Linmei Hu

2.1k total citations
46 papers, 1.3k citations indexed

About

Linmei Hu is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Linmei Hu has authored 46 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Artificial Intelligence, 11 papers in Information Systems and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Linmei Hu's work include Topic Modeling (31 papers), Natural Language Processing Techniques (11 papers) and Multimodal Machine Learning Applications (8 papers). Linmei Hu is often cited by papers focused on Topic Modeling (31 papers), Natural Language Processing Techniques (11 papers) and Multimodal Machine Learning Applications (8 papers). Linmei Hu collaborates with scholars based in China, Singapore and Pakistan. Linmei Hu's co-authors include Chuan Shi, Tianchi Yang, Xiaoli Li, Liqiang Nie, Houye Ji, Chao Shao, Ziwang Zhao, Cheng Yang, Juanzi Li and Chen Li and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Information Sciences.

In The Last Decade

Linmei Hu

39 papers receiving 1.2k citations

Peers

Linmei Hu
Comparison fields: 5 of 83
  • Artificial Intelligence 961
  • Information Systems 520
  • Sociology and Political Science 269
  • Computer Vision and Pattern Recognition 181
  • Statistical and Nonlinear Physics 109
Replace Rui Xia with:
Rui Xia China
Belén Díaz‐Agudo Spain
Donghong Ji China
Aliaksei Severyn Italy
Kaize Ding United States
Alper Bilge Türkiye
Qiaoming Zhu China
Jinoh Oh South Korea
Jianxun Lian China
Mohamed Aly United States
Rui Xia China View profile →
Citations per field, relative to Linmei Hu
Linmei Hu · 1×
Citations per year, relative to Linmei Hu
Linmei Hu · 1×

Countries citing papers authored by Linmei Hu

Since Specialization
Citations

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

Fields of papers citing papers by Linmei Hu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Linmei Hu

This figure shows the co-authorship network connecting the top 25 collaborators of Linmei Hu. A scholar is included among the top collaborators of Linmei Hu 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 Linmei Hu. Linmei Hu 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
# Work Indexed citations
1 0
2 0
3 1
4 0
5 15
6 6
7 2
8 1
9 2
10 2
11 16
12 13
13 3
14 92
15 120
16 82
17 30
18 237
19
RiMOM-IM results for OAEI 2014
3
20
RiMOM2013 results for OAEI 2013
7

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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