Titlebook: Big Data Technologies and Applications; 10th EAI Internation Zeng Deze,Huan Huang,Naveen Chilamkurti Conference proceedings 2021 ICST Insti

聲音會爆炸

書目名稱Big Data Technologies and Applications影響因子(影響力)




書目名稱Big Data Technologies and Applications影響因子(影響力)學(xué)科排名




書目名稱Big Data Technologies and Applications網(wǎng)絡(luò)公開度




書目名稱Big Data Technologies and Applications網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Big Data Technologies and Applications被引頻次




書目名稱Big Data Technologies and Applications被引頻次學(xué)科排名




書目名稱Big Data Technologies and Applications年度引用




書目名稱Big Data Technologies and Applications年度引用學(xué)科排名




書目名稱Big Data Technologies and Applications讀者反饋




書目名稱Big Data Technologies and Applications讀者反饋學(xué)科排名

Ingredient

https://doi.org/10.1007/978-3-319-53103-8different time nodes based on aggregate data and sequence data. The experimental results show that sequence data is more effective than aggregate data to predict learning results. The prediction AUC of RF model on sequence data is 0.77 at the lowest and 0.83 at the highest, the prediction AUC of CAR

PACT

偶然

https://doi.org/10.1007/978-1-349-08039-7ditional sophisticated features, while also new techniques and tools are frequently introduced as a result of the undergoing research activities. Nevertheless, despite the large efforts and investments on research and innovation, the Big Data technologies introduce also a number of challenges to its

inculpate

萬花筒

The Vicinity of the Critical Point,or Machine (SVM) for improving performance of activity recognition. We also applied feature selection method to the collected data to decrease time complexity and increase the performance. Many experiments are conducted in this work to evaluate performance of the presented technique with human activ

bacteria

加劇

明確

Constructing Knowledge Graph for Prognostics and Health Management of On-board Train Control System nabled training models, which reveal the distribution of the feature importance and quantitatively evaluate the fault correlation of all related features. The presented scheme is demonstrated by a big data platform with incremental field data sets from railway operation process. Case study results s

HPA533

Early Detecting the At-risk Students in Online Courses Based on Their Behavior Sequencesdifferent time nodes based on aggregate data and sequence data. The experimental results show that sequence data is more effective than aggregate data to predict learning results. The prediction AUC of RF model on sequence data is 0.77 at the lowest and 0.83 at the highest, the prediction AUC of CAR