Figure - available from: Expert Systems
This content is subject to copyright. Terms and conditions apply.
Structures of transformer‐model, scaled dot‐product and multi‐head attention (Vaswani et al., 2017).
Source publication
The lung cancer incidence and mortality in China have always been high. Moreover, due to the limited level of professional technology, misdiagnosis and missed diagnosis of lung cancer often occur. To improve the accuracy of diagnosis, this paper proposes an interpretable diagnostic method for lung cancer based on Chinese electronic medical records...
Similar publications
Background
Information on electrocardiogram (ECG) has not been quantified in obstructive coronary artery disease (ObCAD), despite the deep learning (DL) algorithm being proposed as an effective diagnostic tool for acute myocardial infarction (AMI). Therefore, this study adopted a DL algorithm to suggest the screening of ObCAD from ECG.
Methods
ECG...
Citations
... The continuous increase in the number of motor vehicles has brought many problems to society, such as traffic congestion, a waste of resources, economic losses, excessive commuting times, and frequent traffic accidents. In addition, the pollution caused by the large number of cars may threaten human health [1]. Since traffic flow can reflect the number of vehicles that pass a point in a certain period of time [2], accurate traffic flow forecasting is of great significance to management departments and individuals, which can optimize the design and operation of transportation systems to proposed a method to predict the spatio-temporal characteristics of short-term traffic flow by combing the k-nearest neighbor algorithm and bidirectional long-short-term memory network model. ...
Since traffic congestion during peak hours has become the norm in daily life, research on short-term traffic flow forecasting has attracted widespread attention that can alleviate urban traffic congestion. However, the existing research ignores the uncertainty of short-term traffic flow forecasting, which will affect the accuracy and robustness of traffic flow forecasting models. Therefore, this paper proposes a short-term traffic flow forecasting algorithm combining the cloud model and the fuzzy inference system in an uncertain environment, which uses the idea of the cloud model to process the traffic flow data and describe its randomness and fuzziness at the same time. First, the fuzzy c-means algorithm is selected to carry out cluster analysis on the original traffic flow data, and the number and parameter values of the initial membership function of the system are obtained. Based on the cloud reasoning algorithm and the cloud rule generator, an improved fuzzy reasoning system is proposed for short-term traffic flow predictions. The reasoning system cannot only capture the uncertainty of traffic flow data, but it also can describe temporal dependencies well. Finally, experimental results indicate that the proposed model has a better prediction accuracy and better stability, which reduces 0.6106 in RMSE, reduces 0.281 in MAE, and reduces 0.0022 in MRE compared with the suboptimal comparative methods.
