Archetypal analysis is an unsupervised learning method for exploratory data analysis. One major challenge that limits the applicability of archetypal analysis in practice is the inherent computational complexity of th...
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Graph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification, leveraging the information from neighboring nodes to improve the representation learning of target node. The ...
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With the rapid development of 5th Generation Mobile Communication Technology (5G), the diverse forms of collaboration and extensive data in academic social networks constructed by 5G papers make the management and ana...
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Motivated by the advancing computational capacity of distributed end-user equipment (UE), as well as the increasing concerns about sharing private data, there has been considerable recent interest in machine learning ...
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Lane detection task is the most necessary part in modern intelligent driving technology. However, there still have many challenge need to be conquered. In this paper, we proposed a three-branch neural network, in this...
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ISBN:
(纸本)9781728145709
Lane detection task is the most necessary part in modern intelligent driving technology. However, there still have many challenge need to be conquered. In this paper, we proposed a three-branch neural network, in this framework, there are several new techniques are used. Including multi-branch, lightweight module, feature recalibration and decoder module, named Three Branch Net. Furthermore, A new dataset have been used, which includes much more complex situation and more close to the real world. Compared with other newest method, experiment results shows the proposed approach is the most effective method in complex road conditions task.
Pan-sharpening is used to fuse multispectral images with low spatial resolution and a panchromatic (Pan) image with high spatial resolution to generate synthesized images featured with high spatial and multispectral p...
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ISBN:
(纸本)9781450387835
Pan-sharpening is used to fuse multispectral images with low spatial resolution and a panchromatic (Pan) image with high spatial resolution to generate synthesized images featured with high spatial and multispectral properties. The pan-sharpened images are assumed valuable for further application. However, there have been a few investigations on the effectiveness of the pan-sharpened products in practice (i.e. object detection), compared with the fact many algorithms for pan-sharpening have been developed. In this paper, improvements contributed by pan-sharpening process for the object detection in multispectral imagery were investigated. Original multispectral images along with the corresponding Pan images acquired by Gaojing-1 (SuperView-1, as the first sub-meter high-resolution commercial remote sensing satellite independently developed in China) satellite were used. Seven algorithms widely used in pan-sharpening were applied separately and compared, while the object detection experiments were done by implementing Faster RCNN. The preliminary findings show: (1) the pan-sharpened images present obviously positive contribution to object detection with Faster RCNN, compared to the original multispectral images; (2) detection results of the pan-sharpened images vary with the algorithms used in pan-sharpening process. Furthermore, this investigation suggests none of the pan-sharpening algorithms showed absolute advantages in image fusion to achieve better object detection consequently.
Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of dia...
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Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit
We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on gr...
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Most existing popular methods for learning graph embedding only consider fixed-order global structural features and lack structures hierarchical representation. To address this weakness, we propose a novel graph embed...
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The existence of noise in data may decrease the performance of the classification model. An intuitive assumption suggests that samples with a higher local density have a greater probability of being correctly classifi...
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The existence of noise in data may decrease the performance of the classification model. An intuitive assumption suggests that samples with a higher local density have a greater probability of being correctly classified. Moreover, there are differences in the importance of features. It should further increase the influence of important features on the classification task. Motivated by these issues, we propose a sample-driven adaptive feature selection method. It aims to the impact of noise and obtain the adaptive importance of features. In this paper, the Weighted Fuzzy Rough Sets model (WFRS) is proposed by utilizing more reliable samples to address noisy data. Firstly, the approximation margin is derived by applying the WFRS model within the original feature space from the sample's perspective. It serves as a basis for deriving the feature weights. On this basis, the weighted feature space is obtained. This space compresses the features with the lower weight and stretches the features with higher weight. Then, we obtain a specific fuzzy relation to maximize the within-class samples and minimize the between-class samples in the weighted feature space. Further, an evaluation function of features is constructed to model the uncertainty of fuzzy positive and non-negative regions. A Sample-driven adaptive Feature selection algorithm based on the Weighted fuzzy rough sets model (SFW) is designed to capture the varying importance of features by considering the local density of samples. The weighted features are subsequently selected to facilitate the downstream classification tasks. Experimental results show the WFRS model's robustness, the effectiveness of SFW, and its superiority compared to alternative methods.
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