Titlebook: Web Information Systems and Applications; 15th International C Xiaofeng Meng,Ruixuan Li,Gansen Zhao Conference proceedings 2018 Springer Na

弄碎

書目名稱Web Information Systems and Applications影響因子(影響力)




書目名稱Web Information Systems and Applications影響因子(影響力)學(xué)科排名




書目名稱Web Information Systems and Applications網(wǎng)絡(luò)公開度




書目名稱Web Information Systems and Applications網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Web Information Systems and Applications被引頻次




書目名稱Web Information Systems and Applications被引頻次學(xué)科排名




書目名稱Web Information Systems and Applications年度引用




書目名稱Web Information Systems and Applications年度引用學(xué)科排名




書目名稱Web Information Systems and Applications讀者反饋




書目名稱Web Information Systems and Applications讀者反饋學(xué)科排名

飛鏢

我正派

Deceit

儀式

Osteoarthritis

Liang Li,Zhongmin Zhang,Shengli Wue theorems and should be able to follow a substantial portion of the mathematical development. The entire book can be read by someone familiar with the basic as978-1-4419-2804-7978-1-4757-6097-2Series ISSN 0066-5452 Series E-ISSN 2196-968X

somnambulism

A Frequent Sequential Pattern Based Approach for Discovering Event Correlationsions in an IoT environment, and proposes an algorithm to discover event correlations. We transform the event correlation discovery problem into a time-constrained frequent sequence mining problem. Moreover, we apply our approach in anomaly warning in a coal power plant. We have made extensive experiments to verify the effectiveness of our approach.

Mucosa

A Frequent Sequential Pattern Based Approach for Discovering Event Correlationsions in an IoT environment, and proposes an algorithm to discover event correlations. We transform the event correlation discovery problem into a time-constrained frequent sequence mining problem. Moreover, we apply our approach in anomaly warning in a coal power plant. We have made extensive experiments to verify the effectiveness of our approach.

顯微鏡

熱心

Multilingual Short Text Classification via Convolutional Neural Network in multilingual text semantics, we introduce convolutional neural network into the model, and output classification prediction results. This model can process multilingual textual data well. Experiments show that convolutional neural network combined with auto-associative memory improves classifica