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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.
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