2024


E. Jo, H. You, JH. Kim, YM Kim, S. Song,  & HJ. Joo*. Fine-Tuned Bidirectional Encoder Representations From Transformers Versus ChatGPT for Text-Based Outpatient Department Recommendation: Comparative Study. JMIR Formative Research 8(3):e47814, 2024. 

SW. Kong, YM. Kim*, & SH. Cho. REO: Resource efficient object detection in embedded system using bitstreams. Multimedia Tools and Applications, 2024.  [HTML]

E. Jo, S. Song, JH. Kim, S. Lim, JH. Kim, JJ. Cha, YM Kim, & HJ. Joo*. Assessing GPT-4’s Performance in Delivering Medical Advice: Comparative Analysis With Human Experts. JMIR Medical Education 10:e51282, 2024.  [HTML]
 

2023


MG. Kim, Y. Kim, & YM. Kim.* Structural sensitivity to reliability of flexible AMOLED modules using mechanical simulation and machine learning. Organic Electronics125, 2023.  [HTML]

김용우, 김대영, 서현희, 김영민*. Sentence BERT를 이용한 내용 기반 국문 저널추천 시스템지능정보연구 29(3), 2023.  [HTML]

고강욱, 옥창훈, 김영민*. Supply Chain에서 인공지능의 활용에 관한 연구 :머신러닝 기반의 분류 기법 활용한국경영공학회지, 2023. [HTML]

김용우, 최진선, 김영민*. 국내 생명보험시장의 효율성 분석: 인터넷 및 방카슈랑스 전문 생보사를 중심으로연세경영연구, 2023. [HTML]


 

2022


Y. Kim, JH. Kim, YM. Kim, S. Song, HJ. Joo*. Predicting medical specialty from text based on a domain-specific pre-trained BERTInternational Journal of Medical Informatics, 2022. [HTML]

김민구, 김용우, 정태현, 김영민*. Organic Light-Emitting Diodes 디스플레이 기술의 특허 동향과 기술적 가치에 관한 탐색적 연구지능정보연구 28(4), 2022. [HTML]

김정희, 김영민*. 특허정보의 NLP 분석을 통한 R&D 계획수립 방안 연구: 디스플레이 기술 분석을 중심으로. 한국산업융합학회 논문집, 2022. [HTML]

MG. Kim, YM. Kim & SY. Han*. Mechanical Reliability Prediction of Foldable Displays Using Subcritical Crack Growth in Siloxane‑Based Cover Window by Two‑Point Bending Test. International Journal of Precision Engineering and Manufacturing, 2022. [HTML]

Y. Kim, JH. Kim, JM. Lee, MJ. Jang, YJ. Yum, S. Kim, U. Shin, YM. Kim, HJ. Joo, S. Song*. A pre-trained BERT for Korean medical natural language processingSCIENTIFIC REPORTS, 2022. [HTML]

김용우, 김민구, 김영민*. 기계학습을 활용한 특허수명 예측 및 영향요인 분석. 지능정보연구, 28(2), 2022. [HTML]

YM. Kim*, TH Lee, SO Na. Constructing Novel Datasets for Intent Detection and NER in a Korean Healthcare Advice System: Guidelines and Empirical Results. Applied Intelligence, 2022. [HTML]


안세환, 김영민*. 미국 프로농구(NBA)의 플레이오프 진출에 영향을 미치는 주요 변수 예측: 3점과 턴오버 속성을 중심으로. 지능정보연구, 28(1), 2022. [HTML]

고강욱, 안세환, 김영민*. 스마트 물류에서 인공지능 활용에 관한 연구, 한국경영공학회지, 27(1), 2022. [HTML]
 

김용우, 김영민*. 기계학습을 활용한 온라인게임 매치메이킹 개선방안. 한국게임학회 논문지, 22(1), 2022. [HTML]

 

