• Korean Journal of Food Science and Technology
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• v.29 no.4
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• pp.776-783
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• 1997

Response surface methodology was applied to determine the optimum roasting conditions (roasting temperature and time) for the high-quality Polygonatum odoratum tea which has been roasting with a traditional means. As quality criteria of Polygonatum odoratum tea, water-soluble solids, browning color, total phenolic compounds and electron-donating ability were proportionally increased with increased temperature and time of roasting conditions up to around $145^{\circ}C$ and 55 min, respectively, while they were decreased under the extended-roasting conditions. The optimum roasting temperature and time based on the organoleptic overall acceptability were $146^{\circ}C$ and 52 min, respectively. On the basis of superimposed contour maps for the tea characteristics, the optimum range of roasting conditions were $135{\sim}140^{\circ}C$ and $58{\sim}64$ min. Predicted values at the optimum conditions $(137^{\circ}C,\;60\;min)$ were in good agreement with experimental values.

• Korean Journal of Food Science and Technology
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• v.14 no.4
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• pp.330-335
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• 1982

A powdered beverage with afruit flavor was stored at 4, 21, 35 and $45^{\circ}C$ for 180 days to study ascerbic acid destruction at the selected temperatures. Degradation of ascorbic acid in the model followed the first order reaction, and the temperature dependence of reaction rate constants at tested temperatures was accounted for by the Arrhenius equqtion. The calculated activation energy for the destruction of ascorbic acid was 3.3 Kcal/mole. The relationship between ascorbic acid content and sensory flavor score of the beverage indicated that samples with destruction of ascorbic acid over 25% showed objectionable flavor. An attempt was made to predict the quality of powdered beverage by using a simulation model. A comparision between ascorbic acid values from shelflife tests and the simulation program showed a good agreement within acceptable error. This result demonstrated that quality of powdered beverage could be predicted by using a computer simulation model with a desired accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.970-977
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• 2020

The collaborative filtering recommendation technique has been the most widely used since the beginning of e-commerce companies introducing the recommendation system. As the online purchase of products or contents became an ordinary thing, however, recommendation simply applying purchasers' ratings led to the problem of low accuracy in recommendation. To improve the accuracy of recommendation, in this paper suggests the method of collaborative filtering that analyses product reviews and uses them as a weighted value. The proposed method refines product reviews with text mining to extract features and conducts sentiment analysis to draw a sentiment score. In order to recommend better items to user, sentiment weight is used to calculate the predicted values. The experiment results show that higher accuracy can be gained in the proposed method than the traditional collaborative filtering.

• The Journal of Bigdata
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• v.9 no.1
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• pp.49-60
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• 2024

This study aims to provide insights for developing innovative products, based on reviews from females aged 30 to 70 who bought cosmetic cushions via TV home shopping. Analyzing 200,000 reviews with Selenium and NLP techniques, we found the main audience is in their 50s and 60s, prioritizing radiance, blemish and wrinkle coverage, and adherence. Notably, products with appealing designs were preferred, especially for gifting among relatives and friends. The proposed innovation is Korea's first AI-recommended cushion, utilizing NLP to match customer needs. Key ingredient recommendations include S.Acamella extract and AHA components, chosen for their perceived benefits and consumer preference. The research also highlights the importance of product aesthetics and gift potential, suggesting marketing strategies should emphasize these aspects to appeal to the target demographic. This approach aims to guide product development and marketing towards meeting consumer expectations in the cosmetic cushion industry, making products more personalized and gift-worthy.

• Journal of Korean Society of Transportation
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• v.21 no.5
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• pp.73-81
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• 2003

The Government presented improvement plans such as "Traffic Accident Frequent Point" and "Hazardous Roads" to reduce traffic accidents on the increase after 1980s. In case of the hazardous roads, they are expressed by grades which are marked by geometric elements such as width, radius, grade. sight distance. and other environmental factors. As each business for improving roads goes by only focusing on improvement of geometric elements, excessive expense can be invested too much nowadays causing economical waste. Therefore, as improvement plans approached by economic access are needed, this paper shows the cost-effective improvement of the business to keep safety related to traffic accident and economical waste. The hazardous roads which authorized by Gwang-ju National Road Preservation Office of Construction and Transportation Ministry in 1995 for business for improvement of roads, were investigated before 1999. First of all, estimating traffic accident models are presented by using existed data statistically. The models help to maximize traffic accident decrease through control of the presented factor. Secondly, optimum construction cost of improvement is presented to prevent overcapitalization. However, this paper is limited because it was difficult to sort the data with various areas and to approach various ways.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.151-166
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• 2020

