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.

Contents:

• 
  

  Vision 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)
  
  
  
  

Readership: Researchers working in computer vision, signals, networks, machine learning, and deep learning.

Key Features:

• Samples of real-world examples in AI applications


• State-of-the-art AI solutions for industrial applications


• Case studies in vision and signal processing, as well as networking