Short-term passenger flow forecasting is an essential component in urban rail transit operation. Emerging deep-learning models provide good insight into improving prediction precision. Therefore, we propose a deep-learning architecture combined residual network (ResNet), graph convolutional network (GCN) and long short-term memory (LSTM) (called "ResLSTM") to forecast short-term passenger flow in urban rail transit on a network scale. First, improved methodologies of ResNet, GCN, and attention LSTM models are presented. Then, model architecture is proposed, wherein ResNet is used to capture deep abstract spatial correlations between subway stations, GCN is applied to extract network-topology information, and attention LSTM is used to extract temporal correlations. Model architecture includes four branches for inflow, outflow, graph-network topology, as well as weather conditions and air quality. To the best of our knowledge, this is the first time that air-quality indicators have been taken into account, and their influences on prediction precision have been quantified. Finally, ResLSTM is applied to Beijing subway. Three time granularities (10, 15, and 30 min) are chosen to conduct short-term passenger flow forecasting. Comparison of prediction performance of ResLSTM with those of many state-of-the-art models shows the advancement and robustness of ResLSTM. Moreover, comparison of prediction precisions obtained from time granularities of 10, 15, and 30 min indicates that prediction precision increases with increasing time granularity. The study can provide subway operators with insight into short-term passenger flow forecasting by leveraging deep-learning models.

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Purpose The objective of this study is to construct a computer aided diagnosis system for normal people and pneumoconiosis using X-raysand deep learning algorithms. Materials and methods 1760 anonymous digital X-ray images of real patients between January 2017 and June 2020 were collected for this experiment. In order to concentrate the feature extraction ability of the model more on the lung region and restrain the influence of external background factors, a two-stage pipeline from coarse to fine was established. First, the U-Net model was used to extract the lung regions on each sides of the collection images. Second, the ResNet-34 model with transfer learning strategy was implemented to learn the image features extracted in the lung region to achieve accurate classification of pneumoconiosis patients and normal people. Results Among the 1760 cases collected, the accuracy and the area under curve of the classification model were 92.46% and 89% respectively. Conclusion The successful application of deep learning in the diagnosis of pneumoconiosis further demonstrates the potential of medical artificial intelligence and proves the effectiveness of our proposed algorithm. However, when we further classified pneumoconiosis patients and normal subjects into four categories, we found that the overall accuracy decreased to 70.1%. We will use the CT modality in future studies to provide more details of lung regions.

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