Sport Resource Classification Algorithm for Health Promotion Based on Cloud Computing: Rhythmic Gymnastics' Example.

In the processing of rhythmic gymnastics resources, there are inefficiency problems such as confusion of teaching resources and lack of individuation. To improve the health access to teaching resource data, such as videos and documents, this study proposes a cloud computing-based personalized rhythmic gymnastics teaching resource classification algorithm for health promotion. First, personalized rhythmic gymnastics teaching resource database is designed based on cloud computing technology, and the teaching resources in the database are preprocessed to obtain a meta-sample set. Then, the characteristics of teaching resources are selected by the information acquisition method, and a vector space model is established to calculate the similarity of teaching resources. Finally, the distance-weighted k-NN method is used to classify the teaching resources for health promotion. The experimental results show that the classification accuracy of the proposed algorithm is high, the recall rate is high, and the F-measure value is high, which verifies the effectiveness of the algorithm.

Forecasting Subway Passenger Flow for Station-Level Service Supply.

Demand forecasting is one of the managers' concerns in service supply chain management. With accurate passenger flow forecasting, the station-level service suppliers can make better service plans accordingly. However, the existing forecasting model cannot identify the different future passenger flow at different types of stations. As a result, the service suppliers cannot make service plans according to the demands of different stations. In this article, we propose a deep learning architecture called DeepSPF (Deep Learning for Subway Passenger Forecasting) to predict subway passenger flow considering the different functional types of stations. We also propose the sliding long short-term memory (LSTM) neural networks as an important component of our model, combining LSTM and one-dimensional convolution. In the experiments of the Beijing subway, DeepSPF outperforms the baseline models in three-time granularities (10, 15, and 30 minutes). Moreover, a comparison between variants of DeepSPF indicates that, with the information of stations' functional types, DeepSPF has strong robustness when an abnormal situation happens.