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Record Nr. |
UNINA9910983050303321 |
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Autore |
Pedreschi Dino |
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Titolo |
Discovery Science : 27th International Conference, DS 2024, Pisa, Italy, October 14–16, 2024, Proceedings, Part I / / edited by Dino Pedreschi, Anna Monreale, Riccardo Guidotti, Roberto Pellungrini, Francesca Naretto |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (642 pages) |
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Collana |
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Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 15243 |
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Altri autori (Persone) |
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MonrealeAnna |
GuidottiRiccardo |
PellungriniRoberto |
NarettoFrancesca |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Education - Data processing |
Data mining |
Social sciences - Data processing |
Image processing - Digital techniques |
Computer vision |
Artificial Intelligence |
Computers and Education |
Data Mining and Knowledge Discovery |
Computer Application in Social and Behavioral Sciences |
Computer Imaging, Vision, Pattern Recognition and Graphics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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LLM, Text Analytics, and Ethical Aspects of AI -- Natural Language Processing, Sequential Data and Science Discovery -- Data-Driven Science Discovery Methodologies; Graph Neural Network, Graph Theory -- Unsupervised Learning and Regression. |
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Sommario/riassunto |
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The two-volume set LNAI 15243 + 15244 constitutes the proceedings |
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of the 27th International Conference on Discovery Science, DS 2024, which took place in Pisa, Italy, during October 14-16, 2024. The 53 full papers presented in the proceedings were carefully reviewed and selected from 121 submissions. They were organized in topical sections as follows: Part I: LLM, Text Analytics, and Ethical Aspects of AI; Natural Language Processing, Sequential Data and Science Discovery; Data-Driven Science Discovery Methodologies; Graph Neural Network, Graph Theory, Unsupervised Learning and Regression; Part II: Tree-Based Models and Causal Discovery; Security and Anomaly Detection; Computer Vision and Explainable AI; Classification Models; SoBigData++: City for Citizens and Explainable AI; SoBigData++: Societal Debates and Misinformation Analysis. . |
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