Ruibing Jin

471 total citations
23 papers, 316 citations indexed

About

Ruibing Jin is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Ruibing Jin has authored 23 papers receiving a total of 316 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 4 papers in Molecular Biology. Recurrent topics in Ruibing Jin's work include Advanced Neural Network Applications (4 papers), Machine Fault Diagnosis Techniques (4 papers) and Plant-Microbe Interactions and Immunity (3 papers). Ruibing Jin is often cited by papers focused on Advanced Neural Network Applications (4 papers), Machine Fault Diagnosis Techniques (4 papers) and Plant-Microbe Interactions and Immunity (3 papers). Ruibing Jin collaborates with scholars based in Singapore, China and United States. Ruibing Jin's co-authors include Zhenghua Chen, Min Wu, Xiaoli Li, Keyu Wu, Ruqiang Yan, Wenjun Sun, Kaizhou Gao, Yucheng Wang, Jianliang Wang and Changyun Wen and has published in prestigious journals such as SHILAP Revista de lepidopterología, Plant Cell & Environment and Frontiers in Plant Science.

In The Last Decade

Ruibing Jin

22 papers receiving 310 citations

Peers

Ruibing Jin
Comparison fields: 5 of 72
  • Control and Systems Engineering 146
  • Artificial Intelligence 72
  • Computer Vision and Pattern Recognition 57
  • Mechanical Engineering 49
  • Electrical and Electronic Engineering 32
Replace Junyan Yang with:
Junyan Yang China
Shinq-Jen Wu Taiwan
Shan Pang China
Zhiang Li China
Steffen Junginger Germany
Tong Guo China
Zhizhong Xing China
Rui Peng United States
Junyan Yang China View profile →
Citations per field, relative to Ruibing Jin
Ruibing Jin · 1×
Citations per year, relative to Ruibing Jin
Ruibing Jin · 1×

Countries citing papers authored by Ruibing Jin

Since Specialization
Citations

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

Fields of papers citing papers by Ruibing Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ruibing Jin

This figure shows the co-authorship network connecting the top 25 collaborators of Ruibing Jin. A scholar is included among the top collaborators of Ruibing Jin 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 Ruibing Jin. Ruibing Jin 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 5
3 3
4 2
5 21
6 31
7 13
8 8
9 16
10 99
11 41
12 2
13 13
14 4
15 8
16 3
17 9
18 3
19 4
20 1

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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