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Titlebook: IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le; Second International Joao Gama,Sepide

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發(fā)表于 2025-3-21 18:45:28 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le
副標題Second International
編輯Joao Gama,Sepideh Pashami,Michaela Blott
視頻videohttp://file.papertrans.cn/476/475095/475095.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le; Second International Joao Gama,Sepide
描述This book constitutes selected papers from the Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and?First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 and held in September 2020. Due to the COVID-19 pandemic the workshops were held online.?.The 21 full papers and 3 short papers presented in this volume were thoroughly reviewed and selected from 35 submissions and are organized according to the workshops and their topics:?.IoT Streams 2020:?.Stream Learning;?Feature Learning; .ITEM 2020:.?Unsupervised Machine Learning; Hardware; Methods;?Quantization..
出版日期Conference proceedings 2020
關(guān)鍵詞artificial intelligence; computer hardware; computer networks; computer systems; data mining; data securi
版次1
doihttps://doi.org/10.1007/978-3-030-66770-2
isbn_softcover978-3-030-66769-6
isbn_ebook978-3-030-66770-2Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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書目名稱IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le被引頻次




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1865-0929 ams 2020, and?First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 and held in September 2020. Due to the COVID-19 pandemic the workshops were held online.?.The 21 full papers and 3 short papers presented in this volume were t
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Challenges of Stream Learning for?Predictive Maintenance in the Railway Sector primary goal of leveraging stream learning in order to enhance maintenance operations in the railway sector. We justify the applicability and promising benefits of stream learning via the example of a real-world railway dataset of the train doors.
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Conference proceedings 2020reviewed and selected from 35 submissions and are organized according to the workshops and their topics:?.IoT Streams 2020:?.Stream Learning;?Feature Learning; .ITEM 2020:.?Unsupervised Machine Learning; Hardware; Methods;?Quantization..
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發(fā)表于 2025-3-22 22:22:59 | 只看該作者
1865-0929 horoughly reviewed and selected from 35 submissions and are organized according to the workshops and their topics:?.IoT Streams 2020:?.Stream Learning;?Feature Learning; .ITEM 2020:.?Unsupervised Machine Learning; Hardware; Methods;?Quantization..978-3-030-66769-6978-3-030-66770-2Series ISSN 1865-0929 Series E-ISSN 1865-0937
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: PyTorch for BrainScaleS-2 runtime overhead, measure performance and evaluate the results in terms of the hardware design limitations. As an application of the introduced framework, we present a model that classifies activities of daily living with smartphone sensor data.
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