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Proceedings of the 22nd Engineering Applications of Neural Networks Conference

EANN 2021

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Lazaros Iliadis

    1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

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  2. John Macintyre

    1. Faculty of Applied Sciences, University of Sunderland, Sunderland, UK

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  3. Chrisina Jayne

    1. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

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  4. Elias Pimenidis

    1. University of the West of England, Bristol, UK

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  • Recent research in Engineering Applications of Neural Networks
  • Proceedings of the 22nd EANN (Engineering Applications of Neural Networks) 2021 conference held at Porto Carras Grand Resort, Halkidiki, Greece, from 25 – 27 June, 2021
  • Written by experts in the field

Part of the book series: Proceedings of the International Neural Networks Society (INNS, volume 3)

Included in the following conference series:

Conference proceedings info: EANN 2021.

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Table of contents (41 papers)

  1. Front Matter

    Pages i-xliii
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  2. Adversarial Neural Networks - Classification

    1. Front Matter

      Pages 1-1
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    2. Automatic Facial Expression Neutralisation Using Generative Adversarial Network

      • Grina Wiem, Douik Ali
      Pages 3-12

    3. Creating Ensembles of Generative Adversarial Network Discriminators for One-Class Classification

      • Mihai Ermaliuc, Daniel Stamate, George D. Magoulas, Ida Pu
      Pages 13-23

  3. Anomaly Detection

    1. Front Matter

      Pages 25-25
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    2. A Hybrid Deep Learning Ensemble for Cyber Intrusion Detection

      • Anastasios Panagiotis Psathas, Lazaros Iliadis, Antonios Papaleonidas, Dimitris Bountas
      Pages 27-41

    3. Anomaly Detection by Robust Feature Reconstruction

      • Ron Triepels
      Pages 42-53

  4. Classification - Medical Applications

    1. Front Matter

      Pages 55-55
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    2. Deep Learning of Brain Asymmetry Images and Transfer Learning for Early Diagnosis of Dementia

      • Nitsa J. Herzog, George D. Magoulas
      Pages 57-70

    3. Deep Learning Topology–Preserving EEG–Based Images for Autism Detection in Infants

      • Cosmin Stamate, George D. Magoulas, Michael S. C. Thomas, the BASIS Team
      Pages 71-82

    4. Improving the Diagnosis of Breast Cancer by Combining Visual and Semantic Feature Descriptors

      • George Apostolopoulos, Athanasios Koutras, Dionysios Anyfantis, Ioanna Christoyianni, Evangelos Dermatas
      Pages 83-94

    5. Liver Cancer Trait Detection and Classification Through Machine Learning on Smart Mobile Devices

      • Olympia Giannou, Anastasios D. Giannou, Dimitra E. Zazara, Dörte Kleinschmidt, Tobias Mummert, Björn Ole Stüben et al.
      Pages 95-108

    6. Using WOA with Feed Forward Neural Network in Prediction of Subcutaneous Glucose Concentration for Type-1 Diabetic Patients

      • Fayrouz Allam
      Pages 109-118

  5. Deep Learning - Convolutional Neural Networks Modeling

    1. Front Matter

      Pages 119-119
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    2. A Novel CNN-LSTM Hybrid Architecture for the Recognition of Human Activities

      • Sofia Stylianou-Nikolaidou, Ioannis Vernikos, Eirini Mathe, Evaggelos Spyrou, Phivos Mylonas
      Pages 121-132

    3. An Artificial Intelligence System for Endotracheal Intubation Confirmation

      • Dror Lederman
      Pages 133-140

    4. Data Fusion for Deep Learning on Transport Mode Detection: A Case Study

      • Hugues Moreau, Andrea Vassilev, Liming Chen
      Pages 141-152

    5. Deep Learning for Water Quality Classification in Water Distribution Networks

      • Essa Q. Shahra, Wenyan Wu, Shadi Basurra, Stamatia Rizou
      Pages 153-164

    6. Deep Learning Modeling of Groundwater Pollution Sources

      • Yiannis N. Kontos, Theodosios Kassandros, Konstantinos L. Katsifarakis, Kostas Karatzas
      Pages 165-177

    7. Deep Neural Networks for Indoor Geomagnetic Field Fingerprinting with Regression Approach

      • Mahdi Abid, Grégoire Lefebvre
      Pages 178-189
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  1. Proceedings of the 22nd Engineering Applications of Neural Networks Conference

Keywords

About this book

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.


Editors and Affiliations

  • School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  • Faculty of Applied Sciences, University of Sunderland, Sunderland, UK

    John Macintyre

  • School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  • University of the West of England, Bristol, UK

    Elias Pimenidis

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