Chest X-ray film is the most widely used and common method of clinical examination for pulmonary nodules. However, the number of radiologists obviously cannot keep up with this outburst due to the sharp increase in the number of pulmonary diseases, which increases the rate of missed diagnosis and misdiagnosis. The method based on deep learning is the most appropriate way to deal with such problems so far. The main research in this paper was using inception-v3 transfer learning model to classify pulmonary images, and finally to get a practical and feasible computer-aided diagnostic model. The computer-aided diagnostic model could improve the accuracy and rapidity of doctors in the diagnosis of thoracic diseases. In this experiment, we augmented the data of pulmonary images, then used the fine-tuned Inception-v3 model based on transfer learning to extract features automatically, and used different classifiers (Softmax, Logistic, SVM) to classify the pulmonary images. Finally, it was compared with various models based on the original Deep Convolution Neural Network (DCNN) model. The experiment proved that the experiment based on transfer learning was meaningful for pulmonary image classification. The highest sensitivity and specificity are 95.41% and 80.09% respectively in the experiment, and the better pulmonary image classification performance was obtained than other methods.

The existing rail station passenger flow prediction models are inefficient, due to that most of them use single-source data to predict. In this paper, a novel method is proposed based on multi-layer LSTM, which integrates multi-source traffic data and multi-techniques (including feature selection based on Spearman correlation and time feature clustering), to improve the performance of predicting passenger flow. The experimental results show that the multi-source data and the techniques integrated in the model are helpful, and the proposed method obtains a higher prediction accuracy which outperforms other methods (e.g. SARIMA, SVR and BP network) greatly.

To address the current objective of short‐term traffic flow prediction on roads, this paper aims to predict not only the future traffic flow on roads, but also the average speed of future roads. Specifically, the authors propose a short‐term traffic flow prediction method based on the Convolutional Neural Network—Gate Recurrent Unit—Multi Task Learning‐time‐separated attention mechanism (AST‐MTL‐CNN‐GRU). Firstly, the authors add a time‐separated attention mechanism that can effectively generate attention matrices independently for different locations and times of the day. Secondly, the three main parameters of traffic flow, average speed and occupancy are combined in pairs as input data while the multi‐task learning is applied to predict the future traffic flow and average speed on the road. Finally, the CNNs are used to extract the spatial features and the GRUs are used to extract temporal features. Compared with the baseline models (diffusion CNNs (DCRNN), 1D‐CNN‐long short‐term memory (1DCNN‐LSTM), convolutional LSTM (ConvLSTM), graph WaveNet and ST‐ResNet), the authors’ proposed model has high performance in terms of accuracy, stability and robustness. A large number of ablation experiments are conducted based on five‐fold cross validation using the Quanzhou city dataset and the California dataset. The experimental results show that the combination of average speed and occupancy for input has higher prediction accuracy. In addition, the authors’ proposed model can outperform the other models being compared in terms of accuracy, stability and robustness even with noise added.