Youngchoon Park

792 citations
17 papers · 443 · 1 hit paper · h-index 8

Impact in

Papers in

Youngchoon Park

14 papers receiving 407 citations

Youngchoon Park's Hit Papers

Deep Learning for Entity Matching 2018 · 282 citations
2820+2+5Years since publication50100150200250

Peers

Youngchoon Park
Comparison fields: 5 of 57
  • Management Science and Operations Research 307
  • Artificial Intelligence 322
  • Information Systems 113
  • Signal Processing 36
  • Computer Vision and Pattern Recognition 61
Replace Gengxin Miao with:
Gengxin Miao United States
Varish Mulwad United States
Linhao Luo Australia
Jiayu Han China
Lipyeow Lim United States
Héléna Galhardas Portugal
Chengfeng Xu China
Haokun Chen China
Yu Lei Hong Kong
Youngchoon Park relative to Gengxin Miao United States Gengxin Miao's profile →
Citations per field
00.5×10×13×
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Citations per year

Countries citing papers authored by Youngchoon Park

Since Specialization
Citations

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

Fields of papers citing papers by Youngchoon Park

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 24 scholars most cited alongside Youngchoon 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 Youngchoon Park Line = papers co-authored together Youngchoon Park links everyone, so they are left out of the graph.

All Works

17 of 17 papers shown
#Work
1
Deep Learning for Entity Matching
Hit paper breakdown →
2018282
2 201763
3 200920
4 201713
5 201912
6 200411
7 201810
8 20189
9 20107
10 19997
11 19974
12 19983
13 20001
14 19981
15
The Measurement of Pedestrian Speed in Central Business District
20030
16 20000
17 20000

About Youngchoon Park

Youngchoon Park is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Management Science and Operations Research, Signal Processing and Management Information Systems, having authored 17 papers that have together received 443 indexed citations. Recurring topics across this work include Image Retrieval and Classification Techniques (5 papers), Advanced Image and Video Retrieval Techniques (5 papers), Data Quality and Management (5 papers), Video Analysis and Summarization (3 papers), Topic Modeling (3 papers), Semantic Web and Ontologies (2 papers), Video Surveillance and Tracking Methods (2 papers) and Music and Audio Processing (2 papers). The work is most often cited by research in Management Science and Operations Research (307 citations), Artificial Intelligence (322 citations), Information Systems (113 citations), Signal Processing (36 citations) and Computer Vision and Pattern Recognition (61 citations). Youngchoon Park has collaborated with scholars based in United States and South Korea. Frequent co-authors include AnHai Doan, Ganesh Krishnan, Esteban Arcaute, Sidharth Mudgal, Han Li, Theodoros Rekatsinas, Forouzan Golshani, Paul Suganthan, Sanjib Das and Jeffrey F. Naughton. Their work appears in journals such as Multimedia Tools and Applications, Journal of Intelligent & Robotic Systems, Proceedings of the VLDB Endowment, DIAL (Catholic University of Leuven) and Movebank.

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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