Tae‐Seong Kim

1.4k total citations · 1 hit paper
17 papers, 642 citations indexed

About

Tae‐Seong Kim is a scholar working on Biomedical Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Tae‐Seong Kim has authored 17 papers receiving a total of 642 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Biomedical Engineering, 6 papers in Computer Vision and Pattern Recognition and 5 papers in Artificial Intelligence. Recurrent topics in Tae‐Seong Kim's work include Robot Manipulation and Learning (4 papers), Non-Invasive Vital Sign Monitoring (4 papers) and Context-Aware Activity Recognition Systems (4 papers). Tae‐Seong Kim is often cited by papers focused on Robot Manipulation and Learning (4 papers), Non-Invasive Vital Sign Monitoring (4 papers) and Context-Aware Activity Recognition Systems (4 papers). Tae‐Seong Kim collaborates with scholars based in South Korea, Ecuador and Yemen. Tae‐Seong Kim's co-authors include Mugahed A. Al–antari, Seung‐Moo Han, Tae‐Yeon Kim, Patricio Rivera, Edwin Valarezo Añazco, Mohammed A. Al‐masni, Mun‐Taek Choi, Jeongmin Park, Jin-Hyuk Lee and Ji‐Hwan Kim and has published in prestigious journals such as Sensors, Applied Sciences and Journal of Medical Virology.

In The Last Decade

Tae‐Seong Kim

14 papers receiving 616 citations

Hit Papers

Simultaneous detection and classification of breast masse... 2018 2026 2020 2023 2018 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system
    2018 345 citations Mohammed A. Al‐masni, Mugahed A. Al–antari et al. Computer Methods and Programs in Biomedicine profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Tae‐Seong Kim South Korea 8 424 352 170 101 78 17 642
Samir S. Yadav India 7 330 0.8× 345 1.0× 212 1.2× 102 1.0× 101 1.3× 11 822
Edwin Valarezo Añazco South Korea 9 340 0.8× 269 0.8× 162 1.0× 82 0.8× 92 1.2× 24 586
Stefanie Demirci Germany 12 347 0.8× 268 0.8× 268 1.6× 50 0.5× 129 1.7× 22 718
Najah Alsubaie Saudi Arabia 14 330 0.8× 242 0.7× 161 0.9× 105 1.0× 87 1.1× 35 654
Zhemin Zhuang China 18 420 1.0× 391 1.1× 297 1.7× 89 0.9× 66 0.8× 56 850
Mahboubeh Jannesari Germany 3 234 0.6× 245 0.7× 108 0.6× 55 0.5× 79 1.0× 7 510
Frank Kulwa China 11 451 1.1× 338 1.0× 212 1.2× 66 0.7× 94 1.2× 25 765
Zhennan Yan United States 13 228 0.5× 281 0.8× 205 1.2× 41 0.4× 131 1.7× 37 616
D. R. Sarvamangala India 3 194 0.5× 207 0.6× 133 0.8× 87 0.9× 70 0.9× 5 541
Qihang Yu United States 10 405 1.0× 365 1.0× 615 3.6× 94 0.9× 86 1.1× 20 991

Countries citing papers authored by Tae‐Seong Kim

Since Specialization
Citations

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

Fields of papers citing papers by Tae‐Seong Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tae‐Seong Kim. 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 Tae‐Seong Kim. The network helps show where Tae‐Seong Kim may publish in the future.

Co-authorship network of co-authors of Tae‐Seong Kim

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

All Works

17 of 17 papers shown
1.
Kim, Tae‐Seong, et al.. (2024). Bimanual Long-Horizon Manipulation Via Temporal-Context Transformer RL. IEEE Robotics and Automation Letters. 9(12). 10898–10905.
2.
3.
Chola, Channabasava, et al.. (2023). Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks. Sensors. 23(14). 6491–6491. 10 indexed citations
4.
Chung, Heewon, Hoon Ko, Hooseok Lee, et al.. (2023). Development and validation of a deep learning model to diagnose COVID‐19 using time‐series heart rate values before the onset of symptoms. Journal of Medical Virology. 95(2). e28462–e28462. 8 indexed citations
5.
6.
Rivera, Patricio, et al.. (2022). Dexterous Object Manipulation with an Anthropomorphic Robot Hand via Natural Hand Pose Transformer and Deep Reinforcement Learning. Applied Sciences. 13(1). 379–379. 3 indexed citations
7.
Rivera, Patricio, et al.. (2022). Real-Time Human Activity Recognition with IMU and Encoder Sensors in Wearable Exoskeleton Robot via Deep Learning Networks. Sensors. 22(24). 9690–9690. 28 indexed citations
8.
Añazco, Edwin Valarezo, Patricio Rivera, & Tae‐Seong Kim. (2021). Three‐dimensional shape reconstruction of objects from a single depth view using deep U‐Net convolutional neural network with bottle‐neck skip connections. IET Computer Vision. 15(1). 24–35. 1 indexed citations
9.
Añazco, Edwin Valarezo, et al.. (2020). Natural object manipulation using anthropomorphic robotic hand through deep reinforcement learning and deep grasping probability network. Applied Intelligence. 51(2). 1041–1055. 15 indexed citations
10.
Al–antari, Mugahed A., Seung‐Moo Han, & Tae‐Seong Kim. (2020). Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms. Computer Methods and Programs in Biomedicine. 196. 105584–105584. 184 indexed citations
11.
Rivera, Patricio, Edwin Valarezo Añazco, & Tae‐Seong Kim. (2020). An Integrated ARMA-Based Deep Autoencoder and GRU Classifier System for Enhanced Recognition of Daily Hand Activities. International Journal of Pattern Recognition and Artificial Intelligence. 35(6). 2152006–2152006. 7 indexed citations
12.
Rivera, Patricio, Edwin Valarezo Añazco, Mun‐Taek Choi, & Tae‐Seong Kim. (2019). Trilateral convolutional neural network for 3D shape reconstruction of objects from a single depth view. IET Image Processing. 13(13). 2457–2466. 4 indexed citations
13.
Al‐masni, Mohammed A., Mugahed A. Al–antari, Jeongmin Park, et al.. (2018). Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Computer Methods and Programs in Biomedicine. 157. 85–94. 345 indexed citations breakdown →
14.
Al–antari, Mugahed A., et al.. (2018). Denoising images of dual energy X-ray absorptiometry using non-local means filters. Journal of X-Ray Science and Technology. 26(3). 395–412. 9 indexed citations
15.
Al‐masni, Mohammed A., et al.. (2017). A rapid algebraic 3D volume image reconstruction technique for cone beam computed tomography. Journal of Applied Biomedicine. 37(4). 619–629. 6 indexed citations
16.
17.
Jeong, Jeong‐Won, Tae‐Seong Kim, & Manbir Singh. (2002). Independent component analysis with mixture density model and its application to localize the brain alpha activity in fMRI and EEG. 2001 IEEE Nuclear Science Symposium Conference Record (Cat. No.01CH37310). 4. 1897–1901. 1 indexed citations

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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Samir S. Yadav Breakdown of academic impact, for papers by Edwin Valarezo Añazco Breakdown of academic impact, for papers by Stefanie Demirci Breakdown of academic impact, for papers by Najah Alsubaie Breakdown of academic impact, for papers by Zhemin Zhuang Breakdown of academic impact, for papers by Mahboubeh Jannesari Breakdown of academic impact, for papers by Frank Kulwa Breakdown of academic impact, for papers by Zhennan Yan Breakdown of academic impact, for papers by D. R. Sarvamangala Breakdown of academic impact, for papers by Qihang Yu
Rankless by CCL
2026