Jang‐Sik Park

695 citations
74 papers · 442 · h-index 11

Impact in

  • Archeology top 5%
    • Metallurgy and Cultural Artifacts
    • Cultural Heritage Materials Analysis
    • Video Surveillance and Tracking Methods
    • Advanced Neural Network Applications
    • Human Pose and Action Recognition

Papers in

Jang‐Sik Park

63 papers receiving 408 citations

Peers

Jang‐Sik Park
Comparison fields: 5 of 93
  • Archeology 52
  • Computer Vision and Pattern Recognition 174
  • Paleontology 40
  • Safety, Risk, Reliability and Quality 45
  • Archeology 42
Replace Goran Kvaščev with:
Goran Kvaščev Serbia
Xiang He China
Ryo Takahashi Japan
Yankui Sun China
Mo Yu China
Wei Ke China
Ganbayar Batchuluun South Korea
Donghai Zhai China
Xingfang Yuan China
Jang‐Sik Park relative to Goran Kvaščev Serbia Goran Kvaščev's profile →
Citations per field
00.5×
Goran Kvaščev · 1×
Citations per year

Countries citing papers authored by Jang‐Sik Park

Since Specialization
Citations

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

Fields of papers citing papers by Jang‐Sik Park

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jang‐Sik Park. 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 Jang‐Sik Park. The network helps show where Jang‐Sik Park may publish in the future.

Co-authors

The 22 scholars most cited alongside Jang‐Sik Park, 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 Jang‐Sik Park Line = papers co-authored together Jang‐Sik Park links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 74 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202171
2 200959
3 201932
4 201732
5 202119
6 202017
7 201016
8 201412
9 200711
10 201510
11 201910
12 202010
13 20189
14 20127
15 20187
16 20067
17 20216
18 20176
19 20115
20 20145

About Jang‐Sik Park

Jang‐Sik Park is a scholar working on Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality, Artificial Intelligence, Archeology and Paleontology, having authored 74 papers that have together received 442 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (26 papers), Fire Detection and Safety Systems (17 papers), Archaeology and ancient environmental studies (9 papers), Metallurgy and Cultural Artifacts (9 papers), Anomaly Detection Techniques and Applications (8 papers), Advanced Neural Network Applications (6 papers), Brain Tumor Detection and Classification (5 papers) and Gait Recognition and Analysis (5 papers). The work is most often cited by research in Archeology (52 citations), Computer Vision and Pattern Recognition (174 citations), Paleontology (40 citations), Safety, Risk, Reliability and Quality (45 citations) and Archeology (42 citations). Jang‐Sik Park has collaborated with scholars based in South Korea, Türkiye and Mongolia. Frequent co-authors include Do‐Young Kang, Cheol Woo Park, İbrahim Furkan İnce, Thilo Rehren, Маршалл, Vasant Shinde, Dmitriy Voyakin, A. Beisenov, Hyun-Suk Shin and Chunag Amartuvshin. Their work appears in journals such as Applied Sciences, Archaeological and Anthropological Sciences, ETRI Journal, Archaeometry and Materials Characterization.

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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