• Journal of Engineering Education Research
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• v.18 no.1
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• pp.54-60
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• 2015

The purposes of the study are to investigate the effects on academic of the self-directed learning and in-depth interviewing program in engineering underachieved students. 17 students participated in program and the grade points average(GPA) of participants are less than 2.5. First, we focus on the change of academic achievement after the self-directed learning and in-depth interviewing program. According to results, it is very effective not only in improving academic achievement of the participation subject but also in increasing GPA. Second, the pre-survey and the post-survey were conducted to the participants. We found some facts from the difference between the pre and post surveys. The expectation and satisfaction about learning have improved after self-directed learning, and the participants' recognition showed the meaningful change in important factors about learning.

• The Journal of Bigdata
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• v.3 no.1
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• pp.63-71
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• 2018

In the last five years (2013~2017), we analyzed what factors influenced Korean films that have surpassed 10 million viewers in the Korean movie industry, where the total number of moviegoers is over 200 million. In general, many people consider the number of screens and ratings as important factors that affect the audience's success. In this study, four additional factors, including the number of screens and ratings, were established to establish a hypothesis and correlate it with the presence of 10 million spectators through big data analysis. The results were significant, with 91 percent accuracy in predicting 10 million viewers and 99.4 percent accuracy in estimating cumulative attendance.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.329-345
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• 2021

Recommender systems use data from customers to suggest personalized products. The recommender systems can be categorized into three cases; collaborative filtering, contents-based filtering, and hybrid recommender system that combines the first two filtering methods. In this work, we introduce and compare deep learning-based recommender system using autoencoder. Autoencoder is an unsupervised deep learning that can effective solve the problem of sparsity in the data matrix. Five versions of autoencoder-based deep learning models are compared via three real data sets. The first three methods are collaborative filtering and the others are hybrid methods. The data sets are composed of customers' ratings having integer values from one to five. The three data sets are sparse data matrix with many zeroes due to non-responses.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.401-404
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• 2022

한국 문학이 세계로 뻗어나감에 따라 해외 시장에서 자리를 잡는 것이 중요해진 시점이다. 본 연구에서는 2016 년도부터 2020 년도까지 최근 5 년간 해외 출간된 도서들 중에서 굿셀러로 분류되는 누적 5 천부 이상 판매 여부를 예측하고자 했다. 굿셀러로 분류되는 도서는 전체 번역 도서 중 적은 비율을 차지하여 데이터 불균형이 발생하였으며, 본 연구에서는 SMOTE 기법과 앙상블 알고리즘을 적용하여 데이터 불균형 문제를 해결하였다. 그 결과, 데이터 클래스 비율이 1:1 에 가까울수록 성능 개선 효과가 나타났으며 LightGBM 모델이 99.83%의 AUC 값을 얻어 다른 앙상블 알고리즘에 비해 가장 좋은 예측 성능을 보임을 검증하였다. 또한 누적 5 천부 이상 판매 여부 예측에 있어 큰 영향을 미치는 변수로는 작가가 가장 중요한 요인으로 나타났으며 출간 국가, 그리고 평점 평균, 평점 참여자 수 같은 온라인 요인도 판매 예측에 유의미한 변수로 나타난 것을 확인할 수 있었다.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.71-89
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• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.

• The Journal of Korean Society for Radiation Therapy
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• v.18 no.2
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• pp.97-103
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• 2006

The purpose of this study is to understand preliminary education. level of understanding and the degrees of educational requirement for cancer patients on radiotherapy and to present the preliminary data to development of effective and practical patients treatment programs. Based on the abovementioned results of this study. Relationship betweendegrees of knowledge and demand for educational requirement for patients who are undertaking radiotherapy could be varied with different factors such as educational background, ages, regions of treatment, experience of symptoms. In general, patients do not have enough information, on the other hand, have very high demand for educational requirement. Customized education patients by patients would not be possible in reality. However, if we could provide standard for patients and establish systematic sessions during treatment based on this study, more and better patients satisfaction and results of treatments could be achieved.

• Journal of Technology Innovation
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• v.17 no.2
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• pp.55-80
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• 2009

