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Deep learning applicationsin computer vision, signals and networks /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning applicationsedited by Qi Xuan, Yun Xiang, Dongwei Xu.
Reminder of title:	in computer vision, signals and networks /
other author:	Xuan, Qi.
Published:	Singapore ;World Scientific,c2023.
Description:	1 online resource :ill.
Subject:	Deep learning (Machine learning)
Online resource:	https://www.worldscientific.com/worldscibooks/10.1142/13158#t=toc
ISBN:	9789811266911$q(ebook for institutions)

Deep learning applicationsin computer vision, signals and networks /
Deep learning applicationsin computer vision, signals and networks /[electronic resource] :edited by Qi Xuan, Yun Xiang, Dongwei Xu. - 1st ed. - Singapore ;World Scientific,c2023. - 1 online resource :ill.

Includes bibliographical references and index.

"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"--

ISBN: 9789811266911$q(ebook for institutions)

LCCN: 2022040917Subjects--Topical Terms:

913129
Deep learning (Machine learning)

LC Class. No.: Q325.73 / .D45 2023

Dewey Class. No.: 006.3/1

Deep learning applicationsin computer vision, signals and networks /
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020 $a 9789811266911$q(ebook for institutions)
020 $a 9789811266928$q(ebook for individuals)
020 $z 9789811266904$q(hardcover)
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040 $a DLC $b eng $c DLC $d DLC
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050 0 0 $a Q325.73 $b .D45 2023
082 0 0 $a 006.3/1 $2 23
245 0 0 $a Deep learning applications $h [electronic resource] : $b in computer vision, signals and networks / $c edited by Qi Xuan, Yun Xiang, Dongwei Xu.
250 $a 1st ed.
260 $a Singapore ; $a Hackensack, NJ : $b World Scientific, $c c2023.
300 $a 1 online resource : $b ill.
504 $a Includes bibliographical references and index.
520 $a "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"-- $c Provided by publisher.
588 $a Description based on print version record.
650 0 $a Deep learning (Machine learning) $3 913129
700 1 $a Xuan, Qi. $3 901352
700 1 $a Xiang, Yun. $3 976669
700 1 $a Xu, Dongwei. $3 976670
856 4 0 $u https://www.worldscientific.com/worldscibooks/10.1142/13158#t=toc