Dalian Liu

892 citations
41 papers · 576 · h-index 14

Impact in

Papers in

Dalian Liu

34 papers receiving 562 citations

Peers

Dalian Liu
Comparison fields: 5 of 100
  • Computer Vision and Pattern Recognition 315
  • Artificial Intelligence 368
  • Media Technology 65
  • Computational Mathematics 3
  • Control and Systems Engineering 65
Replace Bin Shen with:
Bin Shen China
Xianli Pan China
Grigorios Tzortzis Greece
Hamed Masnadi-Shirazi United States
Marzieh Zarinbal Iran
Raju Pal India
Salima Ouadfel Algeria
Christian Döring Germany
Aiping Huang China
Dalian Liu relative to Bin Shen China Bin Shen's profile →
Citations per field
00.5×4.3×
Bin Shen · 1×
Citations per year

Countries citing papers authored by Dalian Liu

Since Specialization
Citations

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

Fields of papers citing papers by Dalian Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Dalian Liu, 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 Dalian Liu Line = papers co-authored together Dalian Liu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 41 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201987
2 201868
3 201547
4 201843
5 201840
6 201839
7 201835
8 201632
9 202024
10 201921
11 201417
12 201916
13 201616
14 201615
15 201410
16 20159
17 20158
18 20208
19 20136
20 20165

About Dalian Liu

Dalian Liu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Signal Processing and Media Technology, having authored 41 papers that have together received 576 indexed citations. Recurring topics across this work include Face and Expression Recognition (17 papers), Advanced Algorithms and Applications (9 papers), Machine Learning and ELM (9 papers), Text and Document Classification Technologies (8 papers), Imbalanced Data Classification Techniques (4 papers), Domain Adaptation and Few-Shot Learning (3 papers), Remote Sensing and Land Use (3 papers) and Remote-Sensing Image Classification (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (315 citations), Artificial Intelligence (368 citations), Media Technology (65 citations), Computational Mathematics (3 citations) and Control and Systems Engineering (65 citations). Dalian Liu has collaborated with scholars based in China, United States and Saudi Arabia. Frequent co-authors include Yingjie Tian, Guoqiang Wu, Jingjing Tang, Yong Shi, Gang Kou, Yiwei He, Xiankai Huang, Qin Zhang, Jia Lv and Qi Wang. Their work appears in journals such as Neural Networks, Knowledge-Based Systems, Information Sciences, Neural Computing and Applications and Neurocomputing.

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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