Zobrazeno 1 - 10
of 98
pro vyhledávání: '"Zhonghong Ou"'
Autor:
Yunfeng Duan, Kaiwen Xue, Hao Sun, Haotong Bao, Yadong Wei, Zhangzheng You, Yuantian Zhang, Xiwei Jiang, Sangning Yang, Jiaxing Chen, Boya Duan, Zhonghong Ou
Publikováno v:
Applied Sciences, Vol 14, Iss 16, p 7055 (2024)
With advancements in digital technologies such as 5G communications, big data, and cloud computing, the components of network operation systems have become increasingly complex, significantly complicating system monitoring and maintenance. Correspond
Externí odkaz:
https://doaj.org/article/2f4e6aeeb52e47c0bb07019627115d87
Autor:
Zhenyu Wei, Shigeng Wang, Zhiqun Wang, Yang Zhang, Kexin Chen, Lan Gong, Guigang Li, Qinxiang Zheng, Qin Zhang, Yan He, Qi Zhang, Di Chen, Kai Cao, Jinding Pang, Zijun Zhang, Leying Wang, Zhonghong Ou, Qingfeng Liang
Publikováno v:
EBioMedicine, Vol 88, Iss , Pp 104438- (2023)
Summary: Background: Fungal keratitis (FK) is a leading cause of corneal blindness in developing countries due to poor clinical recognition and laboratory identification. Here, we aimed to identify the distinct clinical signature of FK and develop a
Externí odkaz:
https://doaj.org/article/9fdc712f977c48e2b8528c807b92ba87
Autor:
Zijun Zhang, Haoyu Wang, Shigeng Wang, Zhenyu Wei, Yang Zhang, Zhiqun Wang, Kexin Chen, Zhonghong Ou, Qingfeng Liang
Publikováno v:
Therapeutic Advances in Chronic Disease, Vol 13 (2022)
Background: Infectious keratitis (IK) is an ocular emergency caused by a variety of microorganisms, including bacteria, fungi, viruses, and parasites. Culture-based methods were the gold standard for diagnosing IK, but difficult biopsy, delaying repo
Externí odkaz:
https://doaj.org/article/ee295019d4144946a17266ad08cbcebd
Publikováno v:
IEEE Access, Vol 7, Pp 178798-178810 (2019)
Traffic sign detection has achieved promising results in recent years. Nevertheless, there are still two problems remain to be overcome. One problem is the detection of small traffic signs, which usually occupy less than 2% of the image area. The oth
Externí odkaz:
https://doaj.org/article/e2d09d2ddf7043db84f1335a180fb5c2
Publikováno v:
IEEE Access, Vol 7, Pp 105470-105478 (2019)
Low-rank decomposition is an effective way to decrease the model size of convolutional neural networks (CNNs). Nevertheless, selecting the layer-specific rank is a difficult task, because the layers are not equally redundant. The previous methods are
Externí odkaz:
https://doaj.org/article/f5e85b049959423abb77faae6629d8c4
Publikováno v:
Applied Sciences, Vol 11, Iss 14, p 6633 (2021)
We, the authors, wish to make the following corrections to our paper [1] [...]
Externí odkaz:
https://doaj.org/article/7213038b815c4e48a34da4b27268cd3b
An Attention-Based Latent Information Extraction Network (ALIEN) for High-Order Feature Interactions
Publikováno v:
Applied Sciences, Vol 10, Iss 16, p 5468 (2020)
One of the primary tasks for commercial recommender systems is to predict the probabilities of users clicking items, e.g., advertisements, music and products. This is because such predictions have a decisive impact on profitability. The classic recom
Externí odkaz:
https://doaj.org/article/a1fd204c5a5e4f989e4bd5a607c85f91
Publikováno v:
Applied Sciences, Vol 8, Iss 12, p 2426 (2018)
Recent years have witnessed the growth of recommender systems, with the help of deep learning techniques. Recurrent Neural Networks (RNNs) play an increasingly vital role in various session-based recommender systems, since they use the user’s seque
Externí odkaz:
https://doaj.org/article/b28834ac78164b69aa993ec0419b60e1
Autor:
Zhonghong Ou, Zhaofengnian Wang, Fenrui Xiao, Baiqiao Xiong, Hongxing Zhang, Meina Song, Yan Zheng, Pan Hui
Publikováno v:
IEEE Internet of Things Journal. 10:4226-4238
Publikováno v:
Multimedia Tools and Applications. 82:1017-1043