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

租期

書目名稱IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le影響因子(影響力)




書目名稱IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le影響因子(影響力)學科排名




書目名稱IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le網(wǎng)絡(luò)公開度




書目名稱IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le網(wǎng)絡(luò)公開度學科排名




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




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




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




書目名稱IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le年度引用學科排名




書目名稱IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le讀者反饋




書目名稱IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Le讀者反饋學科排名

發(fā)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

玩忽職守

嬰兒

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

沙草紙

: 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