Titlebook: Data Science; 6th International Co Jing He,Philip S. Yu,Fu Xiao Conference proceedings 2020 Springer Nature Singapore Pte Ltd. 2020 artific

大脑

书目名称Data Science影响因子(影响力)




书目名称Data Science影响因子(影响力)学科排名




书目名称Data Science网络公开度




书目名称Data Science网络公开度学科排名




书目名称Data Science被引频次




书目名称Data Science被引频次学科排名




书目名称Data Science年度引用




书目名称Data Science年度引用学科排名




书目名称Data Science读者反馈




书目名称Data Science读者反馈学科排名

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搜寻

Dockless Bicycle Sharing Simulation Based on Arenao bicycle to borrow” and “no land to return”. Existing research, in response to the problem of unbalanced site demand, most scholars predict the demand for bicycle sites. In this study, the use of public bicycles at the site is analyzed from the perspective of simulation. The Arena simulation softwa

Arctic

Simplification of 3D City Models Based on K-Means Clustering models is also increasing at the same time, which brings great pressure to data storage and visualization. Therefore, it is necessary to simplify 3D models. In this paper, a three-step simplification method is proposed. Firstly, the geometric features of the building are used to extract the walls a

ensemble

Fulsome

Fulsome

Measurement Methodology for Empirical Study on Pedestrian Flowussed, separately self-organized behaviors, fundamental diagrams and crowd anomaly detection. Various measurements were put forward by researchers which enriched pedestrian walking characteristics, while it still needs to develop effective measurement methodology to understand and interpret individu

Oscillate

Discovering Traffic Anomaly Propagation in Urban Space Using Traffic Change Peaks accurate for two reasons. First, they discover the propagation pattern based on the detected anomalies. The imperfection of the detection method itself may introduce false anomalies and miss the real anomaly. Second, they develop a propagation tree of anomalies by searching continuous spatial and t

荒唐

Forecasting on Electricity Consumption of Tourism Industry in Changli Countyrgy consumption, this paper applies a new model, NEWARMA model, which means to add the variable’s own medium- and long-term cyclical fluctuations item to the basic ARMA model, and the prediction accuracy will be significantly improved. This paper also compares fitting result of NEWARMA to neural net

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