Ke Lv

954 citations
86 papers · 575 indexed · h-index 14

Impact in

Papers in

Ke Lv

74 papers receiving 547 citations

Peers

Ke Lv
Comparison fields: 5 of 130
  • Computer Vision and Pattern Recognition 169
  • Media Technology 71
  • Nuclear Energy and Engineering 2
  • Computer Graphics and Computer-Aided Design 12
  • Artificial Intelligence 103
Replace Shoubhik Debnath with:
Shoubhik Debnath United States
Jianzhong Cao China
Zhuang Liu China
Anamika Dhillon India
Matthias Humt Germany
Franck Marzani France
Guanying Huo China
Ke Lv relative to Shoubhik Debnath United States Shoubhik Debnath's profile →
Citations per field
00.5×10×17×
Shoubhik Debnath · 1×
Citations per year

Countries citing papers authored by Ke Lv

Since Specialization
Citations

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

Fields of papers citing papers by Ke Lv

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 86 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201644
2 202238
3 202132
4 201427
5 202326
6 201825
7 202024
8 202121
9 202120
10 202320
11 202116
12 201516
13 202114
14 202113
15 202412
16 202412
17 201912
18 202211
19 202211
20 202111

About Ke Lv

Ke Lv is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Artificial Intelligence, Computational Mechanics and Control and Systems Engineering, having authored 86 papers that have together received 575 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (8 papers), Electric Motor Design and Analysis (7 papers), Machine Fault Diagnosis Techniques (5 papers), Advanced Numerical Analysis Techniques (5 papers), 3D Shape Modeling and Analysis (5 papers), Computer Graphics and Visualization Techniques (5 papers), Advanced Image and Video Retrieval Techniques (5 papers) and Advanced Vision and Imaging (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (169 citations), Media Technology (71 citations), Nuclear Energy and Engineering (2 citations), Computer Graphics and Computer-Aided Design (12 citations) and Artificial Intelligence (103 citations). Ke Lv has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Bin Luo, Weiqiang Wang, Lianlei Shan, Si-Bao Chen, Caixia Gao, Jikai Si, Haichao Feng, Jin Tang, Lingfeng Wang and Chunhong Pan. Their work appears in journals such as Electronics, IEEE Access, IEEE Transactions on Geoscience and Remote Sensing, IET Electric Power Applications and Sensors.

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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