Technology innovation activity plays a pivotal role in constructing the entrance barrier for other firms and making process improvement and new product. and these activities give a profit increase and growth to firms. Thus, technology innovation activity can reduce the default risk of firms. However, technology innovation activity can also increase the firm's default risk because technology innovation activity requires too much investment of the firm's resources and has the uncertainty on success. The purpose of this study is to examine the effect of technology innovation activity on the default risk of firms. This study's sample consists of manufacturing firms listed on the Korea Securities Market and The Kosdaq Market from January 1,2000 to December 31, 2008. This study makes use of R&D intensity as an proxy variable of technology innovation activity. The default probability which proxies the default risk of firms is measured by the Merton's(l974) debt pricing model. The main empirical results are as follows. First, from the empirical results, it is found that technology innovation activity has a negative and significant effect on the default risk of firms independent of the Korea Securities Market and Kosdaq Market. In other words, technology innovation activity reduces the default risk of firms. Second, technology innovation activity reduces the default risk of firms independent of firm size, firm age, and credit score. Third, the results of robust analysis also show that technology innovation activity is the important factor which decreases the default risk of firms. These results imply that a manager must show continuous interest and investment in technology innovation activity of one's firm. And a policymaker also need design an economic policy to promote the technology innovation activity of firms.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.1-17
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• 2023

With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.219-239
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• 2019

As the importance of providing customized services to individuals becomes important, researches on personalized recommendation systems are constantly being carried out. Collaborative filtering is one of the most popular systems in academia and industry. However, there exists limitation in a sense that recommendations were mostly based on quantitative information such as users' ratings, which made the accuracy be lowered. To solve these problems, many studies have been actively attempted to improve the performance of the recommendation system by using other information besides the quantitative information. Good examples are the usages of the sentiment analysis on customer review text data. Nevertheless, the existing research has not directly combined the results of the sentiment analysis and quantitative rating scores in the recommendation system. Therefore, this study aims to reflect the sentiments shown in the reviews into the rating scores. In other words, we propose a new algorithm that can directly convert the user 's own review into the empirically quantitative information and reflect it directly to the recommendation system. To do this, we needed to quantify users' reviews, which were originally qualitative information. In this study, sentiment score was calculated through sentiment analysis technique of text mining. The data was targeted for movie review. Based on the data, a domain specific sentiment dictionary is constructed for the movie reviews. Regression analysis was used as a method to construct sentiment dictionary. Each positive / negative dictionary was constructed using Lasso regression, Ridge regression, and ElasticNet methods. Based on this constructed sentiment dictionary, the accuracy was verified through confusion matrix. The accuracy of the Lasso based dictionary was 70%, the accuracy of the Ridge based dictionary was 79%, and that of the ElasticNet (${\alpha}=0.3$) was 83%. Therefore, in this study, the sentiment score of the review is calculated based on the dictionary of the ElasticNet method. It was combined with a rating to create a new rating. In this paper, we show that the collaborative filtering that reflects sentiment scores of user review is superior to the traditional method that only considers the existing rating. In order to show that the proposed algorithm is based on memory-based user collaboration filtering, item-based collaborative filtering and model based matrix factorization SVD, and SVD ++. Based on the above algorithm, the mean absolute error (MAE) and the root mean square error (RMSE) are calculated to evaluate the recommendation system with a score that combines sentiment scores with a system that only considers scores. When the evaluation index was MAE, it was improved by 0.059 for UBCF, 0.0862 for IBCF, 0.1012 for SVD and 0.188 for SVD ++. When the evaluation index is RMSE, UBCF is 0.0431, IBCF is 0.0882, SVD is 0.1103, and SVD ++ is 0.1756. As a result, it can be seen that the prediction performance of the evaluation point reflecting the sentiment score proposed in this paper is superior to that of the conventional evaluation method. In other words, in this paper, it is confirmed that the collaborative filtering that reflects the sentiment score of the user review shows superior accuracy as compared with the conventional type of collaborative filtering that only considers the quantitative score. We then attempted paired t-test validation to ensure that the proposed model was a better approach and concluded that the proposed model is better. In this study, to overcome limitations of previous researches that judge user's sentiment only by quantitative rating score, the review was numerically calculated and a user's opinion was more refined and considered into the recommendation system to improve the accuracy. The findings of this study have managerial implications to recommendation system developers who need to consider both quantitative information and qualitative information it is expect. The way of constructing the combined system in this paper might be directly used by the developers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.618-628
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• 2016

This study was conducted to identify the influence of clinical practice stress and communication skills on the ego-resilience of nursing students. The subjects were 122 nursing college seniors in B city who have experience in clinical practice. Data were collected from March 23 to 30, 2015, and analyzed by the t-test, ANOVA, Scheffe's test, Pearson's correlation coefficients and multiple regression. It was found that there were significant differences in the ego-resilience depending on the gender, satisfaction with nursing major and relationships among fellow students during clinical practice. There was a significant positive correlation between ego-resilience and communication skills. The meaningful variables which influence the ego-resilience were communication skills, satisfaction with nursing major, relationships among fellow students during clinical practice and gender. These factors were responsible for 38.9% of the total variance in the ego-resilience of the nursing students, and communication skills were the most influential factor. In conclusion, to increase the ego-resilience of nursing students, it is necessary to develop effective communication training programs and strategies to improve their satisfaction with the nursing major and relationships among fellow students during clinical practice.