Convolutional neural networks; Long Short-Term Memory; Attention Mechanism; Traffic Flow Prediction; Transportation Cyber-Physical Systems

Many of the publicly available electrocardiogram (ECG) databases either have a low number of people in the database, each with longer recordings, or have more people, each with shorter recordings. As a result, attempting to split a single database into training, testing, and, optionally, validation datasets is challenging. Some models seem to do well with larger training sets, but that leaves only a small set of data for testing. Moreover, if the ECG is segmented by heartbeat, the data are further limited by the number of heartbeats in the recording.

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This dataset mainly consists 1) source codes of wide-attention and deep model (WADC); 2) datasets to evaluate the performance of the proposed model. Datasets are obtained from the Caltrans Performance Measurement System (CPeMS) http://pems.doc.ca.gov; and Fremont Bridge Bicycle Counter (FBBC), https://data.seattle.gov.

 

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