Jae Joon Ahn

689 total citations
41 papers, 480 citations indexed

About

Jae Joon Ahn is a scholar working on Management Science and Operations Research, Economics and Econometrics and Information Systems. According to data from OpenAlex, Jae Joon Ahn has authored 41 papers receiving a total of 480 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Management Science and Operations Research, 8 papers in Economics and Econometrics and 7 papers in Information Systems. Recurrent topics in Jae Joon Ahn's work include Stock Market Forecasting Methods (11 papers), Forecasting Techniques and Applications (5 papers) and Customer churn and segmentation (4 papers). Jae Joon Ahn is often cited by papers focused on Stock Market Forecasting Methods (11 papers), Forecasting Techniques and Applications (5 papers) and Customer churn and segmentation (4 papers). Jae Joon Ahn collaborates with scholars based in South Korea, Puerto Rico and Ethiopia. Jae Joon Ahn's co-authors include Kyong Joo Oh, Tae Yoon Kim, Dong Ha Kim, Keunje Yoo, Sudheer Kumar Shukla, Joonhong Park, Suk Jun Lee, Hyunchul Ahn, Y.-M. Kim and Young Min Kim and has published in prestigious journals such as PLoS ONE, Journal of Cleaner Production and Expert Systems with Applications.

In The Last Decade

Jae Joon Ahn

38 papers receiving 451 citations

Peers

Jae Joon Ahn

MSOR

EEE

FINAN

Mohd Tahir Ismail Malaysia

Lu‐Tao Zhao China

Rajalingam Sokkalingam Malaysia

Jingtao Yao Canada

Bonsoo Koo Australia

Devon K. Barrow United Kingdom

Christoph Lohrmann Finland

Ming Xie China

Liwei Fan China

Mohd Tahir Ismail Malaysia

Jae Joon Ahn

219 ×1.2

MSOR

223 ×1.7

139 ×1.7

EEE

88 ×1.4

98 ×1.6

FINAN

Citations per year, relative to Jae Joon Ahn Jae Joon Ahn (= 1×) peers Mohd Tahir Ismail

Countries citing papers authored by Jae Joon Ahn

Since Specialization

Citations

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

Fields of papers citing papers by Jae Joon Ahn

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jae Joon Ahn. 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 Jae Joon Ahn. The network helps show where Jae Joon Ahn may publish in the future.

Co-authorship network of co-authors of Jae Joon Ahn

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

All Works

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20 of 20 papers shown

Lee, Hee Soo, et al.. (2024). Developing a Dynamic Feature Selection System (DFSS) for Stock Market Prediction: Application to the Korean Industry Sectors. Applied Sciences. 14(16). 7314–7314.

Kim, Seong‐Gon, et al.. (2024). Image reconstruction method of gamma emission tomography based on prior-aware information and machine learning for partial-defect detection of PWR-type spent nuclear fuel. Nuclear Engineering and Technology. 56(11). 4770–4781. 2 indexed citations

Kim, Ji‐Woo, et al.. (2024). Evaluating Linkage Quality of Population-Based Administrative Data for Health Service Research. Journal of Korean Medical Science. 39(14). e127–e127. 1 indexed citations

Ahn, Jae Joon, et al.. (2024). Performance evaluation of a nuclear facility monitoring system using multi-sensor network and artificial intelligence algorithm. Nuclear Engineering and Technology. 56(11). 4481–4486. 1 indexed citations

Ahn, Jae Joon, et al.. (2023). Design of a Nuclear Monitoring System Based on a Multi-Sensor Network and Artificial Intelligence Algorithm. Sustainability. 15(7). 5915–5915. 5 indexed citations

Ahn, Jae Joon, et al.. (2022). Preliminary study of artificial intelligence-based fuel-rod pattern analysis of low-quality tomographic image of fuel assembly. Nuclear Engineering and Technology. 54(10). 3943–3948. 4 indexed citations

Kong, Tae Hoon, et al.. (2022). Epidemiology of idiopathic sudden sensorineural hearing loss in the era of big data. European Archives of Oto-Rhino-Laryngology. 280(5). 2181–2190. 10 indexed citations

Cho, Sohee, Eun Hee Lee, Hae-In Kim, et al.. (2021). Validation of BMI genetic risk score and DNA methylation in a Korean population. International Journal of Legal Medicine. 135(4). 1201–1212. 8 indexed citations

Park, Joo‐Hyun, et al.. (2021). Association between cotinine-verified smoking status and moderately increased albuminuria in the middle-aged and older population in Korea: A nationwide population-based study. PLoS ONE. 16(2). e0246017–e0246017. 8 indexed citations

10.

Kim, Youngmin, et al.. (2019). Development of Product Recommender System using Collaborative Filtering and Stacking Model. Journal of Convergence Information Technology. 9(6). 83–90. 1 indexed citations

11.

Lee, Hyun Jun, et al.. (2018). A study on initial price change prediction of IPO shares using non-financial information. Journal of the Korean Data and Information Science Society. 29(2). 425–439. 1 indexed citations

12.

Ahn, Jae Joon, et al.. (2016). The Study on Development of Carbon Emission Price Forecasting Model Reflecting Emission Trading Market Characteristics. 15(1). 7–16. 3 indexed citations

13.

Yoo, Keunje, et al.. (2016). Decision tree-based data mining and rule induction for identifying hydrogeological parameters that influence groundwater pollution sensitivity. Journal of Cleaner Production. 122. 277–286. 54 indexed citations

14.

Ahn, Jae Joon, Dong Ha Kim, Kyong Joo Oh, & Tae Yoon Kim. (2012). Applying option Greeks to directional forecasting of implied volatility in the options market: An intelligent approach. Expert Systems with Applications. 39(10). 9315–9322. 18 indexed citations

15.

Ahn, Hyunchul, et al.. (2011). A novel customer scoring model to encourage the use of mobile value added services. Expert Systems with Applications. 38(9). 11693–11700. 5 indexed citations

16.

Ahn, Jae Joon, Young Min Kim, Keunje Yoo, Joonhong Park, & Kyong Joo Oh. (2011). Using GA-Ridge regression to select hydro-geological parameters influencing groundwater pollution vulnerability. Environmental Monitoring and Assessment. 184(11). 6637–6645. 18 indexed citations

17.

Ahn, Hyunchul, et al.. (2010). Using Hybrid Data Mining Techniques for Facilitating Cross-Selling of a Mobile Telecom Market to Develop Customer Classification Model. 4. 1–10. 2 indexed citations

18.

Ahn, Jae Joon, Suk Jun Lee, Kyong Joo Oh, & Tae Yoon Kim. (2009). Intelligent forecasting for financial time series subject to structural changes. Intelligent Data Analysis. 13(1). 151–163. 10 indexed citations

19.

Ahn, Jae Joon, et al.. (2009). Machine Learning Algorithm Selection for Forecasting Behavior of Global Institutional Investors. 2. 1–9. 2 indexed citations

20.

Ahn, Jae Joon, et al.. (2007). Using Support Vector Machine to Development Early Warning System for Financial Crisis. 대한산업공학회 추계학술대회 논문집. 439–446. 2 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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