• Korean Journal of Food Science and Technology
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• v.32 no.5
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• pp.1073-1078
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• 2000

Wet noodles were prepared with all purposed flour and whey powder, and effects of added whey powder on dough rheology and noodle quality were examined using texture profile analysis, sensory evaluation and colorimeter. The initial pasting temperature in amylograph and the maximum resistance in extensograph increased with the addition of whey powder, while the water absorption and the dough development time in farinograph and the extensibility in extensograph decreased. The weight and volume of cooked noodles decreased and turbidity of soup increased with the addition of whey powder. Sensory evaluation revealed that the texture, taste and overall acceptabillity of cooked noodle from 5% whey powder were significantly different from the others. Texture profile analysis of cooked noodles showed decrease of hardness, cohesiveness, chewiness and springiness with the increase of whey powder. L and a values of wet noodles decreased with the addition of whey powder and b value increased.

• Journal of the Korean Society of Food Culture
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• v.10 no.4
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• pp.241-245
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• 1995

The roasting condition and organoleptic characteristics in Cassia tora tea were investigated. Intact Cassia tora seeds were composed of water 11.6%, crude protein 13.1%, crude fat 4.4%, crude fiber 13.8%, N-free extract 47.2% and ash 4.9%. Organoleptic qualities in Cassia tora tea were sweetness, astringency, tartness, bitterness, roasted coffee like, roasted barley like and burnt smell. Organoleptic qualities were investigated by descriptive analysis method, too. Overall acceptability was increased by roasting but it was low because of formation of bitterness and burnt smell at excessive roasting conditions. Sweetness was the most important factor in organoleptic quality of Cassia tora seeds and the optimum condition for the best quality was $210^{\circ}C$, 20 minutes.

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

• Journal of the Korean Society for information Management
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• v.39 no.3
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• pp.311-336
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• 2022

This study analyzed the contents of Goodreads review data, which is a social cataloging service with the participation of book users around the world, to identify the preference factors that affect book users' book recommendations in the library information service environment. To understand user preferences from a more detailed point of view, sub-datasets for each rating group, each book, and each user were constructed in the sample selection process. Stratified sampling was also performed based on the result of topic modeling of review text data to include various topics. As a result, a total of 90 preference factors belonging to 7 categories('Content', 'Character', 'Writing', 'Reading', 'Author', 'Story', 'Form') were identified. Also, the general preference factors revealed according to the ratings, as well as the patterns of preference factors revealed in books and users with clear likes and dislikes were identified. The results of this study are expected to contribute to more sophisticated recommendations in future recommendation systems by identifying specific aspects of user preference factors.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.227-239
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• 2024

This study aims to analyze the impact of vocational training received by learning workers through the degree-linked work-study program on their learning outcomes. Specifically, we explore the causal relationship between various factors considered during university degree program admission and selection, and the average GPA (Grade Point Average) after admission. To achieve this, we conducted regression analysis and variance analysis using historical admission data and GPA records of 976 students from three undergraduate programs at a domestic K university that implements the degree-linked work-study model. Additionally, we included company information from publicly available databases that could potentially influence the academic performance of learning workers. Our analysis revealed significant causal relationships across various factors, including the classification of the high school attended, gender, family background, subject-specific grades in high school, duration of employment at the company, and age at the time of admission. Based on these findings, we anticipate that universities operating similar degree programs can enhance their selection procedures for learning workers. Furthermore, the results of this study can serve as foundational data for future policy recommendations related to degree-linked work-study programs.

