| Using AHP to determine intangible priority factors for technology transfer adoption S Lee, W Kim, YM Kim, KJ Oh Expert Systems with Applications 39 (7), 6388-6395, 2012 | 147 | 2012 |
| A machine learning trading system for the stock market based on N-period Min-Max labeling using XGBoost Y Han, J Kim, D Enke Expert Systems with Applications 211, 118581, 2023 | 77 | 2023 |
| Using genetic algorithm to support clustering-based portfolio optimization by investor information D Cheong, YM Kim, HW Byun, KJ Oh, TY Kim Applied Soft Computing 61, 593-602, 2017 | 77 | 2017 |
| An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms Y Kim, W Ahn, KJ Oh, D Enke Applied Soft Computing 55, 127-140, 2017 | 73 | 2017 |
| Developing a rule change trading system for the futures market using rough set analysis Y Kim, D Enke Expert Systems with Applications 59, 165-173, 2016 | 68 | 2016 |
| The prioritization and verification of IT emerging technologies using an analytic hierarchy process and cluster analysis S Lee, W Kim, YM Kim, HY Lee, KJ Oh Technological forecasting and social change 87, 292-304, 2014 | 55 | 2014 |
| The framework for factors affecting technology transfer for suppliers and buyers of technology in Korea S Lee, BS Kim, Y Kim, W Kim, W Ahn Technology Analysis & Strategic Management 30 (2), 172-185, 2018 | 45 | 2018 |
| Developing an individual glucose prediction model using recurrent neural network DY Kim, DS Choi, J Kim, SW Chun, HW Gil, NJ Cho, AR Kang, J Woo Sensors 20 (22), 6460, 2020 | 38 | 2020 |
| Using deep learning to develop a stock price prediction model based on individual investor emotions J Chun, J Ahn, Y Kim, S Lee Journal of Behavioral Finance 22 (4), 480-489, 2021 | 34 | 2021 |
| Using GA-Ridge regression to select hydro-geological parameters influencing groundwater pollution vulnerability JJ Ahn, YM Kim, K Yoo, J Park, KJ Oh Environmental monitoring and assessment 184, 6637-6645, 2012 | 31 | 2012 |
| A study on KOSPI 200 direction forecasting using XGBoost model† DW Hah, YM Kim, JJ Ahn The Korean Data & Information Science Society 30 (3), 655-669, 2019 | 25 | 2019 |
| Using neural networks to forecast volatility for an asset allocation strategy based on the target volatility Y Kim, D Enke Procedia Computer Science 95, 281-286, 2016 | 17 | 2016 |
| Instance selection using genetic algorithms for an intelligent ensemble trading system Y Kim, D Enke Procedia computer science 114, 465-472, 2017 | 13 | 2017 |
| A dynamic target volatility strategy for asset allocation using artificial neural networks Y Kim, D Enke The Engineering Economist 63 (4), 273-290, 2018 | 12 | 2018 |
| Intelligent ensemble deep learning system for blood glucose prediction using genetic algorithms DY Kim, DS Choi, AR Kang, J Woo, Y Han, SW Chun, J Kim Complexity 2022 (1), 7902418, 2022 | 11 | 2022 |
| Forecasting the KOSPI 200 stock index based on LSTM Autoencoder JH Yang, Y Kim, KJ Oh Quantitative Bio-Science 39 (2), 101-109, 2020 | 8 | 2020 |
| A relative value trading system based on a correlation and rough set analysis for the foreign exchange futures market S Lee, D Enke, Y Kim Engineering Applications of Artificial Intelligence 61, 47-56, 2017 | 8 | 2017 |
| Intelligent stock market instability index: Application to the Korean stock market YM Kim, SK Han, TY Kim, KJ Oh, Z Luo, C Kim Intelligent Data Analysis 19 (4), 879-895, 2015 | 5 | 2015 |
| Using change-point detection to identify structural changes in stock market: application to Russell 2000 SY Jeon, HS Ryou, Y Kim, KJ Oh Quantitative Bio-Science 39 (1), 61-69, 2020 | 4 | 2020 |
| An intelligent early warning system for forecasting abnormal investment trends of foreign investors KJ Oh, YM Kim Journal of the Korean Data and Information Science Society 24 (2), 223-233, 2013 | 4 | 2013 |
Kyong Joo OhYonsei UniversityE-mail megerősítve itt: yonsei.ac.kr