Hua Zuo

1.8k citations
36 papers · 1.4k indexed · 1 hit paper · h-index 12
Topics
Domain Adaptation and Few-Shot Learning (23 papers)Machine Learning and ELM (17 papers)Multimodal Machine Learning Applications (11 papers)
Partner nations
AustraliaChinaCanada

In The Last Decade

Hua Zuo

33 papers receiving 1.4k citations

Hit Papers

Transfer learning using computational intelligence: A survey20152026201820222015200400600

Peers

Hua Zuo
Comparison fields: 5 of 138
  • Artificial Intelligence 833
  • Computer Vision and Pattern Recognition 364
  • Control and Systems Engineering 139
  • Mechanical Engineering 94
  • Information Systems 84
Replace Vahid Behbood with:
Vahid Behbood Australia
Georgios Douzas Portugal
Deepak Gupta India
Takashi Onoda Japan
Ji Feng China
Junhai Zhai China
José A. Sáez Spain
Kaiyong Zhao Hong Kong
Harun Uğuz Türkiye
Hua Zuo relative to Vahid Behbood Australia Vahid Behbood's profile →
Citations per field
00.5×1.5×2.0×
Vahid Behbood · 1×
Citations per year

Countries citing papers authored by Hua Zuo

Since Specialization
Citations

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

Fields of papers citing papers by Hua Zuo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hua Zuo

This figure shows the co-authorship network connecting the top 25 collaborators of Hua Zuo. A scholar is included among the top collaborators of Hua Zuo 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 Hua Zuo. Hua Zuo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 1
3 1
4 1
5 0
6 6
7 1
8 3
9 7
10 33
11 6
12 94
13 46
14 1
15 71
16 4
17 119
18
Transfer learning using computational intelligence: A surveybreakdown →
743
19 2
20 2

About Hua Zuo

Hua Zuo is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Ecological Modeling, having authored 36 papers that have together received 1.4k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (23 papers), Machine Learning and ELM (17 papers) and Multimodal Machine Learning Applications (11 papers). The work is most often cited by research in Artificial Intelligence (833 citations), Computer Vision and Pattern Recognition (364 citations) and Signal Processing (65 citations). Hua Zuo has collaborated with scholars based in Australia, China and Canada. Frequent co-authors include Jie Lü, Guangquan Zhang, Vahid Behbood, Shan Xue, Hao Peng, Witold Pedrycz, Feng Liu, Shirui Pan, Jiadi Yu and Siyu Lin. Their work appears in journals such as Renewable Energy, IEEE Transactions on Fuzzy Systems and IEEE Transactions on Cybernetics.

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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