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Titlebook: Disruptive Technologies for Big Data and Cloud Applications; Proceedings of ICBDC J. Dinesh Peter,Steven Lawrence Fernandes,Amir H. Confer

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发表于 2025-3-21 19:11:42 | 显示全部楼层 |阅读模式
书目名称Disruptive Technologies for Big Data and Cloud Applications
副标题Proceedings of ICBDC
编辑J. Dinesh Peter,Steven Lawrence Fernandes,Amir H.
视频video
概述Gathers peer-reviewed proceedings of the International Conference on Big Data and Cloud Computing.Presents not only state of the art in cloud computing technologies and big data.Provides recent innova
丛书名称Lecture Notes in Electrical Engineering
图书封面Titlebook: Disruptive Technologies for Big Data and Cloud Applications; Proceedings of ICBDC J. Dinesh Peter,Steven Lawrence Fernandes,Amir H.  Confer
描述.This book provides a written record of the synergy that already exists among the research communities and represents a solid framework in the advancement of big data and cloud computing disciplines from which new interaction will result in the future. This book is a compendium of the International Conference on Big Data and Cloud Computing (ICBDCC 2021). It includes recent advances in big data analytics, cloud computing, the Internet of nano things, cloud security, data analytics in the cloud, smart cities and grids, etc. This book primarily focuses on the application of knowledge that promotes ideas for solving the problems of society through cutting-edge technologies. The articles featured in this book provide novel ideas that contribute to the growth of world-class research and development. The contents of this book are of interest to researchers and professionals alike..
出版日期Conference proceedings 2022
关键词Big Data; Data Analytics; Cloud Infrastructure for Big Data; Resource Scheduling; Data Models; Machine Le
版次1
doihttps://doi.org/10.1007/978-981-19-2177-3
isbn_ebook978-981-19-2177-3Series ISSN 1876-1100 Series E-ISSN 1876-1119
issn_series 1876-1100
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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1876-1100 d computing technologies and big data.Provides recent innova.This book provides a written record of the synergy that already exists among the research communities and represents a solid framework in the advancement of big data and cloud computing disciplines from which new interaction will result in
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A Statistical Performance Analysis of GPU WAH Range Querying,raphical processing units (GPUs) can process bitmap index queries more efficiently than CPUs in many instances. This paper presents a statistical performance analysis of a GPU bitmap query engine applied to range queries. The results of this analysis provide insights for future GPU query engine design.
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The Instrumental Method in Psychologyrning is that these models can create features without a human intervention. The performance of five different models for forest fire classification is analyzed in this paper: VGG-16, ResNet-50-V2, MobileNet-V2, Inception-V2, and Xception. MobileNet-V2 performed the best among all the architectures with an accuracy of 96.84% on the dataset.
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