Overview
- 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
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.
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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Keywords
Table of contents (48 papers)
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Front Matter
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Convolutional Neural Networks in Robotics/Computer Vision
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Front Matter
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Machine Learning in Engineering and Environment
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Front Matter
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Other volumes
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Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference
Editors and Affiliations
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