In intelligent transportation system (ITS), accurate and real-time prediction of short-term multi-lane traffic flow with existing traffic data is an important part of urban traffic planning, traffic management and control. The data generated in the process of vehicle driving has the cooperative characteristics of multi-source, space-time and dynamic. Combining the data with high-performance computing or cloud computing, a new space-time information framework is designed, which is of great significance for the analysis and prediction of traffic flow, as well as intelligent traffic management, service and decision-making. This paper analyzes the statistical characteristics of urban road traffic flow from the two dimensions of time and space through the spatial-temporal correlation between multi-lane short term traffic flow in single observation point and multi observation points. We constructed a data fusion powered bi-directional long short term memory (DFBD-LSTM) model for individual lane and aggregate traffic flow, then used this model to predict multi-lane short term traffic flow. By taking individual lane traffic flow and aggregate traffic flow as different variables, the model produces more accurate predictions, which can better guide people to travel, alleviate the congestion of urban road traffic network to a certain extent, and improve the utilization rate and transportation efficiency of traffic road.

The computer-aided system and chest X-ray images play an important role in the diagnosis of pneumonia, which are the main way of pneumonia diagnosis. The traditional deep learning models have achieved some success in medical images, which captures the potential features of the image by continuously sliding the fixed convolution kernel. The disadvantage of this method is that it cannot effectively capture the long-distance dependencies in the image, and it does not have the ability of dynamic adaptive modeling. Next, the high-quality labeled data of chest X-ray images are very scarce. In order to achieve high-quality artificial intelligence diagnosis, a large number of high-quality annotated chest X-ray images are required. In this work, based on technologies such as Internet of Medical Things (IoMT) and Digital Twins, we built an intelligent IoMT platform for automatic diagnosis of pneumonia. For the digital twin of the lung, we propose an enhanced vision transformer model (EVTM) for analyzing chest X-ray images to determine whether the patient is infected with pneumonia. The EVTM model utilizes the vision transformer for training and inference on chest X-ray images. Then the EVTM model uses the variational autoencoder model for data augmentation, so that the amount of chest X-ray images meets the training requirements of the model. Finally, we conducted extensive experiments on the standard chest X-ray image dataset to verify the effectiveness of the EVTM model.

Smart applications are essential in intelligent consumer electronics. With the rising focus on health concerns, self-diagnosis applications on smart devices have gained widespread popularity, attracting significant attention from both consumers and researchers. These applications play a crucial role in accurately predicting potential diseases based on user-provided symptom descriptions, thereby facilitating effective treatment. Distinguishing between diseases with similar symptoms yet requiring different treatments is essential. However, automated ICD coding faces challenges due to subtle differences between some ICD codes. Although existing methods has utilized contrastive learning to differentiate between codes, precise selection of high-quality negative samples and accurate matching of code descriptions with patient conditions remain critical factors influencing their performance. Drawing inspiration from medical practices, doctors tend to utilize back-reference knowledge to differentiate between similar diseases. We propose a novel framework for automated ICD coding via contrastive learning with back-reference and synonym knowledge (BRSK) for smart self-diagnosis applications. Specifically, by incorporating back-reference knowledge, we generate challenging negative samples, enhancing the effectiveness of contrastive learning. Additionally, multi-view synonym matching networks are used to fully leverage synonym knowledge, resulting in more precise modeling of semantic matching between codes and patient descriptions. Extensive experiments on MIMIC-III datasets demonstrate that BRSK significantly outperforms state-of-the-art baselines.

Utilizing electrocardiogram (ECG) to assess patients' cardiac health is currently the popular diagnostic method. This approach demonstrates high accuracy and helps alleviate the workload of medical professionals. However, the ECG signals consist of physiological signals with low-frequency and low-amplitude features, including various interference noises. Traditional feature extraction methods of classification models have proven ineffective in handling this data type. Moreover, previous classification models fail to capture the spatial and local structures within ECG data, resulting in learned features that do not include the key features of ECG signals. Additionally, traditional models often adopt neural network as prediction modules, which are likewise inefficient in handling noise within the data. To end these issues, we propose a fuzzy-operated convolutional autoencoder (FOCAE) for the classification of ECG collected from wearable devices. The FOCAE model integrates convolutional autoencoder and fuzzy neural network, enabling it to learn high-quality features from noisy data to ensure the model's inferential capability. Furthermore, the FOCAE model inputs the learned features into the fuzzy neural network and fully connected network to obtain the classification results of ECG. Using fuzzy rules in the fuzzy neural network to describe the mapping between features and classification results enhances the robustness of the predictive outcomes. Some experimental results on a real ECG dataset validate the superior performance of the FOCAE model. The performance of the FOCAE model is superior to other three baseline models. Specifically, the Precision, Accuracy, and F1-Score of the FOCAE model are 0.971, 0.968 and 0.965, respectively.