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Könyv Deep Learning Applications: In Computer Vision, Signals and Networks Yun Xiang

Deep Learning Applications: In Computer Vision, Signals and Networks

Szerző: Yun Xiang, Dongwei Xu
Nyelv: Angol
Kötés: Kemény kötésű
Kiadó: WORLD SCIENTIFIC PUB CO INC
Elérhetőség: Beszállítói készleten
Küldés 14-21 napon belül
42 058 Ft
This book proposes various deep learning models featuring how deep learning algorithms have been app...

Információk a könyvről

Szerző
Yun Xiang, Dongwei Xu
Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2023
oldal
310
EAN
9789811266904
ISBN
9811266905
Enbook ID
41590153
Kiadó
WORLD SCIENTIFIC PUB CO INC
Súly
584

Teljes leírás

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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