Deep Learning Applications: In Computer Vision, Signals and Networks

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.

本書提出了各種深度學習模型，介紹了深度學習算法在實際應用中的應用和使用情況。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，給工程師和開發人員帶來了巨大的挑戰，需要提出解決方案以滿足這些需求。本書介紹了貢獻者進行的案例研究，以克服這些問題。這些研究可以作為設計師在應用深度學習解決視覺、信號和網絡等實際問題時的參考。

本書的內容分為三個部分。第一部分介紹了應用於植物病害診斷、PM2.5濃度估計、表面缺陷檢測和船板識別等領域的人工智能視覺應用。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、基於廣義學習的信號調製識別和基於圖神經網絡（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在網絡人工智能中的應用，例如用於爭議檢測的端到端圖嵌入方法、推斷Apache軟件之間關係的自主系統-GNN架構、用於識別和檢測庞氏騙局的庞氏騙局檢測框架，以及用於預測分子生物活性的GNN應用。