• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.175-191
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• 2011

With the MICE(Meeting, Incentive travel, Convention, Exhibition) industry coming into the spotlight, there has been a growing interest in the domestic exhibition industry. Accordingly, in Korea, various studies of the industry are being conducted to enhance exhibition performance as in the United States or Europe. Some studies are focusing particularly on analyzing visiting patterns of exhibition visitors using intelligent information technology in consideration of the variations in effects of watching exhibitions according to the exhibitory environment or technique, thereby understanding visitors and, furthermore, drawing the correlations between exhibiting businesses and improving exhibition performance. However, previous studies related to booth recommendation systems only discussed the accuracy of recommendation in the aspect of a system rather than determining changes in visitors' behavior or perception by recommendation. A booth recommendation system enables visitors to visit unplanned exhibition booths by recommending visitors suitable ones based on information about visitors' visits. Meanwhile, some visitors may be satisfied with their unplanned visits, while others may consider the recommending process to be cumbersome or obstructive to their free observation. In the latter case, the exhibition is likely to produce worse results compared to when visitors are allowed to freely observe the exhibition. Thus, in order to apply a booth recommendation system to exhibition halls, the factors affecting the performance of the system should be generally examined, and the effects of the system on visitors' unplanned visiting behavior should be carefully studied. As such, this study aims to determine the factors that affect the performance of a booth recommendation system by reviewing theories and literature and to examine the effects of visitors' perceived performance of the system on their satisfaction of unplanned behavior and intention to reuse the system. Toward this end, the unplanned behavior theory was adopted as the theoretical framework. Unplanned behavior can be defined as "behavior that is done by consumers without any prearranged plan". Thus far, consumers' unplanned behavior has been studied in various fields. The field of marketing, in particular, has focused on unplanned purchasing among various types of unplanned behavior, which has been often confused with impulsive purchasing. Nevertheless, the two are different from each other; while impulsive purchasing means strong, continuous urges to purchase things, unplanned purchasing is behavior with purchasing decisions that are made inside a store, not before going into one. In other words, all impulsive purchases are unplanned, but not all unplanned purchases are impulsive. Then why do consumers engage in unplanned behavior? Regarding this question, many scholars have made many suggestions, but there has been a consensus that it is because consumers have enough flexibility to change their plans in the middle instead of developing plans thoroughly. In other words, if unplanned behavior costs much, it will be difficult for consumers to change their prearranged plans. In the case of the exhibition hall examined in this study, visitors learn the programs of the hall and plan which booth to visit in advance. This is because it is practically impossible for visitors to visit all of the various booths that an exhibition operates due to their limited time. Therefore, if the booth recommendation system proposed in this study recommends visitors booths that they may like, they can change their plans and visit the recommended booths. Such visiting behavior can be regarded similarly to consumers' visit to a store or tourists' unplanned behavior in a tourist spot and can be understand in the same context as the recent increase in tourism consumers' unplanned behavior influenced by information devices. Thus, the following research model was established. This research model uses visitors' perceived performance of a booth recommendation system as the parameter, and the factors affecting the performance include trust in the system, exhibition visitors' knowledge levels, expected personalization of the system, and the system's threat to freedom. In addition, the causal relation between visitors' satisfaction of their perceived performance of the system and unplanned behavior and their intention to reuse the system was determined. While doing so, trust in the booth recommendation system consisted of 2nd order factors such as competence, benevolence, and integrity, while the other factors consisted of 1st order factors. In order to verify this model, a booth recommendation system was developed to be tested in 2011 DMC Culture Open, and 101 visitors were empirically studied and analyzed. The results are as follows. First, visitors' trust was the most important factor in the booth recommendation system, and the visitors who used the system perceived its performance as a success based on their trust. Second, visitors' knowledge levels also had significant effects on the performance of the system, which indicates that the performance of a recommendation system requires an advance understanding. In other words, visitors with higher levels of understanding of the exhibition hall learned better the usefulness of the booth recommendation system. Third, expected personalization did not have significant effects, which is a different result from previous studies' results. This is presumably because the booth recommendation system used in this study did not provide enough personalized services. Fourth, the recommendation information provided by the booth recommendation system was not considered to threaten or restrict one's freedom, which means it is valuable in terms of usefulness. Lastly, high performance of the booth recommendation system led to visitors' high satisfaction levels of unplanned behavior and intention to reuse the system. To sum up, in order to analyze the effects of a booth recommendation system on visitors' unplanned visits to a booth, empirical data were examined based on the unplanned behavior theory and, accordingly, useful suggestions for the establishment and design of future booth recommendation systems were made. In the future, further examination should be conducted through elaborate survey questions and survey objects.