A recommender system is a system that recommends products or services that best meet the preferences of each customer using statistical or machine learning techniques. Collaborative filtering (CF) is the most commonly used algorithm for implementing recommender systems. However, in most cases, it only uses purchase history or customer ratings, even though customers provide numerous other data that are available. E-commerce customers frequently use a search function to find the products in which they are interested among the vast array of products offered. Such search keyword data may be a very useful information source for modeling customer preferences. However, it is rarely used as a source of information for recommendation systems. In this paper, we propose a novel hybrid CF model based on the Doc2Vec algorithm using search keywords and purchase history data of online shopping mall customers. To validate the applicability of the proposed model, we empirically tested its performance using real-world online shopping mall data from Korea. As the number of recommended products increases, the recommendation performance of the proposed CF (or, hybrid CF based on the customer's search keywords) is improved. On the other hand, the performance of a conventional CF gradually decreased as the number of recommended products increased. As a result, we found that using search keyword data effectively represents customer preferences and might contribute to an improvement in conventional CF recommender systems.

• Journal of Digital Convergence
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• v.14 no.12
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• pp.377-388
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• 2016

This study was done to compare consciousness of biomedical ethics in nursing and general students. Participants were 382 nursing and general students at a college in S city. Mean score of consciousness of biomedical ethics(range:1~4) in nursing students was 3.04 and general students, 3.12. Thus, mean score of consciousness of biomedical ethics of two group were above the average and general students significantly higher than nursing students. Life-respect consciousness, perceived ethical values in nursing students were shown as significant predictors on consciousness of biomedical ethics and life-respect consciousness, sexual attitude, value regarding child rearing in general students. The most influential predictor of two groups was life-respect consciousness. To establish desirable biomedical ethics of nursing students, it is necessary that subjects related to biomedical ethics should be mandatory, and it is necessary to raise the proportion of credit for the curriculum.

• Korean Journal of Food Science and Technology
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• v.45 no.1
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• pp.13-19
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• 2013

This study was carried out to find the optimal conditions for red cabbage seed sprouts in terms of their physicochemical and sensory qualities by electron-beam irradiation, cultivation and storage using the response surface methodology (RSM). Moisture content ($R^2$=0.9638) was affected by irradiation dose and cultivation time. Total phenolics content ($R^2$=0.9117) was mainly affected by irradiation dose, but carotenoid content ($R^2$=0.8338) was affected in the order of irradiation dose, cultivation time and storage time. Sensory properties were also affected by irradiation dose, and thus scores decreased as irradiation dose increased. The optimum conditions estimated by superimposing total phenolics content and overall acceptance were 2.2-3.8 kGy of the irradiation dose, 3.0-4.0 days of cultivation and 2.0-3.0 days of storage.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.31-49
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• 2023

With the growth of the food-catering industry, consumer preferences and the number of dine-in restaurants are gradually increasing. Thus, personalized recommendation services are required to select a restaurant suitable for consumer preferences. Previous studies have used questionnaires and star-rating approaches, which do not effectively depict consumer preferences. Online reviews are the most essential sources of information in this regard. However, previous studies have aggregated online reviews into long documents, and traditional machine-learning methods have been applied to these to extract semantic representations; however, such approaches fail to consider the surrounding word or context. Therefore, this study proposes a novel review textual-based restaurant recommendation model (RT-RRM) that uses deep learning to effectively extract consumer preferences from online reviews. The proposed model concatenates consumer-restaurant interactions with the extracted high-level semantic representations and predicts consumer preferences accurately and effectively. Experiments on real-world datasets show that the proposed model exhibits excellent recommendation performance compared with several baseline models.