Yuhai Yu

590 total citations
14 papers, 406 citations indexed

About

Yuhai Yu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Yuhai Yu has authored 14 papers receiving a total of 406 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Yuhai Yu's work include Sentiment Analysis and Opinion Mining (4 papers), AI in cancer detection (4 papers) and Topic Modeling (3 papers). Yuhai Yu is often cited by papers focused on Sentiment Analysis and Opinion Mining (4 papers), AI in cancer detection (4 papers) and Topic Modeling (3 papers). Yuhai Yu collaborates with scholars based in China. Yuhai Yu's co-authors include Jiana Meng, Hongfei Lin, Zhehuan Zhao, Hongfei Lin, Liang Yang, Hai Guo, Zhiyong Tan, Dandan Zhao, Shuang Liu and Xinghao Yu and has published in prestigious journals such as Computers & Mathematics with Applications, Computer Methods and Programs in Biomedicine and International Journal of Machine Learning and Cybernetics.

In The Last Decade

Yuhai Yu

12 papers receiving 388 citations

Peers

Yuhai Yu

AI

CVPR

RNMI

IS

CSA

Hongfei Lin China

Jason Phang United States

Ibon Ruiz Spain

Mohammed Meknassi Morocco

A.K.M. Muzahidul Islam Bangladesh

M. Mohamed Sathik India

Mo Zhang United States

Md. Abul Ala Walid Bangladesh

Hyuk-Yoon Kwon South Korea

Andrew Trask United Kingdom

Hongfei Lin China

Yuhai Yu

177 ×0.6

AI

69 ×0.6

CVPR

58 ×0.6

RNMI

26 ×0.4

IS

35 ×1.0

CSA

Citations per year, relative to Yuhai Yu Yuhai Yu (= 1×) peers Hongfei Lin

Countries citing papers authored by Yuhai Yu

Since Specialization

Citations

This map shows the geographic impact of Yuhai Yu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yuhai Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuhai Yu more than expected).

Fields of papers citing papers by Yuhai Yu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yuhai Yu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yuhai Yu. The network helps show where Yuhai Yu may publish in the future.

Co-authorship network of co-authors of Yuhai Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Yuhai Yu. A scholar is included among the top collaborators of Yuhai Yu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yuhai Yu. Yuhai Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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14 of 14 papers shown

1.

Tan, Zhiyong, et al.. (2023). Self-supervised learning with self-distillation on COVID-19 medical image classification. Computer Methods and Programs in Biomedicine. 243. 107876–107876. 15 indexed citations

2.

Sun, Shichang, Yongdong Huang, Di Zhao, Yuhai Yu, & Jiana Meng. (2023). Transferring variational autoencoders with coarse-and-fine alignment for open set broad classification. International Journal of Machine Learning and Cybernetics. 14(10). 3655–3669.

3.

Meng, Jiana, et al.. (2022). TL-med: A Two-stage transfer learning recognition model for medical images of COVID-19. Journal of Applied Biomedicine. 42(3). 842–855. 21 indexed citations

4.

Meng, Jiana, et al.. (2022). Image Restoration Based on Improved Generative Adversarial Networks. 799–804.

5.

Meng, Jiana, et al.. (2019). Cross-Domain Text Sentiment Analysis Based on CNN_FT Method. Information. 10(5). 162–162. 31 indexed citations

6.

Lin, Hongfei, et al.. (2017). A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification. Information. 8(3). 92–92. 53 indexed citations

7.

Yu, Yuhai, et al.. (2017). Assembling Deep Neural Networks for Medical Compound Figure Detection. Information. 8(2). 48–48. 5 indexed citations

8.

Lin, Hongfei, et al.. (2017). Low-Resource Cross-Domain Product Review Sentiment Classification Based on a CNN with an Auxiliary Large-Scale Corpus. Algorithms. 10(3). 81–81. 14 indexed citations

9.

Yu, Yuhai, et al.. (2017). Deep Transfer Learning for Modality Classification of Medical Images. Information. 8(3). 91–91. 97 indexed citations

10.

Yu, Yuhai, Hongfei Lin, Jiana Meng, & Zhehuan Zhao. (2016). Visual and Textual Sentiment Analysis of a Microblog Using Deep Convolutional Neural Networks. Algorithms. 9(2). 41–41. 95 indexed citations

11.

Yu, Yuhai, Hongfei Lin, Xinghao Yu, & Jiana Meng. (2015). Modality Classification for Medical Images Using Multiple Deep Convolutional Neural Networks. 18 indexed citations

12.

Meng, Jiana, Hongfei Lin, & Yuhai Yu. (2011). A two-stage feature selection method for text categorization. Computers & Mathematics with Applications. 62(7). 2793–2800. 45 indexed citations

13.

Yu, Yuhai, et al.. (2009). Blind Digital Image Watermarking Technique Based On Intermediate Significant Bit and Discrete Wavelet Transform. 38. 1–4. 8 indexed citations

14.

Yu, Yuhai & Ping Zhang. (2004). Service mobility in mobile network. 2. 1698–1701. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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