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Titlebook: Intelligent Data Communication Technologies and Internet of Things; Proceedings of ICICI Jude Hemanth,Robert Bestak,Joy Iong-Zong Chen Conf

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书目名称Intelligent Data Communication Technologies and Internet of Things
副标题Proceedings of ICICI
编辑Jude Hemanth,Robert Bestak,Joy Iong-Zong Chen
视频video
概述Presents research works in the field of intelligent data communication technologies.Discusses results of ICICI 2020 held in Coimbatore, India, during 27–28 August 2020.Serves as a reference for resear
丛书名称Lecture Notes on Data Engineering and Communications Technologies
图书封面Titlebook: Intelligent Data Communication Technologies and Internet of Things; Proceedings of ICICI Jude Hemanth,Robert Bestak,Joy Iong-Zong Chen Conf
描述.This book solicits the innovative research ideas and solutions for almost all the intelligent data intensive theories and application domains. The proliferation of various mobile and wireless communication networks has paved way to foster a high demand for intelligent data processing and communication technologies. The potential of data in wireless mobile networks is enormous, and it constitutes to improve the communication capabilities profoundly. As the networking and communication applications are becoming more intensive, the management of data resources and its flow between various storage and computing resources are posing significant research challenges to both ICT and data science community. ..The general scope of this book covers the design, architecture, modeling, software, infrastructure and applications of intelligent communication architectures and systems for big data or data-intensive applications. In particular, this book reports the novel and recent research works on big data, mobile and wireless networks, artificial intelligence, machine learning, social network mining, intelligent computing technologies, image analysis, robotics and autonomous systems, data secur
出版日期Conference proceedings 2021
关键词Data Communication; Computer Networking; Communication Intelligence; Communication Technologies; Mobile
版次1
doihttps://doi.org/10.1007/978-981-15-9509-7
isbn_softcover978-981-15-9508-0
isbn_ebook978-981-15-9509-7Series ISSN 2367-4512 Series E-ISSN 2367-4520
issn_series 2367-4512
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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A Semi-supervised Learning Approach for Complex Information Networks,rates on supervised and unsupervised, proof-of-concept modeling experiments on multi-typed, interconnected data while retaining their semantic importance. The results show that this method yields promising results and can be extended and utilized, using larger, real-world datasets.
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