Information Science and Applications : Proceedings of ICISA 2020 (Lecture Notes in Electrical Engineering)

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Information Science and Applications : Proceedings of ICISA 2020 (Lecture Notes in Electrical Engineering)

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 398 p.
  • 言語 ENG
  • 商品コード 9789813363878
  • DDC分類 004

Full Description

This book presents select proceedings of 11th International Conference on Information Science and Applications 2020 (ICISA 2020) and provides a snapshot of the latest issues encountered in technical convergence and convergences of security technology.  It explores how information science is core to most current research, industrial and commercial activities and consists of contributions covering topics including Ubiquitous Computing, Networks and Information Systems, Multimedia and Visualization, Middleware and Operating Systems, Security and Privacy, Data Mining and Artificial Intelligence, Software Engineering, and Web Technology.  Also the proceedings introduce the most recent information technology and ideas, applications and problems related to technology convergence, illustrated through case studies, and reviews converging existing security techniques.  Through this book, readers can gain an understanding of the current state-of-the-art information strategies and technologies of convergence security.

Contents

Experience and prospects of tele-health technologies.- Exploring the Effects of Network Topology Layers on Quality of Service Mechanisms in the Context of Software-Defined Networking.- Image Background Subtraction and Partial Stylization based on Style Representation of Convolutional Neural Networks.- Multi-source log clustering in distributed systems.- A Study on Classification and Integration of Research on both AI and Security in the IoT Era.- Safe Internet: An Edutainment Tool for Teenagers.- Predictive Analytics Service for Security of Blockchain and Peer-to-Peer Payment Solutions.- Deterring SLCAs by Establishment of ACSC based on IPP YoungYungShin 9.30.- Research on life cycle model of data for handling Big Data-based security incident.- Feature Selection Based on a Shallow Convolutional Neural Network and Saliency Maps on Metagenomic Data.

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