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Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference

Proceedings of the EANN 2020

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Lazaros Iliadis

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

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  2. Plamen Parvanov Angelov

    1. Lancaster University, Lancaster, 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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  • Is dedicated to advancing the state of the art in AI algorithms and their applications
  • Serves as a source of inspiration for colleagues from various scientific domains
  • Presents new algorithms and new hybrid approaches, offering significant guidance for all AI researchers
  • Offers extensive information on both theoretical aspects and application areas
  • Covers areas such as convolutional neural networks, deep learning, and LSTM in robotics/machine vision/engineering/image processing/medical systems/the environment
  • Describes state-of-the-art hybrid systems, the algorithmic foundations of artificial neural networks, and machine learning / meta learning as applied to neurobiological modeling/optimization

Part of the book series: Proceedings of the International Neural Networks Society (INNS, volume 2)

Included in the following conference series:

Conference proceedings info: EANN 2020.

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Table of contents (48 papers)

  1. Front Matter

    Pages i-xxvii
    Download chapter PDF

  2. Classification/Machine Learning

    1. Front Matter

      Pages 1-1
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    2. A Compact Sequence Encoding Scheme for Online Human Activity Recognition in HRI Applications

      • Georgios Tsatiris, Kostas Karpouzis, Stefanos Kollias
      Pages 3-14

    3. Classification of Coseismic Landslides Using Fuzzy and Machine Learning Techniques

      • Anastasios Panagiotis Psathas, Antonios Papaleonidas, George Papathanassiou, Sotiris Valkaniotis, Lazaros Iliadis
      Pages 15-31

    4. Evaluating the Transferability of Personalised Exercise Recognition Models

      • Anjana Wijekoon, Nirmalie Wiratunga
      Pages 32-44

  3. Convolutional Neural Networks in Robotics/Computer Vision

    1. Front Matter

      Pages 45-45
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    2. Deep Learning-Based Computer Vision Application with Multiple Built-In Data Science-Oriented Capabilities

      • Sorin Liviu Jurj, Flavius Opritoiu, Mircea Vladutiu
      Pages 47-69

    3. Visual Movement Prediction for Stable Grasp Point Detection

      • Constanze Schwan, Wolfram Schenck
      Pages 70-81

  4. Machine Learning in Engineering and Environment

    1. Front Matter

      Pages 83-83
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    2. Accomplished Reliability Level for Seismic Structural Damage Prediction Using Artificial Neural Networks

      • Magdalini Tyrtaiou, Antonios Papaleonidas, Anaxagoras Elenas, Lazaros Iliadis
      Pages 85-98

    3. Efficient Implementation of a Self-sufficient Solar-Powered Real-Time Deep Learning-Based System

      • Sorin Liviu Jurj, Raul Rotar, Flavius Opritoiu, Mircea Vladutiu
      Pages 99-118

    4. Leveraging Radar Features to Improve Point Clouds Segmentation with Neural Networks

      • Alessandro Cennamo, Florian Kaestner, Anton Kummert
      Pages 119-131

    5. LSTM Neural Network for Fine-Granularity Estimation on Baseline Load of Fast Demand Response

      • Shun Matsukawa, Keita Suzuki, Chuzo Ninagawa, Junji Morikawa, Seiji Kondo
      Pages 132-142

    6. Predicting Permeability Based on Core Analysis

      • Harry Kontopoulos, Hatem Ahriz, Eyad Elyan, Richard Arnold
      Pages 143-154

    7. Probabilistic Estimation of Evaporated Water in Cooling Towers Using a Generative Adversarial Network

      • Serafí­n Alonso, Antonio Morán, Daniel Pérez, Miguel A. Prada, Juan J. Fuertes, Manuel Domí­nguez
      Pages 155-166

    8. Reconstructing Environmental Variables with Missing Field Data via End-to-End Machine Learning

      • Matteo Sangiorgio, Stefano Barindelli, Valerio Guglieri, Giovanna Venuti, Giorgio Guariso
      Pages 167-178

    9. Semantic Segmentation Based on Convolution Neural Network for Steel Strip Position Estimation

      • Aline de Faria Lemos, Bálazs Vince Nagy
      Pages 179-189

    10. Towards a Digital Twin with Generative Adversarial Network Modelling of Machining Vibration

      • Evgeny Zotov, Ashutosh Tiwari, Visakan Kadirkamanathan
      Pages 190-201

    11. \(\lambda \)-DNNs and Their Implementation in Aerodynamic and Conjugate Heat Transfer Optimization

      • Marina Kontou, Dimitrios Kapsoulis, Ioannis Baklagis, Kyriakos Giannakoglou
      Pages 202-214

    12. Symbols in Engineering Drawings (SiED): An Imbalanced Dataset Benchmarked by Convolutional Neural Networks

      • Eyad Elyan, Carlos Francisco Moreno-García, Pamela Johnston
      Pages 215-224
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Other volumes

  1. Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference

Keywords

About this book

This book gathers the proceedings of the 21st Engineering Applications of Neural Networks Conference, which is supported by the International Neural Networks Society (INNS). Artificial Intelligence (AI) has been following a unique course, characterized by alternating growth spurts and “AI winters.” Today, AI is an essential component of the fourth industrial revolution and enjoying its heyday. Further, in specific areas, AI is catching up with or even outperforming human beings. This book offers a comprehensive guide to AI in a variety of areas, concentrating on new or hybrid AI algorithmic approaches with robust applications in diverse sectors.


One of the advantages of this book is that it includes robust algorithmic approaches and applications in a broad spectrum of scientific fields, namely the use of convolutional neural networks (CNNs), deep learning and LSTM in robotics/machine vision/engineering/image processing/medical systems/the environment; machine learning and meta learning applied to neurobiological modeling/optimization; state-of-the-art hybrid systems; and the algorithmic foundations of artificial neural networks.

Editors and Affiliations

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

    Lazaros Iliadis

  • Lancaster University, Lancaster, UK

    Plamen Parvanov Angelov

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

    Chrisina Jayne

  • University of the West of England, Bristol, UK

    Elias Pimenidis

Bibliographic Information

  • Book Title: Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference

  • Book Subtitle: Proceedings of the EANN 2020

  • Editors: Lazaros Iliadis, Plamen Parvanov Angelov, Chrisina Jayne, Elias Pimenidis

  • Series Title: Proceedings of the International Neural Networks Society

  • DOI: https://doi.org/10.1007/978-3-030-48791-1

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

  • Softcover ISBN: 978-3-030-48790-4Published: 28 May 2020

  • eBook ISBN: 978-3-030-48791-1Published: 27 May 2020

  • Series ISSN: 2661-8141

  • Series E-ISSN: 2661-815X

  • Edition Number: 1

  • Number of Pages: XXVII, 619

  • Number of Illustrations: 98 b/w illustrations, 161 illustrations in colour

  • Topics: Artificial Intelligence, Computational Intelligence

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