S.-K. Chang

1.2k citations
50 papers · 613 · h-index 12

Impact in

Papers in

S.-K. Chang

43 papers receiving 558 citations

Peers

S.-K. Chang
Comparison fields: 5 of 59
  • Signal Processing 183
  • Software 55
  • Computer Vision and Pattern Recognition 294
  • Computer Networks and Communications 194
  • Artificial Intelligence 182
Replace Shi-Kuo Chang with:
Shi-Kuo Chang United States
E. Koutsofios United States
Shojiro Tagawa Japan
John Howse United Kingdom
José Oncina Spain
Edward L. Robertson United States
Gem Stapleton United Kingdom
Seung-won Hwang South Korea
Rafael C. Carrasco Spain
Christian Plaunt United States
S.-K. Chang relative to Shi-Kuo Chang United States Shi-Kuo Chang's profile →
Citations per field
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Shi-Kuo Chang · 1×
Citations per year

Countries citing papers authored by S.-K. Chang

Since Specialization
Citations

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

Fields of papers citing papers by S.-K. Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by S.-K. Chang. 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 S.-K. Chang. The network helps show where S.-K. Chang may publish in the future.

Co-authors

The 25 scholars most cited alongside S.-K. Chang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with S.-K. Chang Line = papers co-authored together S.-K. Chang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 50 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1988187
2 198957
3 197740
4 199035
5 199224
6 199023
7
Promising Approach to Distributed Query Processing.
198221
8 197121
9 199421
10 199620
11 200314
12 200812
13 199911
14 197910
15 200110
16 20039
17 19939
18 20028
19 20028
20 20058

About S.-K. Chang

S.-K. Chang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Signal Processing and Information Systems, having authored 50 papers that have together received 613 indexed citations. Recurring topics across this work include Data Management and Algorithms (13 papers), Multimedia Communication and Technology (9 papers), Advanced Database Systems and Queries (9 papers), Video Analysis and Summarization (8 papers), Semantic Web and Ontologies (7 papers), Image Retrieval and Classification Techniques (6 papers), Usability and User Interface Design (5 papers) and Speech and dialogue systems (5 papers). The work is most often cited by research in Signal Processing (183 citations), Software (55 citations), Computer Vision and Pattern Recognition (294 citations), Computer Networks and Communications (194 citations) and Artificial Intelligence (182 citations). S.-K. Chang has collaborated with scholars based in United States, Italy and Taiwan. Frequent co-authors include Timothy Arndt, Y.-M. Deng, Michael J. Tauber, Bing Yu, Deng Tang, Kiyoshi Maruyama, Erland Jungert, A. Hsu, Giuseppe Polese and Genoveffa Tortora. Their work appears in journals such as IEEE Transactions on Software Engineering, Journal of Visual Languages & Computing, IEEE Transactions on Knowledge and Data Engineering, International Journal of Human-Computer Studies and Information Fusion.

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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