Kuo-Lung Wu

23 total papers · 1.7k total citations
18 papers, 1.2k citations indexed

About

Kuo-Lung Wu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistics and Probability. According to data from OpenAlex, Kuo-Lung Wu has authored 18 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 8 papers in Computer Vision and Pattern Recognition and 6 papers in Statistics and Probability. Recurrent topics in Kuo-Lung Wu's work include Advanced Clustering Algorithms Research (12 papers), Face and Expression Recognition (7 papers) and Fuzzy Systems and Optimization (6 papers). Kuo-Lung Wu is often cited by papers focused on Advanced Clustering Algorithms Research (12 papers), Face and Expression Recognition (7 papers) and Fuzzy Systems and Optimization (6 papers). Kuo-Lung Wu collaborates with scholars based in Taiwan and China. Kuo-Lung Wu's co-authors include Miin‐Shen Yang and Jian Yu and has published in prestigious journals such as Pattern Recognition, Neurocomputing and Pattern Recognition Letters.

In The Last Decade

Kuo-Lung Wu

18 papers receiving 1.2k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Kuo-Lung Wu 768 488 179 172 106 18 1.2k
Kuhu Pal 697 0.9× 440 0.9× 125 0.7× 131 0.8× 112 1.1× 11 1.1k
М.А. Айзерман 650 0.8× 426 0.9× 149 0.8× 65 0.4× 169 1.6× 20 1.5k
Frank Höppner 557 0.7× 269 0.6× 216 1.2× 78 0.5× 103 1.0× 49 1.3k
A. Bensaid 769 1.0× 736 1.5× 126 0.7× 151 0.9× 95 0.9× 28 1.5k
M.H.C. Law 794 1.0× 569 1.2× 210 1.2× 76 0.4× 82 0.8× 13 1.3k
Michael P. Windham 516 0.7× 217 0.4× 190 1.1× 64 0.4× 122 1.2× 25 917
E. Backer 520 0.7× 555 1.1× 148 0.8× 111 0.6× 65 0.6× 30 1.3k
Jilian Zhang 787 1.0× 332 0.7× 114 0.6× 71 0.4× 79 0.7× 42 1.2k
Ayhan Demiriz 888 1.2× 548 1.1× 148 0.8× 74 0.4× 79 0.7× 30 1.4k
Raghu Krisnapuram 543 0.7× 406 0.8× 167 0.9× 116 0.7× 104 1.0× 3 1.0k

Countries citing papers authored by Kuo-Lung Wu

Since Specialization
Citations

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

Fields of papers citing papers by Kuo-Lung Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kuo-Lung Wu. 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 Kuo-Lung Wu. The network helps show where Kuo-Lung Wu may publish in the future.

Co-authorship network of co-authors of Kuo-Lung Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Kuo-Lung Wu. A scholar is included among the top collaborators of Kuo-Lung Wu 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 Kuo-Lung Wu. Kuo-Lung Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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