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Record Nr. |
UNINA9910578684303321 |
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Titolo |
Engineering Applications of Neural Networks : 23rd International Conference, EAAAI/EANN 2022, Chersonissos, Crete, Greece, June 17–20, 2022, Proceedings / / edited by Lazaros Iliadis, Chrisina Jayne, Anastasios Tefas, Elias Pimenidis |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
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ISBN |
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Edizione |
[1st ed. 2022.] |
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Descrizione fisica |
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1 online resource (544 pages) |
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Collana |
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Communications in Computer and Information Science, , 1865-0937 ; ; 1600 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Computer engineering |
Computer networks |
Social sciences - Data processing |
Education - Data processing |
Software engineering |
Artificial Intelligence |
Computer Engineering and Networks |
Computer Application in Social and Behavioral Sciences |
Computers and Education |
Software Engineering |
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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 bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Bio inspired Modeling / Novel Neural Architectures -- Classification / Clustering - Machine Learning -- Convolutional / Deep Learning -- Datamining / Learning / Autoencoders -- Deep Learning / Blockchain -- Machine Learning for Medical Images / Genome Classification -- Reinforcement /Adversarial / Echo State Neural Networks -- Robotics / Autonomous Vehicles, Photonic Neural Networks -- Text Classification / Natural Language. |
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Sommario/riassunto |
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This book constitutes the refereed proceedings of the 23rd International Conference on Engineering Applications of Neural |
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Networks, EANN 2022, held in Chersonisos, Crete, Greece, in June 2022. The 37 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 72 submissions. The papers are organized in topical sections on Bio inspired Modeling / Novel Neural Architectures; Classification / Clustering; Machine Learning; Convolutional / Deep Learning; Datamining / Learning / Autoencoders; Deep Learning / Blockchain; Machine Learning for Medical Images / Genome Classification; Reinforcement /Adversarial / Echo State Neural Networks; Robotics / Autonomous Vehicles, Photonic Neural Networks; Text Classification / Natural Language. |
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