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008			210623s2021 sz | s |||| 0|eng d
020			_a9783030805685
024	7		_a10.1007/978-3-030-80568-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.87 _b2021 EB
245	1	0	_aProceedings of the 22nd Engineering Applications of Neural Networks Conference : _bEANN 2021 _cedited by Lazaros Iliadis, John Macintyre, Chrisina Jayne, Elias Pimenidis.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (XLIII, 521 páginas) _b189 ilustraciones, 160 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aProceedings of the International Neural Networks Society _x2661-815X _v3
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_aAutomatic Facial Expression Neutralisation Using Generative Adversarial Network -- Creating Ensembles of Generative Adversarial Network Discriminators for One-class Classification -- A Hybrid Deep Learning Ensemble for Cyber Intrusion Detection -- Anomaly Detection by Robust Feature Reconstruction -- Deep Learning of Brain Asymmetry Images and Transfer Learning for Early Diagnosis of Dementia -- Deep learning topology-preserving EEG-based images for autism detection in infants -- Improving the Diagnosis of Breast Cancer by Combining Visual and Semantic Feature Descriptors -- Liver cancer trait detection and classification through Machine Learning on smart mobile devices.
520	3		_aThis book contains the proceedings of the 22nd EANN "Engineering Applications of Neural Networks" 2021 that comprise of research papers on both theoretical foundations and cutting-edge applications of artificial intelligence. Based on the discussed research areas, emphasis is given in advances of machine learning (ML) focusing on the following algorithms-approaches: Augmented ML, autoencoders, adversarial neural networks, blockchain-adaptive methods, convolutional neural networks, deep learning, ensemble methods, learning-federated learning, neural networks, recurrent - long short-term memory. The application domains are related to: Anomaly detection, bio-medical AI, cyber-security, data fusion, e-learning, emotion recognition, environment, hyperspectral imaging, fraud detection, image analysis, inverse kinematics, machine vision, natural language, recommendation systems, robotics, sentiment analysis, simulation, stock market prediction.
988			_aSpringer_Robotics_2021
650		7	_2embne _9678664 _aRedes neuronales artificiales
700	1		_aIliadis, Lazaros _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _999966
700	1		_aMacintyre, John _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9682685
700	1		_aJayne, Chrisina _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _999965
700	1		_aPimenidis, Elias _eeditor literario _0(orcid)0000-0003-3593-8640 _1https://orcid.org/0000-0003-3593-8640 _4edt _4http://id.loc.gov/vocabulary/relators/edt _9682686
776	0	8	_iPrinted edition: _z9783030805678
776	0	8	_iPrinted edition: _z9783030805692
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-80568-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE