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Record Nr. |
UNINA9910729895703321 |
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Autore |
Iliadis Lazaros |
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Titolo |
Engineering Applications of Neural Networks : 24th International Conference, EAAAI/EANN 2023, León, Spain, June 14–17, 2023, Proceedings / / edited by Lazaros Iliadis, Ilias Maglogiannis, Serafin Alonso, Chrisina Jayne, Elias Pimenidis |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (636 pages) |
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Collana |
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Communications in Computer and Information Science, , 1865-0937 ; ; 1826 |
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Altri autori (Persone) |
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MaglogiannisIlias |
Alonso NavarroSerafín |
JayneChrisina |
PimenidisElias |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Computer engineering |
Computer networks |
Software engineering |
Social sciences - Data processing |
Education - Data processing |
Artificial Intelligence |
Computer Engineering and Networks |
Software Engineering |
Computer Application in Social and Behavioral Sciences |
Computers and Education |
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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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Artificial Intelligence - Computational Methods - Ethology -- Classification - Filtering - Genetic Algorithms -- Complex Dynamic Networks' Optimization/ Graph Neural Networks -- Convolutional Neural Networks / Spiking Neural Networks -- Deep Learning Modeling -- Deep/Machine Learning in Engineering -- LEARNING (Reinforcemet - Federated - Adversarial - Transfer) -- Natural Language - |
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Sommario/riassunto |
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This book constitutes the refereed proceedings of the 24th International Conference on Engineering Applications of Neural Networks, EANN 2023, held in León, Spain, in June 2023. The 41 revised full papers and 8 revised short papers presented were carefully reviewed and selected from 125 submissions. The papers are organized in topical sections on artificial intelligence - computational methods - ethology; classification - filtering - genetic algorithms; complex dynamic networks' optimization/ graph neural networks; convolutional neural networks/spiking neural networks; deep learning modeling; deep/machine learning in engineering; LEARNING (reinforcemet - federated - adversarial - transfer); natural language - recommendation systems. |
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