The objective of this research is to develop a longterm risk model for the development of cardiovascular disease (CVD) because of type-2 diabetes (T2D). We use the support vector machine (SVM) and the K-nearest neighbours algorithms on the dataset collected from a longitudinal study called Framingham Heart Study, to develop the prediction models. The dataset was first balanced by the Synthetic Minority Oversampling Technique algorithm. The SVM algorithm was then used to train the model, and after tuning the parameters and training for 1000 times, the average accuracy to correctly predict the prevalence of CVD due to T2D came out as 96.5% and the average recall rate was 89.8%. Similarly, we also applied the KNN algorithm to train the dataset, and the recall rate even reaches 92.9%. The advantages of our model are: 1) it can predict with high accuracy both the risk of development of T2D and CVD simultaneously; 2) it can be used without the expensive and tedious oral glucose tolerance test. The model yielded high-performance results after training on the Framingham Heart Study dataset.
In recent years, the LiDAR images, as a 2D compact representation of 3D LiDAR point clouds, are widely applied in various tasks, e.g., 3D semantic segmentation, LiDAR point cloud compression (PCC). Among these works, the optical flow estimation for LiDAR image sequences has become a key issue, especially for the motion estimation of the inter prediction in PCC. However, the existing optical flow estimation models are likely to be unreliable for LiDAR images. In this work, we first propose a light-weight flow estimation model for LiDAR image sequences. The key novelty of our method lies in two aspects. One is that for the different characteristics (with the spatial-variation feature distribution) of the LiDAR images w.r.t. the normal color images, we introduce the attention mechanism into our model to improve the quality of the estimated flow. The other one is that to tackle the lack of large-scale LiDAR-image annotations, we present an unsupervised method, which directly minimizes the inconsistency between the reference image and the reconstructed image based on the estimated optical flow. Extensive experimental results have shown that our proposed model outperforms other mainstream models on the KITTI dataset, with much fewer parameters.
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