Ming-Te Chen

524 total citations
32 papers, 410 citations indexed

About

Ming-Te Chen is a scholar working on Information Systems, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Ming-Te Chen has authored 32 papers receiving a total of 410 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Information Systems, 9 papers in Artificial Intelligence and 7 papers in Computer Networks and Communications. Recurrent topics in Ming-Te Chen's work include Cryptography and Data Security (8 papers), Color perception and design (7 papers) and Ergonomics and Musculoskeletal Disorders (6 papers). Ming-Te Chen is often cited by papers focused on Cryptography and Data Security (8 papers), Color perception and design (7 papers) and Ergonomics and Musculoskeletal Disorders (6 papers). Ming-Te Chen collaborates with scholars based in Taiwan and United States. Ming-Te Chen's co-authors include Kong-King Shieh, Chin-Chiuan Lin, Chun‐I Fan, O. Geoffrey Okogbaa, Richard L. Shell, Tsung‐Hung Lin, Tain-Hsiung Chen, Chuan‐Mu Chen, Ming-Chau Chang and Cheng‐Hsun Chen and has published in prestigious journals such as International Journal of Production Research, Applied Sciences and Injury.

In The Last Decade

Ming-Te Chen

28 papers receiving 385 citations

Peers

Ming-Te Chen
Comparison fields: 5 of 68
  • Social Psychology 217
  • Cognitive Neuroscience 111
  • Human-Computer Interaction 56
  • Information Systems 48
  • Computer Vision and Pattern Recognition 47
Replace Davide Gadia with:
Davide Gadia Italy
William C. Treurniet Canada
George Sidiropoulos Greece
Aniello Minutolo Italy
Julia Fink Switzerland
Christian López United States
Jieun Lee South Korea
Andrea Carbone Italy
Gregory M. Corso United States
Davide Gadia Italy View profile →
Citations per field, relative to Ming-Te Chen
Ming-Te Chen · 1×
Citations per year, relative to Ming-Te Chen
Ming-Te Chen · 1×

Countries citing papers authored by Ming-Te Chen

Since Specialization
Citations

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

Fields of papers citing papers by Ming-Te Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming-Te Chen. 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 Ming-Te Chen. The network helps show where Ming-Te Chen may publish in the future.

Co-authorship network of co-authors of Ming-Te Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Ming-Te Chen. A scholar is included among the top collaborators of Ming-Te Chen 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 Ming-Te Chen. Ming-Te Chen 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 2
2 1
3 0
4 3
5 1
6 1
7 5
8 1
9 9
10 21
11 5
12 19
13 7
14 53
15 16
16 1
17 10
18 85
19 43
20 66

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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