Deep Learning Applications: In Computer Vision, Signals and Networks

Xuan, Qi (Editor)/ Xiang, Yun (Editor)/ Xu, Dongwei (Editor)

ISBN 10: 9811266905 / ISBN 13: 9789811266904

Editore: World Scientific Pub Co Inc, 2023

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260 pages. 9.29x6.14x0.94 inches. In Stock.

Codice articolo x-9811266905

Dati bibliografici

• Titolo: Deep Learning Applications: In Computer Vision, Signals and Networks
• Editore: World Scientific Pub Co Inc
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 9811266905
• ISBN 13: 9789811266904
• Rilegatura: Hardcover
• Condizione: Brand New
• Peso: 0,59 chilogrammi

Descrizione articolo

Riassunto

This book proposes various deep learning models featuring how deep learning algorithms have been applied and used in real-life settings. The complexity of real-world scenarios and constraints imposed by the environment, together with budgetary and resource limitations, have posed great challenges to engineers and developers alike, to come up with solutions to meet these demands. This book presents case studies undertaken by its contributors to overcome these problems. These studies can be used as references for designers when applying deep learning in solving real-world problems in the areas of vision, signals, and networks. The contents of this book are divided into three parts. In the first part, AI vision applications in plant disease diagnostics, PM2.5 concentration estimation, surface defect detection, and ship plate identification, are featured. The second part introduces deep learning applications in signal processing; such as time series classification, broad-learning based signal modulation recognition, and graph neural network (GNN) based modulation recognition. Finally, the last section of the book reports on graph embedding applications and GNN in AI for networks; such as an end-to-end graph embedding method for dispute detection, an autonomous System-GNN architecture to infer the relationship between Apache software, a Ponzi scheme detection framework to identify and detect Ponzi schemes, and a GNN application to predict molecular biological activities.

Informazioni sull?autore

Qi Xuan received his BS and PhD degrees in control theory and engineering from Zhejiang University, Hangzhou, China, in 2003 and 2008, respectively. He was a Post-Doctoral Researcher with the Department of Information Science and Electronic Engineering, Zhejiang University, from 2008 to 2010, and a Research Assistant with the Department of Electronic Engineering, City University of Hong Kong, Hong Kong, in 2010 and 2017. From 2012 to 2014, he was a Post-Doctoral Fellow with the Department of Computer Science, University of California at Davis, CA, USA. He is a Senior Member of IEEE, and is currently a Professor with the Institute of Cyberspace Security, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China. His current research interests include network science, graph data mining, cyberspace security, machine learning, and computer vision.

Yun Xiang received his BS degree in physics from Zhejiang University, China, in 2006 and the PhD degree in Electrical Engineering from University of Michigan, USA, in 2014, respectively. He is currently a Lecturer with the Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China. His current research interests include cyberspace security, machine learning, embedded systems, and computer vision.

Dongwei Xu received his BE and PhD degrees from State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China, in 2008 and 2014, respectively. He is currently an associate professor with Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China. His research interests include intelligent transportation control, management, and traffic safety engineering.