Hai Yang

110 papers receiving 1.3k citations

Peers

Hai Yang
Comparison fields: 5 of 148
  • Nuclear Energy and Engineering 6
  • Experimental and Cognitive Psychology 156
  • Cognitive Neuroscience 184
  • Computer Vision and Pattern Recognition 184
  • Human-Computer Interaction 45
Replace Bing Nan Li with:
Bing Nan Li China
Minghui Liu China
Việt Dũng Nguyễn Vietnam
Norimichi Tsumura Japan
Jaeil Kim South Korea
Sergio Benini Italy
Bowon Lee South Korea
Cong Jin China
Pétia Georgieva Portugal
Hai Yang relative to Bing Nan Li China Bing Nan Li's profile →
Citations per field
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Bing Nan Li · 1×
Citations per year

Countries citing papers authored by Hai Yang

Since Specialization
Citations

This map shows the geographic impact of Hai Yang'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 Hai Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hai Yang more than expected).

Fields of papers citing papers by Hai Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hai Yang. 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 Hai Yang. The network helps show where Hai Yang may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Hai Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Hai Yang Line = papers co-authored together Hai Yang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3 20253
4 20241
5 20240
6 20241
7 20234
8 202326
9 202318
10 20229
11 20223
12 20224
13 202210
14 20223
15 202015
16 20194
17
Preparation of High-stable and Monodisperse Colloidal Silver Nanoparticles
20111
18
The Creation and Application of the Anti-counterfeit Packing of Alcoholic Drinks in China
20073
19
PLASMA TREATMENT OF ORGANIC INHIBITORS FOR CORROSION PROTECTION OF AEROSPACE ALLOYS
20033
20
Research on absorbing EMW properties of steel-fiber concrete
200216

About Hai Yang

Hai Yang is a scholar working on General Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Human-Computer Interaction, having authored 120 papers that have together received 1.3k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (11 papers), EEG and Brain-Computer Interfaces (9 papers), Emotion and Mood Recognition (7 papers), COVID-19 diagnosis using AI (6 papers), Underwater Vehicles and Communication Systems (6 papers), Machine Learning and ELM (6 papers), Multimodal Machine Learning Applications (6 papers) and Cancer Genomics and Diagnostics (5 papers). The work is most often cited by research in Nuclear Energy and Engineering (6 citations), Experimental and Cognitive Psychology (156 citations), Cognitive Neuroscience (184 citations), Computer Vision and Pattern Recognition (184 citations) and Human-Computer Interaction (45 citations). Hai Yang has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Zhe Wang, Dongdong Li, Wenli Du, Rui Chen, Wim J. van Ooij, Xinlei Xu, Li Xie, Zhiyong Zhao, Jeffrey Hill and Run Cang Sun. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Geoscience and Remote Sensing, Knowledge-Based Systems, Journal of Visual Communication and Image Representation and Expert Systems with Applications.

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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