Wonjoon Kim

2.2k total citations · 1 hit paper
65 papers, 1.5k citations indexed

About

Wonjoon Kim is a scholar working on Social Psychology, Artificial Intelligence and Sociology and Political Science. According to data from OpenAlex, Wonjoon Kim has authored 65 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Social Psychology, 13 papers in Artificial Intelligence and 7 papers in Sociology and Political Science. Recurrent topics in Wonjoon Kim's work include Color perception and design (10 papers), Human-Automation Interaction and Safety (7 papers) and Digital Marketing and Social Media (6 papers). Wonjoon Kim is often cited by papers focused on Color perception and design (10 papers), Human-Automation Interaction and Safety (7 papers) and Digital Marketing and Social Media (6 papers). Wonjoon Kim collaborates with scholars based in South Korea, United States and Australia. Wonjoon Kim's co-authors include Muhammad Fazal Ijaz, James Jin Kang, Parvathaneni Naga Srinivasu, Akash Kumar Bhoi, Myung Hwan Yun, Namil Kim, Ilsun Rhiu, Youngdoo Son, Chang S. Nam and Joseph B. Lyons and has published in prestigious journals such as Nature Communications, Scientific Reports and Carbon.

In The Last Decade

Wonjoon Kim

63 papers receiving 1.5k citations

Hit Papers

Classification of Skin Disease Using Deep Learning Neural... 2021 2026 2022 2024 2021 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Wonjoon Kim South Korea 20 337 225 208 200 195 65 1.5k
Hee‐Cheol Kim South Korea 30 499 1.5× 62 0.3× 318 1.5× 264 1.3× 110 0.6× 219 3.0k
Jianlong Zhou Australia 28 643 1.9× 280 1.2× 209 1.0× 316 1.6× 47 0.2× 128 2.7k
Shijian Li China 32 405 1.2× 48 0.2× 931 4.5× 269 1.3× 61 0.3× 135 3.9k
Hongyan Liu China 24 610 1.8× 35 0.2× 112 0.5× 382 1.9× 47 0.2× 174 2.3k
Conrad S. Tucker United States 27 555 1.6× 272 1.2× 156 0.8× 200 1.0× 14 0.1× 145 2.2k
Eunji Kim South Korea 23 422 1.3× 88 0.4× 217 1.0× 275 1.4× 26 0.1× 76 2.0k
Rui Li China 33 320 0.9× 71 0.3× 184 0.9× 101 0.5× 15 0.1× 228 3.8k
Raheel Nawaz United Kingdom 29 1.1k 3.2× 82 0.4× 117 0.6× 363 1.8× 44 0.2× 161 2.9k
Jinyoung Han South Korea 19 335 1.0× 199 0.9× 51 0.2× 110 0.6× 15 0.1× 116 1.6k
Raj Gururajan Australia 20 499 1.5× 74 0.3× 101 0.5× 115 0.6× 58 0.3× 152 1.8k

Countries citing papers authored by Wonjoon Kim

Since Specialization
Citations

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

Fields of papers citing papers by Wonjoon Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wonjoon Kim

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

All Works

20 of 20 papers shown
1.
Nagaraj, P., et al.. (2025). A multi-patch-based deep learning model with VGG19 for breast cancer classifications in the pathology images. Digital Health. 11. 599956873–599956873. 2 indexed citations
2.
Rhiu, Ilsun & Wonjoon Kim. (2025). Korean translation and validation of the multidimensional driving style inventory. Heliyon. 11(3). e42344–e42344.
3.
Kim, Wonjoon, et al.. (2024). Auditory experience in vehicles: A systematic review and future research directions. Heliyon. 10(15). e34838–e34838. 2 indexed citations
4.
Feizi‐Derakhshi, Mohammad‐Reza, et al.. (2024). ADeFS: A Deep Forest Regression-Based Model to Enhance the Performance Based on LASSO and Elastic Net. Mathematics. 13(1). 118–118. 1 indexed citations
5.
Feizi‐Derakhshi, Mohammad‐Reza, et al.. (2024). ERDeR: The Combination of Statistical Shrinkage Methods and Ensemble Approaches to Improve the Performance of Deep Regression. IEEE Access. 12. 33361–33383. 5 indexed citations
6.
Kim, Seungwon, et al.. (2023). Flexible thermocouple using a thermoelectric graphene fiber with a seamless junction. Journal of Material Science and Technology. 172. 15–22. 28 indexed citations
7.
Kim, Wonjoon, et al.. (2023). Development of a therblig-based evaluation methodology for accessible product: a case study of spinal-cord impaired users. Disability and Rehabilitation Assistive Technology. 19(4). 1791–1801. 1 indexed citations
8.
Kim, Wonjoon, et al.. (2023). A study on the evaluation of tokenizer performance in natural language processing. Applied Artificial Intelligence. 37(1). 20 indexed citations
9.
Feizi‐Derakhshi, Mohammad‐Reza, et al.. (2022). An Ensemble Framework to Improve the Accuracy of Prediction Using Clustered Random-Forest and Shrinkage Methods. Applied Sciences. 12(20). 10608–10608. 15 indexed citations
11.
Srinivasu, Parvathaneni Naga, et al.. (2021). Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM. Sensors. 21(8). 2852–2852. 495 indexed citations breakdown →
13.
Suh, Daewoo, Wonjoon Kim, Chan-Yong Park, et al.. (2020). Electron tunneling of hierarchically structured silver nanosatellite particles for highly conductive healable nanocomposites. Nature Communications. 11(1). 2252–2252. 41 indexed citations
14.
Lee, Youngdae, et al.. (2018). Research on the Development of Automated Multifunction-Integrated Motion Bed. The Journal of the Institute of Webcasting, Internet and Telecommunication. 18(5). 215–222. 2 indexed citations
15.
Kim, Wonjoon, et al.. (2018). Visual Tactile Attributes in Online Product Presentations for Improving Purchase Intention. International Journal of Contents. 14(2). 7–15. 3 indexed citations
16.
Lee, Joo-Hee, et al.. (2017). The Effects of Consumers’ Perceived Privacy Control on Perceived Privacy Risk in Location-Based Services. International Journal of Contents. 13(1). 22–30. 3 indexed citations
17.
Kim, Wonjoon, et al.. (2016). The Investigation of Study Trends for Heads-Up Displays (HUD) in Visualized Network Form under the Perspectives of Human Factors. International Conference on Human-Computer Interaction. 373–380. 1 indexed citations
18.
Kim, Wonjoon & Changick Kim. (2009). An Efficient Indoor-Outdoor Scene Classification Method. Journal of the Institute of Electronics Engineers of Korea. 46(5). 48–55. 1 indexed citations
19.
Kim, Jong Ho, et al.. (1999). Effect of Imipramine on Calcium Utilization of Single Cells Isolated from Canine Detrusor. Korean Journal of Physiology and Pharmacology. 3(4). 439–445. 1 indexed citations
20.
Woo, Soo Dong, et al.. (1998). The morphology of the polyhedra of a host range-expanded recombinant baculovirus and its parents. Archives of Virology. 143(6). 1209–1214. 15 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.

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