Bin Song

4.1k total citations
215 papers, 2.8k citations indexed

About

Bin Song is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Bin Song has authored 215 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 91 papers in Computer Vision and Pattern Recognition, 49 papers in Artificial Intelligence and 49 papers in Electrical and Electronic Engineering. Recurrent topics in Bin Song's work include Sparse and Compressive Sensing Techniques (23 papers), Advanced Image and Video Retrieval Techniques (23 papers) and Advanced Neural Network Applications (18 papers). Bin Song is often cited by papers focused on Sparse and Compressive Sensing Techniques (23 papers), Advanced Image and Video Retrieval Techniques (23 papers) and Advanced Neural Network Applications (18 papers). Bin Song collaborates with scholars based in China, United States and Qatar. Bin Song's co-authors include Xiaojiang Du, Mohsen Guizani, Jie Guo, Dan Wang, Xi Yang, Hao Qin, F. Richard Yu, Xinbo Gao, Nannan Wang and Nadra Guizani and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Materials Chemistry A and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Bin Song

194 papers receiving 2.7k citations

Peers

Bin Song
Comparison fields: 5 of 125
  • Electrical and Electronic Engineering 944
  • Computer Vision and Pattern Recognition 840
  • Computer Networks and Communications 815
  • Artificial Intelligence 602
  • Aerospace Engineering 374
Replace Bo Li with:
Bo Li China
Li Erran Li United States
Vaneet Aggarwal United States
Hao Jiang China
Bin Yu China
Li Zhang China
Yi Wu China
Jie Yang China
Peng Li China
Hayder Radha United States
Bo Li China View profile →
Citations per field, relative to Bin Song
Bin Song · 1×
Citations per year, relative to Bin Song
Bin Song · 1×

Countries citing papers authored by Bin Song

Since Specialization
Citations

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

Fields of papers citing papers by Bin Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bin Song

This figure shows the co-authorship network connecting the top 25 collaborators of Bin Song. A scholar is included among the top collaborators of Bin Song 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 Bin Song. Bin Song 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 2
4 1
5 5
6 2
7 4
8 3
9 3
10 6
11 1
12 17
13 10
14 8
15 8
16 17
17 15
18
Fault detection for chemical process based on LSNPE method
4
19
A fault diagnosis decision support system based on analytic hierarchy process
1
20
Application study on fault diagnosis of transformer by gray relational analysis
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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