Yanli Liu

618 total citations
29 papers, 217 citations indexed

About

Yanli Liu is a scholar working on Computer Vision and Pattern Recognition, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Yanli Liu has authored 29 papers receiving a total of 217 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 5 papers in Computer Networks and Communications and 5 papers in Artificial Intelligence. Recurrent topics in Yanli Liu's work include Speech and Audio Processing (2 papers), COVID-19 Clinical Research Studies (2 papers) and Structural Health Monitoring Techniques (2 papers). Yanli Liu is often cited by papers focused on Speech and Audio Processing (2 papers), COVID-19 Clinical Research Studies (2 papers) and Structural Health Monitoring Techniques (2 papers). Yanli Liu collaborates with scholars based in China, France and Spain. Yanli Liu's co-authors include Chu-Min Li, Hua Jiang, Ding Long, Felip Manyà, Xianjia Wang, Guici Chen, Yujun Wang, Yu Li, Zhao Su and Liangkai Chen and has published in prestigious journals such as Diabetes Care, Stroke and Applied Energy.

In The Last Decade

Yanli Liu

25 papers receiving 214 citations

Peers

Yanli Liu
Comparison fields: 5 of 84
  • Infectious Diseases 38
  • Artificial Intelligence 37
  • Computer Vision and Pattern Recognition 36
  • Computer Networks and Communications 32
  • Computational Theory and Mathematics 27
Replace José A. Taboada with:
José A. Taboada Spain
Heba Mamdouh Farghaly Egypt
Yang Zhi China
Pei-Yuan Wu Taiwan
Khalid Shaker Iraq
M.A. Abo-Elsoud Egypt
Nathaniel T. Stevens Canada
Leandro Dias da Silva Brazil
Md. Rezwanul Haque Bangladesh
Mohamed Abd Elfattah Egypt
José A. Taboada Spain View profile →
Citations per field, relative to Yanli Liu
Yanli Liu · 1×
Citations per year, relative to Yanli Liu
Yanli Liu · 1×

Countries citing papers authored by Yanli Liu

Since Specialization
Citations

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

Fields of papers citing papers by Yanli Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yanli Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Yanli Liu. A scholar is included among the top collaborators of Yanli Liu 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 Yanli Liu. Yanli Liu 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 1
3 0
4 4
5 16
6 8
7 1
8 8
9 1
10 1
11 2
12 6
13 21
14 12
15 9
16 27
17 0
18 20
19
A Short Text Clustering Method Based on Deep Neural Network Model
1
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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