Jongseong Jang

632 citations
20 papers · 371 · h-index 9

Impact in

Papers in

Jongseong Jang

20 papers receiving 370 citations

Peers

Jongseong Jang
Comparison fields: 5 of 71
  • Health Informatics 31
  • Computer Vision and Pattern Recognition 121
  • Artificial Intelligence 163
  • Radiology, Nuclear Medicine and Imaging 66
  • Media Technology 17
Replace Miguel A. Molina‐Cabello with:
Miguel A. Molina‐Cabello Spain
Xiaozheng Xie China
Gangming Zhao China
Yanning Zhou China
Roberts Kadiķis Latvia
Hossein Kashiani United States
Kh Tohidul Islam Australia
Mohammad Belayet Hossain Australia
Jiangpeng Yan China
Lucía Ramos Spain
Jongseong Jang relative to Miguel A. Molina‐Cabello Spain Miguel A. Molina‐Cabello's profile →
Citations per field
00.5×1.5×
Miguel A. Molina‐Cabello · 1×
Citations per year

Countries citing papers authored by Jongseong Jang

Since Specialization
Citations

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

Fields of papers citing papers by Jongseong Jang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jongseong Jang, 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 Jongseong Jang Line = papers co-authored together Jongseong Jang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 2021101
2 202347
3 201743
4 202040
5 202230
6 201925
7 202123
8 202115
9 20169
10 20248
11 20198
12 20218
13 20145
14 20172
15 20132
16 20221
17 20151
18 20141
19 20171
20 20131

About Jongseong Jang

Jongseong Jang is a scholar working on Biomedical Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Health Informatics, having authored 20 papers that have together received 371 indexed citations. Recurring topics across this work include Soft Robotics and Applications (5 papers), Advanced Neural Network Applications (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Explainable Artificial Intelligence (XAI) (3 papers), Adversarial Robustness in Machine Learning (2 papers), Robotic Path Planning Algorithms (2 papers), Photoacoustic and Ultrasonic Imaging (2 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). The work is most often cited by research in Health Informatics (31 citations), Computer Vision and Pattern Recognition (121 citations), Artificial Intelligence (163 citations), Radiology, Nuclear Medicine and Imaging (66 citations) and Media Technology (17 citations). Jongseong Jang has collaborated with scholars based in South Korea, Canada and United States. Frequent co-authors include Scott Sanner, Zheda Mai, Young Soo Kim, Jihwan Jeong, Hyunwoo Kim, Yee-Suk Kim, Chang Lee, Jeesu Kim, Zhibo Zhang and Chulhong Kim. Their work appears in journals such as Scientific Reports, IEEE Transactions on Industrial Informatics, Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine, IEEE Transactions on Medical Imaging and Journal of Visual Communication and Image Representation.

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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