Qi Xuan – författare

This proceedings constitutes the revised selected papers of the 17th International Conference on Mobile Multimedia Communications, MOBIMEDIA 2024, held in Hangzhou, China, during September 27–29, 2024.The 8 full papers were included in this book were carefully reviewed and selected from 120 submissions. They focus on 5G communication technologies, multimedia services and applications, AI Inspired Future Technologies.

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.

In this context, various advanced techniques, including graph embedding and graph neural networks, have recently been proposed to improve the performance of graph data mining.

In this context, various advanced techniques, including graph embedding and graph neural networks, have recently been proposed to improve the performance of graph data mining.

This book constitutes refereed proceedings of the 7th China National Conference on Big Data and Social Computing, BDSC 2022, held in Hangzhou, China, from August 11-13, 2022The 24 full papers and 2 short papers presented in this volume were carefully reviewed and selected from a total of 99 submissions.