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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书目名称IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le影响因子(影响力)




书目名称IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le影响因子(影响力)学科排名




书目名称IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le网络公开度




书目名称IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le网络公开度学科排名




书目名称IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le被引频次




书目名称IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le被引频次学科排名




书目名称IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le年度引用




书目名称IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le年度引用学科排名




书目名称IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le读者反馈




书目名称IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le读者反馈学科排名

发炎

DEFER

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

IRS

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.

FLACK

玩忽职守

婴儿

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..

Forsake

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

沙草纸

: 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.

DEAF