[ Generative Adversarial Networks]

• Sungho Suh, Paul Lukowicz, Yong Oh Lee, "Discriminative feature generation for classification of imbalanced data", Pattern Recognition 122, 108302, 2022

• Sungho Suh, Haebom Lee, Paul Lukowicz, Yong Oh Lee, "CEGAN: Classification Enhancement Generative Adversarial Networks for unraveling data imbalance problems", Neural Networks 133, 69-86, 2021

[Medical Image/Data Analysis]

• Kim, Hyun-Bum, Jaemin Song, Seho Park, and Yong Oh Lee, "Laryngeal Disease Classification using Voice Data: Octave-band vs. Mel-Frequency Filters." (2024) (accepted in Heliyon)

• Yong Oh Lee, Hana Kim, Yeong Woong Chung, Won-Kyung Cho , Jungyul Park and Ji-Sun Paik, "Segmentation-Based Measurement of Orbital Structures: Achievements in Eyeball Volume Estimation and Barriers in Optic Nerve Analysis." (2024). (accepted in Diagnostics)

• Jinyoung Kim, Min-Hee Kim, Dong-Jun Lim, Hankyeol Lee, Jae Jun Lee, Mee Kyoung Kim, Hyuk-Sang Kwon, Ki-Ho Song, So Lyung Jung, Tae-Jung Kim, Yong Oh Lee*, Baek Ki-Hyun, "Deep Learning Technology for Classification of Thyroid Nodules with Ultrasound Images: Multi-view-based Siamese Convolutional Neural Network." (2024). (accepted in Endocrinology and Metabolism)

• Kim, Hyun-Bum, Jaemin Song, Seho Park, and Yong Oh Lee. "Classification of laryngeal diseases including laryngeal cancer, benign mucosal disease, and vocal cord paralysis by artificial intelligence using voice analysis." (2023).

• Chung, Yeon Woong, Dong Gyun Kang, Yong Oh Lee, and Won-Kyung Cho. "Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography." JoVE (Journal of Visualized Experiments) 189 (2022): e64500.

• Sungho Suh, Sojeong Cheon, Wonseo Choi, Yeon Woong Chung, Won-Kyung Cho, Ji-Sun Paik, Sung Eun Kim, Dong-Jin Chang, Yong Oh Lee, "Supervised segmentation with domain adaptation for small sampled orbital CT images", accepted in Journal of Computational Design and Engineering

[Chemical Property Prediction]

• Sangrak Lim, Yong Oh Lee, Juyong Yoon, Young Jun Kim, "Affinity prediction using deep learning based on SMILES input for D3R Grand Challenge 4", accepted in Journal of Computer Aided Molecular

• Yong Oh Lee and Young Jun Kim, “The Effect of Resampling on Data‐Imbalanced Conditions for Prediction towards Nuclear Receptor Profiling Using Deep Learning”, Molecular informatics 2020

[Fault diagnosis and prognostics]

• Sungho Suh, Paul Lukowicz, Yong Oh Lee, "Generalized multiscale feature extraction for remaining useful life prediction of bearings with generative adversarial networks", Knowledge-Based Systems 237, 107866, 2022

• Sungho Suh, Joel Jang, Seungjae Won, Mayank Shekhar Jha, Yong Oh Lee, "Supervised Health Stage Prediction Using Convolutional Neural Networks for Bearing Wear", Sensors 20 (20), 5846

• Sungho Suh, Haebom Lee, Jun Jo, Paul Lukowicz, and Yong Oh Lee, “Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis”, Journal of Applied Science, vol. 9, no. 4 (2019): 746, 2019

[Miscellaneous]

• 오세준, 임해미, 김성경, 이용오, 최여선, 김민지, 송민주, 김예진, "수학 탐구활동과 평가의 연계를 위한 인공지능 기반 공학 도구 개발 연구", 한국학교수학회논문집 제28권 제1호, 47-64.

• Kim, Dongkyun, Yong Oh Lee, Changhyun Jun, and SeokKoo Kang. "Understanding the Way Machines Simulate Hydrological Processes-A Case Study of Predicting Fine-scale Watershed Response on a Distributed Framework." IEEE Transactions on Geoscience and Remote Sensing (2023).

• Suh, Sungho, Jihun Kim, Paul Lukowicz, and Yong Oh Lee. "Two-stage generative adversarial networks for binarization of color document images." Pattern Recognition 130 (2022): 108810. 

• Jin Woo Moon, Sung Kwon Jung, Yong Oh Lee, Sangsun Choi, “Prediction Performance of an Artificial Neural Network Model for the Amount of Cooling Energy Consumption in Hotel Rooms”. Energy 8, 8226-9243, 2015

• Jaemin Song, Yong Oh Lee, Seho Park, Youn Kyu Lee, Hansang Park, Hyun-Bum Kim, “Enhancing Vocal-Based Laryngeal Cancer Screening with Additional Patient Information and Voice Signal Embedding,” 2023 IEEE international conference on big data (big data), Accepted

• Hana Kim, Yong Oh Lee, Changsoo Ok, Dongkyun Kim, Seugyup Baek, “Embedding Climate Dynamics and Prediction with Deep Learning for Wind Power Forecasting: Short-Term to Long-Term Perspective.” 2023 IEEE international conference on big data (big data), Accepted

• Lim, Sangrak, and Yong Oh Lee. "Predicting chemical properties using self-attention multi-task learning based on SMILES representation." In 2020 25th International Conference on Pattern Recognition (ICPR), pp. 3146-3153. IEEE, 2021.

• Suh, Sungho, Paul Lukowicz, and Yong Oh Lee. "Fusion of global-local features for image quality inspection of shipping label." In 2020 25th International Conference on Pattern Recognition (ICPR), pp. 2643-2649. IEEE, 2021.

• Suh, Sungho, Sojeong Cheon, Dong-Jin Chang, Deukhee Lee, and Yong Oh Lee. "Sequential lung nodule synthesis using attribute-guided generative adversarial networks." In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VI 24, pp. 402-411. Springer International Publishing, 2021.

• Suh, Sungho, Haebom Lee, Yong Oh Lee, Paul Lukowicz, and Jongwoon Hwang. "Robust shipping label recognition and validation for logistics by using deep neural networks." In 2019 IEEE International Conference on Image Processing (ICIP), pp. 4509-4513. IEEE, 2019.

• Lee, Yong Oh, Jun Jo, and Jongwoon Hwang. "Application of deep neural network and generative adversarial network to industrial maintenance: A case study of induction motor fault detection." In 2017 IEEE international conference on big data (big data), pp. 3248-3253. IEEE, 2017.