• Tuberculosis and Respiratory Diseases
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• v.63 no.3
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• pp.235-241
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• 2007

Background: Airway mucus hypersecretion plays an important role in the pathogenesis of asthma, and is associated with the induction of MUC5AC expression in airway secretion. The MUC5AC gene is highly polymorphic; however, there are few studies about the association between the polymorphisms of the MUC5AC gene and asthma susceptibility or asthma phenotypes. We have investigated the association of MUC5AC promoter polymorphisms with the risk of asthma and asthma phenotypes. Methods: We determined the genotypes of the MUC5AC promoter (-1274G>A) in 78 asthma patients and in 78 age, sex-matched control individuals in the Korean population. Genomic DNAs from blood were analyzed by PCR and RFLP (restriction fragment length polymorphism) determination. We examined $FEV_1$, total eosinophil count, serum IgE level, $PC_{20}$ and the presence of atopy (by a skin test) in asthma patients. Results: The mean age of the patients was $47.7{\pm}16.1$ years and 38.5% were men, and the mean $FEV_1$ was $84.4{\pm}22.3%$ of predicted in the asthma patients. The -1274G>A polymorphism of the MUC5AC promoter in asthma patients was not significantly different as compared with normal individuals (GG 57.7%, AG 34.6% and AA 7.7% in asthma patients vs. GG 56.4%, AG 38.5% and AA 5.1% in control subject, p = 0.752, Cod). Several clinical parameters in asthma patients such as $FEV_1$, total eosinophil count, serum IgE level, $PC_{20}$ and the presence of atopy, were not associated with the -1274G>A polymorphism of the MUC5AC promoter. Conclusion: The -1274G>A single nucleotide polymorphism (SNP) frequency of the MUC5AC promoter was not associated with asthma in a Korean population.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.44 no.2
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• pp.117-124
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• 2018

In this study, cosmetic materials were developed using a new method of making juice through the fermentation of raw natural materials with microorganisms in order to supplement the advantages and disadvantages of an organic solvent extraction method and a microbial fermentation method. The natural products were selected from two colors (red, green) of paprika known to be rich in various colors and vitamins. The microorganisms used for fermentation were fermented by inoculating paprika with lactic acid bacteria (Lactobacillus plantarum) having sugar-hydrolyzed ability. First, we investigated the changes of physiologically active substances of two kinds of paprika juice and two kinds of fermented paprika juice. Total phenols content and total flavonoids content were higher in the fermented paprika juice than in the paprika juice, and especially in the fermented red paprika juice. Free radical scavenging effect and lipid peroxidation inhibitory effect were also showed an excellent antioxidative effect on paprika fermented juice, among which the effect of red paprika fermentation juice was the highest. The expression of MMP-1 in fermented red paprika juice with high antioxidant activity was inhibited by concentration-dependent expression of MMP-1 mRNA and MMP-1 protein. In the glycation experiments with aging, the anti-glycation effect of fermented paprika juice was highly inhibited by the production of advanced glycation end-products (AGEs), which was closely related to the antioxidant effect. In addition, the activity of senescence-associated ${\beta}$-galactosidase (SA-${\beta}$-gal), an indicator of cell senescence, was measured using human dermal fibroblast (HDF). The results showed that the cell senescence was inhibited when the cells were treated with fermented paprika juice. In conclusion, fermented paprika juice using lactic acid bacteria showed better antioxidative and anti-aging effects than paprika juice. Among them, fermented red paprika juice has the best antioxidant and anti-aging effect and can be applied as natural new material of antioxidant and anti-aging.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.