Lazaros Iliadis & John Macintyre 
Proceedings of the 22nd Engineering Applications of Neural Networks Conference [PDF ebook] 
EANN 2021

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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.

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Table des matières

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.

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Langue Anglais ● Format PDF ● Pages 521 ● ISBN 9783030805685 ● Taille du fichier 48.7 MB ● Éditeur Lazaros Iliadis & John Macintyre ● Maison d’édition Springer International Publishing ● Lieu Cham ● Pays CH ● Publié 2021 ● Téléchargeable 24 mois ● Devise EUR ● ID 7876804 ● Protection contre la copie DRM sociale

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