• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.101-108
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• 2020

Recommender system is a must-have feature of e-commerce, since it provides customers with convenience in selecting products. Collaborative filtering is a widely-used and representative technique, where it gives recommendation lists of products preferred by other users or preferred by the current user in the past. Recently, researches on the recommendation system using deep learning artificial intelligence technologies are actively being conducted to achieve performance improvement. This study develops a collaborative filtering based recommender system using restricted Boltzmann machines of the deep learning technology by utilizing user ratings. Moreover, a learning parameter update algorithm is proposed for learning efficiency and performance. Performance evaluation of the proposed system is made through experimental analysis and comparison with conventional collaborative filtering methods. It is found that the proposed algorithm yields superior performance than the basic restricted Boltzmann machines.

• Information and Communications Magazine
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• v.33 no.10
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• pp.49-56
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• 2016

본 논문에서는 패턴 인식 및 회귀 문제를 풀기 위해 쓰이는 기계학습에 대한 전반적인 이론과 설계방법에 대해 알아본다. 대표적인 기계학습 방법인 신경회로망과 기저벡터머신 등에 대해 소개하고 이러한 기계학습 모델을 선택하고 구축하는 데에 있어 고려해야 하는 문제점들에 대해 이야기 한다. 그리고 특징 추출 과정이 기계학습 모델의 성능에 어떻게 영향을 미치는지, 일반적으로 특징 추출을 위해 어떤 방법들이 사용되는 지에 대해 알아본다. 또한, 최근 새로운 패러다임으로 대두되고 있는 딥러닝에 대해 소개한다. 자가인코더, 제한볼츠만기계, 컨볼루션신경회로망, 회귀신경회로망과 같이 딥러닝 기술이 적용된 대표적인 신경망 구조에 대해 설명하고 기존의 기계학습 모델과 비교하여 딥러닝이 가지고 있는 특장점을 알아본다.

• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.248-253
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• 2019

Early diagnosis of mild cognitive impairment (MCI) can reduce the incidence of dementia. This study developed the MCI prediction model for the elderly in Korea. The subjects of this study were 3,240 elderly (1,502 men, 1,738 women) aged 65 and over who participated in the Korean Longitudinal Survey of Aging (KLoSA) in 2012. Outcome variables were defined as MCI prevalence. Explanatory variables were age, marital status, education level, income level, smoking, drinking, regular exercise more than once a week, average participation time of social activities, subjective health, hypertension, diabetes Respectively. The prediction model was developed using Restricted Boltzmann Machine (RBM) neural network. As a result, age, sex, final education, subjective health, marital status, income level, smoking, drinking, regular exercise were significant predictors of MCI prediction model of rural elderly people in Korea using RBM neural network. Based on these results, it is required to develop a customized dementia prevention program considering the characteristics of high risk group of MCI.