Proceedings of the 22nd Engineering Applications of Neural Networks Conference: EANN 2021

by Lazaros Iliadis (Editor), John Macintyre (Editor), Chrisina Jayne (Editor), Elias Pimenidis (Editor)

Overview

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.

Product Details

ISBN-13:	9783030805685
Publisher:	Springer-Verlag New York, LLC
Publication date:	06/23/2021
Series:	Proceedings of the International Neural Networks Society , #3
Sold by:	Barnes & Noble
Format:	eBook
File size:	70 MB
Note:	This product may take a few minutes to download.

Table of Contents

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.