22   Artículos

 
en línea
Qingji Guan, Qinrun Chen and Yaping Huang    
Chest X-ray image classification suffers from the high inter-similarity in appearance that is vulnerable to noisy labels. The data-dependent and heteroscedastic characteristic label noise make chest X-ray image classification more challenging. To address... ver más
Revista: Algorithms    Formato: Electrónico

 
en línea
Haoxiang Shi, Jun Ai, Jingyu Liu and Jiaxi Xu    
Software defect prediction is a popular method for optimizing software testing and improving software quality and reliability. However, software defect datasets usually have quality problems, such as class imbalance and data noise. Oversampling by genera... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Yongjian Li, He Li, Dazhao Fan, Zhixin Li and Song Ji    
Sea ice extraction and segmentation of remote sensing images is the basis for sea ice monitoring. Traditional image segmentation methods rely on manual sampling and require complex feature extraction. Deep-learning-based semantic segmentation methods hav... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Wenhua Yu, Mayire Ibrayim and Askar Hamdulla    
Text recognition is an important research topic in computer vision. Scene text, which refers to the text in real scenes, sometimes needs to meet the requirement of attracting attention, and there is the situation such as deformation. At the same time, th... ver más
Revista: Information    Formato: Electrónico

 
en línea
Yunya Gao, Stefan Lang, Dirk Tiede, Getachew Workineh Gella and Lorenz Wendt    
Refugee-dwelling footprints derived from satellite imagery are beneficial for humanitarian operations. Recently, deep learning approaches have attracted much attention in this domain. However, most refugees are hosted by low- and middle-income countries ... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Yaojie Zhang, Huahu Xu, Junsheng Xiao and Minjie Bian    
The real world is full of noisy labels that lead neural networks to perform poorly because deep neural networks (DNNs) are prone to overfitting label noise. Noise label training is a challenging problem relating to weakly supervised learning. The most ad... ver más
Revista: Future Internet    Formato: Electrónico

 
en línea
Jialin Shi, Chenyi Guo and Ji Wu    
Deep-learning models require large amounts of accurately labeled data. However, for medical image segmentation, high-quality labels rely on expert experience, and less-experienced operators provide noisy labels. How one might mitigate the negative effect... ver más
Revista: Future Internet    Formato: Electrónico

 
en línea
Qingbin Tong, Feiyu Lu, Ziwei Feng, Qingzhu Wan, Guoping An, Junci Cao and Tao Guo    
The data-driven intelligent fault diagnosis method of rolling bearings has strict requirements regarding the number and balance of fault samples. However, in practical engineering application scenarios, mechanical equipment is usually in a normal state, ... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Xiang Li, Yangyang Liu, Chengli Zhao, Xue Zhang and Dongyun Yi    
Simultaneous outbreaks of contagion are a great threat against human life, resulting in great panic in society. It is urgent for us to find an efficient multiple sources localization method with the aim of studying its pathogenic mechanism and minimizing... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Kai-Sheng Chen    
We present packet switching applications based on extended spectral-amplitude-coding (SAC) labels in generalized multi-protocol label switching (GMPLS) networks. The proposed approach combines the advantages of wavelength-division multiplexing (WDM) and ... ver más
Revista: Applied Sciences    Formato: Electrónico

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