Deep Learning Applications: In Computer Vision, Signals and Networks
暫譯: 深度學習應用:於計算機視覺、信號與網絡
Xuan, Qi, Xiang, Yun, Xu, Dongwei
- 出版商: World Scientific Pub
- 出版日期: 2023-04-02
- 售價: $4,390
- 貴賓價: 9.5 折 $4,171
- 語言: 英文
- 頁數: 260
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 9811266905
- ISBN-13: 9789811266904
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相關分類:
DeepLearning、Computer Vision
海外代購書籍(需單獨結帳)
商品描述
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.
商品描述(中文翻譯)
本書提出了各種深度學習模型,展示了深度學習演算法在現實生活中的應用與使用。現實世界情境的複雜性以及環境所施加的限制,加上預算和資源的限制,對工程師和開發人員提出了巨大的挑戰,要求他們提出解決方案以滿足這些需求。本書呈現了其貢獻者所進行的案例研究,以克服這些問題。這些研究可作為設計師在應用深度學習解決視覺、信號和網絡等領域的現實問題時的參考。
本書內容分為三個部分。第一部分介紹了人工智慧在植物疾病診斷、PM2.5濃度估算、表面缺陷檢測和船板識別等應用。第二部分介紹了深度學習在信號處理中的應用;例如時間序列分類、基於廣泛學習的信號調製識別,以及基於圖神經網絡(GNN)的調製識別。最後,本書的最後一部分報告了圖嵌入應用和GNN在網絡中的人工智慧應用;例如用於爭議檢測的端到端圖嵌入方法、一種自動化系統-GNN架構以推斷Apache軟體之間的關係、一個用於識別和檢測龐氏騙局的龐氏騙局檢測框架,以及一個用於預測分子生物活性的GNN應用。