• The Journal of Bigdata
• /
• v.4 no.2
• /
• pp.175-183
• /
• 2019

Drop-out issue is one of the challenges of cyber university. There are about 130,000 students enrolled in cyber universities, but the dropout rate is also very high. To lower the dropout rate, cyber universities invest heavily in learning analytics. Some cyber universities analyze the possibility of dropout and actively support students who are more likely to drop out. The purpose of this paper is to identify the learning data affecting the dropout prediction index. As a result of the analysis, it is confirmed that number of lessons(progress), credits, achievement and leave of absence have a significant effect on dropout rate. It is necessary to increase the accuracy of the prediction model through post-test on the student dropout prediction index.

• The Korean Journal of Applied Statistics
• /
• v.29 no.7
• /
• pp.1257-1270
• /
• 2016

This study deals with the prediction of the total number of movie audiences as a measure for the box office. Prediction is performed by classification techniques of data mining such as decision tree, multilayer perceptron(MLP) neural network model, multinomial logit model, and support vector machine over time such as before movie release, release day, after release one week, and after release two weeks. Predictors used are: online word-of-mouth(OWOM) variables such as the portal movie rating, the number of the portal movie rater, and blog; in addition, other variables include showing the inherent properties of the film (such as nationality, grade, release month, release season, directors, actors, distributors, the number of audiences, and screens). When using 10-fold cross validation technique, the accuracy of the neural network model showed more than 90 % higher predictability before movie release. In addition, it can be seen that the accuracy of the prediction increases by adding estimates of the final OWOM variables as predictors.

• The Korean Journal of Applied Statistics
• /
• v.26 no.3
• /
• pp.441-452
• /
• 2013

This study analyzed the relationship between key film and a box office record success factors based on movies released in the first quarter of 2013 in Korea. An over-fitting problem can happen if there are too many explanatory variables inserted to regression model; in addition, there is a risk that the estimator is instable when there is multi-collinearity among the explanatory variables. For this reason, optimal variable selection based on high explanatory variables in box-office performance is of importance. Among the numerous ways to select variables, LASSO estimation applied by a generalized linear model has the smallest prediction error that can efficiently and quickly find variables with the highest explanatory power to box-office performance in order.

• Journal of Digital Convergence
• /
• v.17 no.1
• /
• pp.123-130
• /
• 2019

One of the most commonly used methods of web recommendation techniques is collaborative filtering. Many studies on collaborative filtering have suggested ways to improve accuracy. This study proposes a method of movie recommendation using Word2Vec and an ensemble convolutional neural networks. First, in the user, movie, and rating information, construct the user sentences and movie sentences. It inputs user sentences and movie sentences into Word2Vec to obtain user vectors and movie vectors. User vectors are entered into user convolution model and movie vectors are input to movie convolution model. The user and the movie convolution models are linked to a fully connected neural network model. Finally, the output layer of the fully connected neural network outputs forecasts of user movie ratings. Experimentation results showed that the accuracy of the technique proposed in this study accuracy of conventional collaborative filtering techniques was improved compared to those of conventional collaborative filtering technique and the technique using Word2Vec and deep neural networks proposed in a similar study.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.5
• /
• pp.1-8
• /
• 2023

Cross-domain recommendation is a method that shares related user information data and item data in different domains. It is mainly used in online shopping malls with many users or multimedia service contents, such as YouTube or Netflix. Through K-means clustering, embeddings are created by performing clustering based on user data and ratings. After learning the result through a transformer network, user satisfaction is predicted. Then, items suitable for the user are recommended using a transformer-based recommendation model. Through this study, it was shown through experiments that recommendations can predict cold-start problems at a lesser time cost and increase user satisfaction.

• The Journal of Information Systems
• /
• v.16 no.1
• /
• pp.1-21
• /
• 2007

신용평점을 위한 부도예측의 분류 문제를 다루는데 있어서 통계적 판별분석 및 인공신경망 및 유전자알고리즘 등을 이용한 데이터 마이닝의 방법들이 일반적으로 고려되어왔다. 이 연구에서는 수리계획법을 응용하여 classification gap을 고려한 이단계 수리계획 접근방법을 신용평가에 적용하는 방법론을 제안하여 수리계획법을 통한 신용평가모형 구축의 가능성을 제시한다. 1단계에서는 선형계획법을 이용해서 대출 신청자에게 대출을 허가할 것 인지의 여부를 결정하게 되는 대출 심사 filtering으로의 적용단계이고, 2단계에서는 정수계획법을 이용하여 오분류 비용이 최소가 되도록 하는 판별점수를 찾는 과정으로 모형을 구성한다. 개인 대출 신청자의 데이터(German Credit Data)에 대하여 피셔의 선형 판별함수, 로지스틱 회귀모형 및 기존의 수리계획 기법들과의 비교를 통해서 제안된 모델의 성능을 평가한다. 이단계 수리계획 접근법의 평가 결과를 통하여 신용평가모형에의 적용가능성을 기존 통계적인 접근방법 및 수리계획 접근법과 비교하여 제시하고 있다.

