UID:
almahu_9949083912902882
Format:
XLIII, 521 p. 189 illus., 160 illus. in color.
,
online resource.
Edition:
1st ed. 2021.
ISBN:
9783030805685
Series Statement:
Proceedings of the International Neural Networks Society, 3
Content:
This 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.
Note:
Automatic 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.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783030805678
Additional Edition:
Printed edition: ISBN 9783030805692
Language:
English
DOI:
10.1007/978-3-030-80568-5
URL:
https://doi.org/10.1007/978-3-030-80568-5
URL:
Volltext
(URL des Erstveröffentlichers)
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