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Titlebook: Edge Computing and IoT: Systems, Management and Security; Third EAI Internatio Zhu Xiao,Ping Zhao,Jinmei Shu Conference proceedings 2023 IC

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发表于 2025-3-21 17:43:13 | 显示全部楼层 |阅读模式
书目名称Edge Computing and IoT: Systems, Management and Security
副标题Third EAI Internatio
编辑Zhu Xiao,Ping Zhao,Jinmei Shu
视频video
丛书名称Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engi
图书封面Titlebook: Edge Computing and IoT: Systems, Management and Security; Third EAI Internatio Zhu Xiao,Ping Zhao,Jinmei Shu Conference proceedings 2023 IC
描述This book constitutes the refereed post-conference proceedings of the Third International Conference on Edge Computing and IoT, ICECI 2022, held in December 13-14, 2022 in Changsha, China. Due to COVID-19 pandemic the conference was held virtually.. The explosion of the big data generated by ubiquitous edge devices motivates the emergence of applying machine learning systems for edge computing and Internet of Things (IoT) services. Machine learning techniques are delivering a promising solution to the industry for building IoT systems and to make innovation at a rapid pace.. The 22 full papers of ICECI 2022 were selected from 76 submissions and present results and ideas in the area of edge computing and IoT..
出版日期Conference proceedings 2023
关键词architecture types; artificial intelligence; computer hardware; computer networks; computer science; comp
版次1
doihttps://doi.org/10.1007/978-3-031-28990-3
isbn_softcover978-3-031-28989-7
isbn_ebook978-3-031-28990-3Series ISSN 1867-8211 Series E-ISSN 1867-822X
issn_series 1867-8211
copyrightICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2023
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发表于 2025-3-21 20:58:43 | 显示全部楼层
1867-8211 ull papers of ICECI 2022 were selected from 76 submissions and present results and ideas in the area of edge computing and IoT..978-3-031-28989-7978-3-031-28990-3Series ISSN 1867-8211 Series E-ISSN 1867-822X
发表于 2025-3-22 03:45:36 | 显示全部楼层
Iveta Silova,Zsuzsa Millei,Nelli Piattoevaare briefly introduced. Then the rest of paper mainly discusses the performance of four models implementing greedy algorithm in IoV fields based on MATLAB by pairwise comparison of experimental results and analysis.
发表于 2025-3-22 05:14:27 | 显示全部楼层
Adrienne E. Gavin,Andrew F. Humphriestion. The YOLO approach will be mainly talked about for application, since it can be easily used combined with other algorithms like semantic segmentation, FCN, and etc. to enhance its performance. What’s more, the computation speed of Poly-YOLO is the main advantage why people choose YOLO.
发表于 2025-3-22 10:27:36 | 显示全部楼层
On Enhancing Transmission Performance for IoV Based on Improved Greedy Algorithmare briefly introduced. Then the rest of paper mainly discusses the performance of four models implementing greedy algorithm in IoV fields based on MATLAB by pairwise comparison of experimental results and analysis.
发表于 2025-3-22 14:18:55 | 显示全部楼层
Precise Segmentation on Poly-Yolo, YoloV5 and FCNtion. The YOLO approach will be mainly talked about for application, since it can be easily used combined with other algorithms like semantic segmentation, FCN, and etc. to enhance its performance. What’s more, the computation speed of Poly-YOLO is the main advantage why people choose YOLO.
发表于 2025-3-22 21:02:46 | 显示全部楼层
Forecasting the Temperature of BEV Battery Pack Based on Field Testing Dataan one year to build a machine-learning model that can forecast the battery pack’s average temperature in real-world driving. Our best Bi-LSTM model achieved the mean absolute error of 2.92 .C on test data and 1.7 .C on cross-validation for prediction of 10 min on the battery pack’s temperature.
发表于 2025-3-22 22:28:46 | 显示全部楼层
发表于 2025-3-23 03:17:46 | 显示全部楼层
1867-8211 held in December 13-14, 2022 in Changsha, China. Due to COVID-19 pandemic the conference was held virtually.. The explosion of the big data generated by ubiquitous edge devices motivates the emergence of applying machine learning systems for edge computing and Internet of Things (IoT) services. Mach
发表于 2025-3-23 09:37:43 | 显示全部楼层
Childhood Trauma in Mental Disordersan one year to build a machine-learning model that can forecast the battery pack’s average temperature in real-world driving. Our best Bi-LSTM model achieved the mean absolute error of 2.92 .C on test data and 1.7 .C on cross-validation for prediction of 10 min on the battery pack’s temperature.
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