Chao-Chee Ku

784 citations
9 papers · 605 indexed · 1 hit paper · h-index 5
Topics
Neural Networks and Applications (6 papers)Advanced Image Processing Techniques (3 papers)Advanced Vision and Imaging (2 papers)
Journals
IEEE Transactions on Consumer ElectronicsIEEE Transactions on Neural Networks
Partner nations
United StatesSouth Korea

In The Last Decade

Chao-Chee Ku

8 papers receiving 576 citations

Hit Papers

Diagonal recurrent neural networks for dynamic systems co...19952026200520151995100200300400500

Peers

Chao-Chee Ku
Comparison fields: 5 of 57
  • Control and Systems Engineering 440
  • Artificial Intelligence 307
  • Electrical and Electronic Engineering 124
  • Mechanical Engineering 47
  • Computer Networks and Communications 36
Replace Asriel U. Levin with:
Asriel U. Levin United States
Cajetan M. Akujuobi United States
Shibendu Mahata India
Saša S. Nikolić Serbia
Yinya Li China
Venkatesh Rajagopalan United States
Wen‐Shyong Yu Taiwan
M. Desai United States
W.W. Tan Singapore
Hui Zheng China
Chao-Chee Ku relative to Asriel U. Levin United States Asriel U. Levin's profile →
Citations per field
00.5×1.5×1.9×
Asriel U. Levin · 1×
Citations per year

Countries citing papers authored by Chao-Chee Ku

Since Specialization
Citations

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

Fields of papers citing papers by Chao-Chee Ku

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chao-Chee Ku. 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 Chao-Chee Ku. The network helps show where Chao-Chee Ku may publish in the future.

Co-authorship network of co-authors of Chao-Chee Ku

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

All Works

9 of 9 papers shown
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Diagonal recurrent neural networks for dynamic systems controlbreakdown →
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Diagonal recurrent neural networks for control of dynamic systems
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About Chao-Chee Ku

Chao-Chee Ku is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 9 papers that have together received 605 indexed citations. Recurring topics across this work include Neural Networks and Applications (6 papers), Advanced Image Processing Techniques (3 papers) and Advanced Vision and Imaging (2 papers). The work is most often cited by research in Control and Systems Engineering (440 citations), Artificial Intelligence (307 citations) and Signal Processing (33 citations). Chao-Chee Ku has collaborated with scholars based in United States and South Korea. Frequent co-authors include K.Y. Lee, Kwang Y. Lee, June Ho Park and R.M. Edwards. Their work appears in journals such as IEEE Transactions on Consumer Electronics and IEEE Transactions on Neural Networks.

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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