Wooju Kim
연세대학교 산업정보시스템공학과 · 컴퓨터과학
우진 김 교수의 연구실은 텍스트 분류, 실내 위치 추적, 의료 수요 예측, 금융 사기 탐지, 전자상거래 내재지능, 지식 그래프 기반 지식 추출 등 다학제적 데이터 기반의 지능형 정보 처리 기술을 핵심으로 연구를 진행하고 있습니다. 특히, 실시간 데이터와 센서 정보를 활용한 정밀한 예측 모델링 및 개인 맞춤형 분석 기법 개발에 초점을 맞추고 있으며, 응용 분야로는 의료, 금융, 스마트 시티 등 실생활 문제 해결에 기여하고자 합니다. 연구는 데이터 기반 의사결정 지원 시스템의 구현과 성능 향상을 목표로 하며, 기계학습, 자연어 처리, 지식 기반 시스템 기술을 융합적으로 적용하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Text Classification has received significant attention in recent years because of the proliferation of digital documents and is widely used in various applications such as filtering and recommendation. Consequently, many approaches, including those based on statistical theory, machine learning, and classifier performance improvement, have been proposed for improving text classification performance. Among these approaches, centroid-based classifier, multinomial naïve bayesian (MNB), support vecto
Ubiquitous indoor environments often contain substantial amounts of metal and other similar reflective materials that affect the propagation of radio frequency signals in important ways, causing severe multipath effects, including noise and interference, when measuring the signal strength between sender and receiver. To minimize the noise level, this study proposes advanced fusion filter (AFF) and improved fusion filter (IFF) using received signal strength indicator (RSSI) and link quality indic
Demand forecasting in the biomedical area is becoming more important because of radical changes in the macroeconomic environment and consumption trends. Moreover, the need for big data analysis on data from wireless sensor networks and social media is increasing because it shows not only the rapidly changing environmental data such as fine dust concentration but also the responses of potential customers that are expected to affect the demand for a medicine. Therefore, demand forecasting models b
Financial institutions face challenges of fraud due to an increased number of online transactions and sophisticated fraud techniques. Although fraud detection systems have been implemented to detect fraudulent transactions in online banking, many systems just use conventional rule-based approaches. Rule-based detection systems have a difficulty in updating and managing their rules and conditions manually. Additionally, generated from the few fraud cases, the rules are general rather than specifi
As more and more consumer-to-consumer e-marketplaces have been introduced with rapidly increasing transaction volumes, consumers now hope that computer technology will support their transaction processes in these electronic markets. To satisfy their expectations, many researchers have delved into developing intelligent agent systems to support customer-to-customer electronic commerce more efficiently. However, many of these researchers have focused only upon supporting simple negotiation for the
