Overview
- Lazaros Iliadis
School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece
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- Plamen Parvanov Angelov
Lancaster University, Lancaster, UK
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- Chrisina Jayne
School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK
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- Elias Pimenidis
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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About this book
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.
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Table of contents (48 papers)
Front Matter
Pages i-xxviiClassification/Machine Learning
Front Matter
Pages 1-1A Compact Sequence Encoding Scheme for Online Human Activity Recognition in HRI Applications
- Georgios Tsatiris, Kostas Karpouzis, Stefanos Kollias
Pages 3-14Classification of Coseismic Landslides Using Fuzzy and Machine Learning Techniques
- Anastasios Panagiotis Psathas, Antonios Papaleonidas, George Papathanassiou, Sotiris Valkaniotis, Lazaros Iliadis
Pages 15-31Evaluating the Transferability of Personalised Exercise Recognition Models
- Anjana Wijekoon, Nirmalie Wiratunga
Pages 32-44
Convolutional Neural Networks in Robotics/Computer Vision
Front Matter
Pages 45-45Deep Learning-Based Computer Vision Application with Multiple Built-In Data Science-Oriented Capabilities
- Sorin Liviu Jurj, Flavius Opritoiu, Mircea Vladutiu
Pages 47-69Visual Movement Prediction for Stable Grasp Point Detection
- Constanze Schwan, Wolfram Schenck
Pages 70-81
Machine Learning in Engineering and Environment
Front Matter
Pages 83-83Accomplished Reliability Level for Seismic Structural Damage Prediction Using Artificial Neural Networks
- Magdalini Tyrtaiou, Antonios Papaleonidas, Anaxagoras Elenas, Lazaros Iliadis
Pages 85-98Efficient Implementation of a Self-sufficient Solar-Powered Real-Time Deep Learning-Based System
- Sorin Liviu Jurj, Raul Rotar, Flavius Opritoiu, Mircea Vladutiu
Pages 99-118Leveraging Radar Features to Improve Point Clouds Segmentation with Neural Networks
- Alessandro Cennamo, Florian Kaestner, Anton Kummert
Pages 119-131LSTM Neural Network for Fine-Granularity Estimation on Baseline Load of Fast Demand Response
- Shun Matsukawa, Keita Suzuki, Chuzo Ninagawa, Junji Morikawa, Seiji Kondo
Pages 132-142Predicting Permeability Based on Core Analysis
- Harry Kontopoulos, Hatem Ahriz, Eyad Elyan, Richard Arnold
Pages 143-154Probabilistic 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 155-166Reconstructing Environmental Variables with Missing Field Data via End-to-End Machine Learning
- Matteo Sangiorgio, Stefano Barindelli, Valerio Guglieri, Giovanna Venuti, Giorgio Guariso
Pages 167-178Semantic Segmentation Based on Convolution Neural Network for Steel Strip Position Estimation
- Aline de Faria Lemos, Bálazs Vince Nagy
Pages 179-189Towards a Digital Twin with Generative Adversarial Network Modelling of Machining Vibration
- Evgeny Zotov, Ashutosh Tiwari, Visakan Kadirkamanathan
Pages 190-201\(\lambda \)-DNNs and Their Implementation in Aerodynamic and Conjugate Heat Transfer Optimization
- Marina Kontou, Dimitrios Kapsoulis, Ioannis Baklagis, Kyriakos Giannakoglou
Pages 202-214Symbols in Engineering Drawings (SiED): An Imbalanced Dataset Benchmarked by Convolutional Neural Networks
- Eyad Elyan, Carlos Francisco Moreno-García, Pamela Johnston
Pages 215-224
Other volumes
Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference
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