| Autoencoders for improving quality of process event logs HTC Nguyen, S Lee, J Kim, J Ko, M Comuzzi Expert Systems with Applications 131, 132-147, 2019 | 50 | 2019 |
| Encoding resource experience for predictive process monitoring J Kim, M Comuzzi, M Dumas, FM Maggi, I Teinemaa Decision Support Systems 153, 113669, 2022 | 22 | 2022 |
| A diagnostic framework for imbalanced classification in business process predictive monitoring J Kim, M Comuzzi Expert Systems with Applications 184, 115536, 2021 | 13 | 2021 |
| P‐193L: Late‐News Poster: Optimal Tone Curve Characteristics of Transparent Display for Preferred Image Reproduction Y Kwak, Y Baek, J Kim SID Symposium Digest of Technical papers 45 (1), 1123-1126, 2014 | 6 | 2014 |
| Stability metrics for enhancing the evaluation of outcome-based business process predictive monitoring J Kim, M Comuzzi IEEE Access 9, 133461-133471, 2021 | 4 | 2021 |
| Business Process Intelligence Challenge 2019: Process discovery and deviation analysis of purchase order handling process J Kim, J Ko, S Lee Republic of Korea, 2019 | 2 | 2019 |
| 비즈니스 프로세스 이상 패턴 별 이상 탐지 안정성 측정 방법 J Kim, M Comuzzi 한국경영과학회 학술대회논문집, 3049-3066, 2021 | | 2021 |
| Model-agnostic improvement of predictive business process monitoring J Kim Ulsan National Institute of Science and Technology, 2021 | | 2021 |
| Enhancing the Quality of Predictions in Predictive Business Process Monitoring J Kim CEUR-WS, 2019 | | 2019 |
| Handling imbalanced data in real-time prediction of activities in business processes J Kim, M Comuzzi 대한산업공학회 추계학술대회 논문집, 1162-1182, 2018 | | 2018 |
Marco ComuzziUlsan National Institute of Science and TechnologyVerified email at unist.ac.kr