• Proceedings of the Korean Society for Quality Management Conference
• /
• 2010.04a
• /
• pp.447-447
• /
• 2010

조직이나 기업에서 높은 수준의 직무 성과는 뛰어난 지적 능력과 더불어 다른 요소들의 영향에 의해 결정된다고 광범위하게 인식되고 있다. 따라서 인사선발시 인지적인 능력뿐만이 아니라 성격과 같은 비인지적 능력을 평가하는 시도가 활발히 이루어지고 있다. 본 연구는 이를 실증적으로 검증하고자 수행되었다. 즉, 본 연구에서는 서울지역에서 재학 중인 대학생 (n=260) 을 대상으로 하여 인지적 능력은 입학성적으로, 비인적 능력은 Big Five 성격 특성으로, 직무성과는 학업성과(예, 학업평점, 대학 및 학과에 대한 만족도, 동아리 활동, 등) 로 각각 측정하여 이들의 관계성을 살펴보았다. 연구 결과, 인지적 능력과 더불어 Big Five 성격 특성 중 성실성과 개방성이 다양한 학업성과와 상관관계를 보여주었다. 본 연구는 추가적으로, 학업성과 중 동아리 활동이 대학생활의 만족도와 상관관계가 있음을 보여주고 있다. 이러한 결과는 인사선발 과정에서 지원자의 잠재적 직무성과를 예측할 때 지적 능력 같은 인지적 측면뿐만이 아니라 성격과 같은 비인지적 측면을 동시에 고려해야 한다는 실무적 시사점을 제시하고 있다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2016.10a
• /
• pp.591-594
• /
• 2016

소비자의 선호도 및 여론을 정량적인 방법으로 분석하기 위해 비정형 데이터의 분석은 필수적인 요소가 되고 있다. 하지만 비정형 데이터는 언어의 구조 및 모호성 등으로 인해 분석하기 어려운 형태이다. 따라서 본 연구는 최근 각광받고 있는 인공신경망, 특히 그 중에서도 순환 신경망의 한 모델인 Deep LSTM을 이용하여 비정형 데이터를 분석하고 이를 활용하여 어순 및 어감 등의 언어의 구조적 문제에도 효과적인 정략적 모델을 설계하여 학습하고 이를 기존의 인공신경망 모델과 비교 분석하고자 한다.

• Journal of Digital Convergence
• /
• v.15 no.7
• /
• pp.307-317
• /
• 2017

This study was to identify the factors influencing college life adjustment and sub-scales of nursing students. Self- report questionnaire surveys were conducted toward 282 freshman nursing students to measure college life adjustment, psychological well-being, emotional intelligence, and self-efficacy. Data were collected from September 22 through October 7, 2016. This study was analyzed using SPSS Win 18.0. The average mean of college life adjustment was 3.36 and academic activity was the highest, followed by individual psychology, social experience, Interpersonal relationship, and career preparation. A correlation of psychological well-being, emotional intelligence, self-efficacy, college life adjustment and sub-scales showed positive correlation. The strongest predictor of college life adjustment was a self-efficacy. And sub-scales, the strongest predictor of academic activity was academic achievement, career preparation was self-efficacy, individual psychology and social experience was emotional intelligence, and Interpersonal relationship was psychological well-being. An intervention program which includes these significant variables of subjects is essential to improve of college life adjustment.

• Journal of Digital Convergence
• /
• v.18 no.12
• /
• pp.525-531
• /
• 2020

In today's IT environment where various pieces of information are distributed in large volumes, recommendation systems are in the spotlight capable of figuring out users' needs fast and helping them with their decisions. The current recommendation systems, however, have a couple of problems including that user preference may not be reflected on the systems right away according to their changing tastes or interests and that items with no relations to users' preference may be recommended, being induced by advertising. In an effort to solve these problems, this study set out to propose a Fuzzy-AHP-based movie recommendation system by applying the BRNN(Bidirectional Recurrent Neural Network) language model. Applied to this system was Fuzzy-AHP to reflect users' tastes or interests in clear and objective ways. In addition, the BRNN language model was adopted to analyze movie-related data collected in real time and predict movies preferred by users. The system was assessed for its performance with grid searches to examine the fitness of the learning model for the entire size of word sets. The results show that the learning model of the system recorded a mean cross-validation index of 97.9% according to the entire size of word sets, thus proving its fitness. The model recorded a RMSE of 0.66 and 0.805 against the movie ratings on Naver and LSTM model language model, respectively, demonstrating the system's superior performance in predicting movie ratings.