• Journal of Hospice and Palliative Care
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• v.12 no.2
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• pp.72-79
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• 2009

Purpose: The purpose of this study was to test the correlation between the levels of spiritual well-being and spiritual nursing care of nurses for cancer patients and to provide baseline data for spiritual nursing care. Methods: In the study, there were 209 nurses involved who cared for cancer patients, and they were from Christian General Hospital in a city, Jeonju. Data were collected from September 17 to 30 in 2008 using structured questionnaires. The data were analyzed using research methods, including descriptive statistics, t-test, ANOVA, Duncan test, and Pearson correlation coefficients. Results: The mean score of spiritual well-being of nurses was $63.41{\pm}10.32$ (range $20{\sim}80$) and that of spiritual nursing care was $26.96{\pm}7.05$ (range $15{\sim}60$). There was a significant positive correlation between the spiritual well-being of nurses and their spiritual nursing care (r=.353, P=.000). Conclusion: The spiritual well-being and spiritual nursing care have a positive correlation. The level of spiritual well-being of nurses was relatively significant, whereas that of spiritual nursing care was relatively low. Therefore, it is recommended, for spiritual nursing care that nurses responsible for cancer patients should pursue more spiritual growth, attend church services regularly, and should further be educated in their care and responsibility.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• 한국벤처창업학회:학술대회논문집
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• 2017.04a
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• pp.52-52
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• 2017

글로벌 금융위기 이후 다양한 형태로 등장한 금융상품과 ICT의 결합은 그 동안 생각하지 못한 방식으로 전 세계에 다양한 수요를 충족시키면서 폭발적으로 성장했다. 하지만 IT강국이라고 자부하는 대한민국은 다양한 규제와 시스템의 복잡성 때문에 은행상품이 온라인에서 거래되는 것은 아직까지 익숙하지 않다. 다행히 이러한 규제가 조금씩 완화되어 가면서 2016년은 모바일 송금, 금융상품 추천 플랫폼 등 비 금융업체 주도의 금융시장 온라인화가 소극적으로 이루어지는 과도기로 볼 수 있다. 이러한 시점에서 기존 오프라인 채널이 아닌 온라인 채널을 통해 금융상품을 구매하거나 가입하는 고객의 만족요인에 대해 연구하는 것은 향후 폭발적으로 증가할 수요에 앞서 연구하고, 현상을 주도할 기업에서도 소비자의 만족요인을 미리 파악한다는 점에서 시기적으로 적절하다. 해당 연구는 신용대출, 정기예금, 전세대출, 주택담보대출, 정기적금, 그리고 P2P투자 상품 별 만족도에 영향을 미치는 요인과 영향력을 SERVPERF 모델을 이용하여 분석한 뒤, 회귀분석과 텍스트간의 공동 출현단어에 대해 파이선을 통해 메트릭스를 형성하고, 사회연결망 분석으로 네트워크 중심성을 분석하여 단어간의 관계를 살펴보았다. 해당 연구는 국내 최초 온라인 금융상품 비교 추천 플랫폼인 "Finda"의 리뷰/평점데이터를 이용하였다.

• The Journal of Information Systems
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• v.16 no.1
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• pp.1-21
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• 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 of Broadcast Engineers Conference
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• 2019.11a
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• pp.38-40
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• 2019

본 논문에서는 블록체인 및 암호화폐를 이용한 재능 거래 플랫폼을 제안한다. 재능 거래 플랫폼은 프로그램의 외주나 컨텐츠 제작 등의 재능 거래를 중개하는 플랫폼이다. 기존의 재능 거래 플랫폼은 서버-클라이언트 모델 기반의 서비스를 제공하고 있다. 이에 따라 서버를 운용하는데 드는 비용과 관리를 위한 인건비가 발생한다. 따라서 이용자들은 높은 수수료를 부담하게 된다. 또한 서버-클라이언트 모델의 서비스의 경우 이용 업체의 요청에 따라 중앙 관리자가 평가 및 평점에 대한 조작 및 수정을 할 수 있는 가능성이 존재한다. 이러한 단점을 보완하기 위해 블록체인 및 암호화폐 기술을 적용하여 P2P 거래를 통해 이용자에게 부과하는 수수료의 부담을 낮추고, 평가를 블록 데이터로 포함하여 데이터의 위 변조 가능성을 낮춰 신뢰성을 확보하는 시나리오를 제안한다.