2021


김용우, 김영민*. 기계학습을 활용한 게임승패 예측 및 변수중요도 산출을 통한 전략방향 도출. 한국게임학회 논문지, 21(4), 2021. [HTML]

이태훈, 김영민*, 정은지, 나선옥. 의료 조언을 위한 질문 의도 인식: 학습 데이터 구축 및 의도 분류. 정보과학회논문지, 48(8), 2021. [PDF] 

YM. Kim, SJ. Shin* and HW. Jo. Predictive Modeling for Machining Power Based on Multi-source Transfer Learning in Metal Cutting. International Journal of Precision Engineering and Manufacturing-Green Technology, 2021. [HTML] 


 

2020


YM. Kim, TH Lee. Korean Clinical Entity Recognition from Diagnosis Text using BERT. BMC Medical Informatics and Decision Making supplement, 2020. [HTML] 

김영민, 신승준*, 조해원. 전이학습 기반 가공동력 예측 모델링 방법. 대한산업공학회지, 2020. [HTML] 

G. Jo, YM. Kim, DW. Jun* and E. Jeong*. Pitch Processing Can Indicate Cognitive Alterations in Chronic Liver Disease: An fNIRS Study, Frontiers in Human Neuroscience, 2020. [HTML] 


 

2019


YM. Kim. Feature visualization in comic artist classification using deep neural networks. Journal of Big Data, 2019. [HTML] 

SJ Shin, YM Kim, P Meilanitasari. A Holonic-Based Self-Learning Mechanism for Energy-Predictive Planning in Machining Processes. Processes, 2019. [HTML] 

 

2018


C Kim, J Lee, T Han, YM Kim*. A hybrid framework combining background subtraction and deep neural networks for rapid person detection. Journal of Big Data, 2018. [HTML] 

김영민, 이지영, 윤일로, 한택진, 김철연. 배경 차분과 CNN 기반의 CCTV 객체 검출. 정보과학회 컴퓨팅의 실제 논문지, 2018. [PDF] 

 

2016


T. Kim, MN. Hwang, YM. Kim, and DH. Jeong. Entity Resolution Approach of Data Stream Management Systems. Wireless Personal Communications, 2016. [PDF] 

 

2015


YM. Kim, SK. Song, S. Shin, CN. Seon, S. Hong, and H. Jung. On Designing an Effective Training Set for Information Extraction. Computer Science and its Applications, Lecture Notes in Electrical Engineering, vol 330. Springer, Berlin, Heidelberg. 2015. [HTML] 

SK. Song, S. Shin, YM. Kim, CN. Seon, S. Hong, and H. Jung. Inspecting Retrieval Engines Based on Term’s Weight. Computer Science and its Applications. Lecture Notes in Electrical Engineering, vol 330. Springer, Berlin, Heidelberg. 2015 [HTML] 

YM Kim, MY Hwang, TH Kim, CH. Jeong, and DH. Jeong. Big data mining for natural disaster analysis. Journal of the Korean Data & Information Science Society, 26(5), 1105-1115, 2015. [HTML] 

 

2014


D. Seo, S. Shin, Y. Kim, H. Jung, and S. Song. Dynamic hilbert curve-based b+-tree to manage frequently updated data in big data applications. Life Science Journal, 2014 [PDF] 

P. Bellot, L. Bonnefoy, V. Bouvier, F. Duvert, and Y. Kim. Large scale text mining approaches for information retrieval and extraction. In: Faucher C., Jain L. (eds) Innovations in Intelligent Machines-4. Studies in Computational Intelligence, vol 514. Springer, Cham, 2014 [HTML] 

 

 

2010


JF Pessiot, YM Kim, MR Amini, P Gallinari. Improving document clustering in a learned concept space. Information processing & management, 2010 [HTML] 

YM. Kim, JF. Pessiot, MR. Amini, and P. Gallinari. Apprentissage d'un espace de concepts de mots pour une nouvelle représentation des données textuelles. Document numérique, 2010 [HTML]