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Titlebook: Internet of Things (IoT): Key Digital Trends Shaping the Future; Proceedings of 7th I Rajiv Misra,Muttukrishnan Rajarajan,Ashok Patel Confe

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书目名称Internet of Things (IoT): Key Digital Trends Shaping the Future
副标题Proceedings of 7th I
编辑Rajiv Misra,Muttukrishnan Rajarajan,Ashok Patel
视频video
概述Presents recent advances on IoT and connected technologies.Discusses the outcomes of ICIoTCT 2022, held in IIT Patna, Bihar, India.Serves as a reference guide for researchers and practitioners in acad
丛书名称Lecture Notes in Networks and Systems
图书封面Titlebook: Internet of Things (IoT): Key Digital Trends Shaping the Future; Proceedings of 7th I Rajiv Misra,Muttukrishnan Rajarajan,Ashok Patel Confe
描述.The book is a collection of high-quality research papers presented at 7th International Conference on Internet of Things and Connected Technologies (ICIoTCT 2022), held at Indian Institute of Technology (IIT) Patna, Bihar, India during 29–30 September 2022. This book presents recent advances on IoT and connected technologies. This book is designed for marketing managers, business professionals, researchers, academicians, and graduate-level students seeking to learn how IoT and connecting technologies increase the amount of data gained through devices, enhance customer experience, and widen the scope of IoT analytics in enhancing customer marketing outcomes..
出版日期Conference proceedings 2023
关键词Internet of Things; IOT; Smart Cities; Automation; Smart Home Applications; Communication Technologies; IC
版次1
doihttps://doi.org/10.1007/978-981-19-9719-8
isbn_softcover978-981-19-9718-1
isbn_ebook978-981-19-9719-8Series ISSN 2367-3370 Series E-ISSN 2367-3389
issn_series 2367-3370
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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Lecture Notes in Networks and Systemshttp://image.papertrans.cn/i/image/472462.jpg
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Internet of Things (IoT): Key Digital Trends Shaping the Future978-981-19-9719-8Series ISSN 2367-3370 Series E-ISSN 2367-3389
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An Efficacious Classifier for Recognition of Traffic Symbols,e classification refers to the use of several computer algorithms to classify images to extract useful information and as to what they symbolize. It is one of the deep learning methods. Traffic symbols are important to be noticed while driving any vehicle. This paper proposes an image classifier to
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Localization Technologies, the literature on localization is vast and spans many different disciplines, we conducted a comprehensive review of the dominant technologies used in indoor as well as outdoor environments. Surveying localization technologies, including healthcare, mapping, navigation, indoor environment, and wirel
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