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하성호(Sung Ho Ha),양정원(Jeongwon Yang),민지홍(Jihong Min) 한국경영과학회 2009 韓國經營科學會誌 Vol.34 No.2
The recent economic crisis not only reduces the profit of department stores but also incurs the significance losses caused by the increasing late-payment rate of credit cards. Under this pressure, the scope of credit prediction needs to be broadened from the simple prediction of whether this customer has a good credit or not to the accurate prediction of how much profit can be gained from this customer. This study classifies the delinquent customers of credit card in a Korean department store into homogeneous clusters. Using this information, this study analyzes the repayment patterns for each cluster and develops the credit prediction system to manage the delinquent customers. The model presented by this study uses Kohonen network, which is one of artificial neural networks of data mining technique, to cluster the credit delinquent customers into clusters. Cox proportional hazard model is also used, which is one of survival analysis used in medical statistics, to analyze the repayment patterns of the delinquent customers in each cluster. The presented model estimates the repayment period of delinquent customers for each cluster and introduces the influencing variables on the repayment pattern prediction. Although there are some differences among clusters, the variables about the purchasing frequency in a month and the average number of installment repayment are the most predictive variables for the repayment pattern. The accuracy of the presented system reaches 97.5%.
깊은 신경망 훈련 및 규제 기법을 이용한 요추 환자의 CT 영상에서 요추 분할
장민혜(Min Hye Chang),양정원(Jeongwon Yang),차보경(Bo Kyung Cha),김진성(Jin-Sung Kim),김은(Eun Kim),Yanting Liu,이경희(Kyeong-Hee Lee) 대한전자공학회 2021 대한전자공학회 학술대회 Vol.2021 No.6
In this paper, DNN training and regularization techniques have been explored for lumbar vertebrae segmentation in CT images of patients with degenerative lumbar spine disease. Combinations of loss function, learning rate schedule, normalization, dropout, moving average, weight decay, and squeeze-and-excitation were empirically tested. The results confirmed that the DNN with optimized training and regularization techniques can improve lumbar vertebrae segmentation without complex structural changes.