 | |a TA347.A78 | |
| 072 | 7 | |a UYQ |2 bicssc | |
| 072 | 7 | |a COM004000 |2 bisacsh | |
| 072 | 7 | |a UYQ |2 thema | |
| 082 | 0 | 4 | |a 006.3 |2 23 |
| 245 | 1 | 0 | |a Foundations of Intelligent Systems |h [electronic resource] : |b 27th International Symposium, ISMIS 2024, Poitiers, France, June 17–19, 2024, Proceedings / |c edited by Annalisa Appice, Hanane Azzag, Mohand-Said Hacid, Allel Hadjali, Zbigniew Ras. |
| 250 | |a 1st ed. 2024. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2024. | |
| 300 | |a XIX, 316 p. 80 illus., 61 illus. in color. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Lecture Notes in Artificial Intelligence, |x 2945-9141 ; |v 14670 | |
| 505 | 0 | |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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Application software. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Social sciences |x Data processing. | |
| 650 | 0 | |a Computer vision. | |
| 650 | 1 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Computer and Information Systems Applications. |
| 650 | 2 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 2 | 4 | |a Computer Application in Social and Behavioral Sciences. |
| 650 | 2 | 4 | |a Computer Vision. |
| 700 | 1 | |a Appice, Annalisa. |e editor. |0 (orcid)0000-0001-9840-844X |1 https://orcid.org/0000-0001-9840-844X |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Azzag, Hanane. |e editor. |0 (orcid)0000-0001-6876-0688 |1 https://orcid.org/0000-0001-6876-0688 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Hacid, Mohand-Said. |e editor. |0 (orcid)0000-0002-9591-9591 |1 https://orcid.org/0000-0002-9591-9591 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Hadjali, Allel. |e editor. |0 (orcid)0000-0002-4452-1647 |1 https://orcid.org/0000-0002-4452-1647 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Ras, Zbigniew. |e editor. |0 (orcid)0000-0002-8619-914X |1 https://orcid.org/0000-0002-8619-914X |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783031626999 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031627019 |
| 830 | 0 | |a Lecture Notes in Artificial Intelligence, |x 2945-9141 ; |v 14670 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-62700-2 |
| 912 | |a ZDB-2-SCS | ||
| 912 | |a ZDB-2-SXCS | ||
| 912 | |a ZDB-2-SXPS | ||
| 912 | |a ZDB-2-LNC | ||
| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||
| 950 | |a Springer Nature Proceedings Computer Science (SpringerNature-85344) | ||