000			04400cam a22004218a 4500
001			00013158
003			WSP
005			20260416153411.0
007			cr |nu|||unuuu
008			220901s2023 si ob 001 0 eng
010			_a 2022040917
040			_aWSPC _beng _cWSPC
020			_a9789811266911 _q(ebook)
020			_a9811266913 _q(ebook)
020			_z9789811266904 _q(hbk.)
020			_z9811266905 _q(hbk.)
042			_apcc
050	0	0	_aQ325.73.D311
072		7	_aCOM _x094000 _2bisacsh
072		7	_aCOM _x004000 _2bisacsh
082	0	0	_a006.3/1
049			_aMAIN
245	0	0	_aDeep learning applications : _bin computer vision, signals and networks / _cedited by Qi Xuan, Yun Xiang, Dongwei Xu.
260			_aSingapore : _bWorld Scientific Publishing, _c2023.
300			_a1 online resource (308 p.)
504			_aIncludes bibliographical references and index.
505	0		_aVision applications. Vision-based particulate matter estimation / Kaihua Zhang, Zuohui Chen, and Yun Xiang -- Automatic ship plate recognition using deep learning techniques / Hang Xu, Xinhui Zhang, and Yun Xiang -- Generative adversial network enhanced bearing roller defect detection and segmentation / Jiafei Shao, Zuohui Chen, and Qi Xuan -- Application of deep learning in crop stress /Qijun Chen, Qi Xuan, and Yun Xiang -- Signal applications. A mixed pruning method for signal modulation recognition based on convolutional neural network /Shuncheng Gao, Xuzhang Gao, Jinchao Zhou, Zhuangzhi Chen, Shilian Zheng, and Qi Xuan -- Broad learning system based on Gramian angular field for time series classification / Tingting Feng, Zhuangzhi Chen, Dongwei Xu, and Qi Xuan -- denoising of radio modulation signal based on deep learning / Hongjiao Yao, Qing Zhou, Zhuangzhi Chen, Liang Huang, Dongwei Xu, and Qi Xuan -- A graph neural network modulation recognition framework based on local limited penetrable visibility graph / Jinchao Zhou, Kunfeng Qiu, Zhuangzhi Chen, Shilian Zheng, and Qi Xuan --Network Applications. Study of autonomous system business types based on graph neural networks / Songtao Peng, Lu Zhang, Xincheng Shu, Zhongyuan Ruan, and Qi Xuan -- Social media opinions analysis / Zihan Li and Jian Zhang -- Ethereum's Ponzi scheme detection work based on graph ideas / Jie Jin, Jiajun Zhou, Wanqi Chen, Yunxuan Sheng, and Qi Xuan -- Research on prediction of molecular biological activity based on graph convolution / Yinzuo Zhou, Lulu Tan, Xinxin Zhang, and Shiyue Zhao.
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"-- _cPublisher's website.
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat reader.
650		0	_aDeep learning (Machine learning)
655		0	_aElectronic books.
700	1		_aXuan, Qi.
700	1		_aYun, Xiang.
700	1		_aDongwei Xu.
856	4	0	_uhttps://www.worldscientific.com/worldscibooks/10.1142/13158#t=toc
942			_cE-BOOK
999			_c49753 _d49753