• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.4 s.163
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• pp.615-623
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• 2007

This study aimed to construct an effective decision-making model on selection of cloth stores using AHP technique. The proposed AHP structure consists of three levels. The highest level includes the cloth stores alternative which are department store, specialty store, and a clothes store. The second level consists of the key performance measurements for evaluating the optimal selection of cloth stores, such as location, facilities, product, service, and promotion. The lowest level consists of items which affects the performance measurements in the upper level. The items are convenience location, courteous service, decor/ambience, availability of parking, etc. The data for this research were collected from questionnaires of 132 in Busan. Data were analyzed by frequency and AHP. As the result of this study, 'product' was decided as a most important item in department store, while 'location' was decided as a most important item in specialty store and a clothes store. And 'variety goods' evaluated as that of first priority in the totality evaluation items in department store, but 'convenience location' evaluated as that of first priority in the totality evaluation items specialty store and a clothes store.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1116-1120
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• 2010

In this paper we present two refined neighbor selection algorithms for recommender systems and also show how the attributes of the items can be used for higher prediction quality. The refined neighbor selection algorithms adopt the transitivity-based neighbor selection method using virtual neighbors and alternate neighbors, respectively. The experimental results show that the recommender systems with the proposed algorithms outperform other systems and they can overcome the large scale dataset problem as well as the first rater problem without deteriorating prediction quality.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.737-746
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• 2009

The appearance of SOA affects business and IT environment and many studies on SOA is progressed in academics and industries. Service will increase extremely and make business opportunity by composing service. In addition, It will support it. However, studies on selection of a service composition target are insufficient. In this study, we propose a framework that select a service composition target so we use Analytic Hierarchy Process methodology. As a result of this study, we enabled service selection to apply functional/non-functional attributes of services, various stakeholder's view and some selection criteria with service composition.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.586-589
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• 2012

Web Services have been utilized in a wide variety of applications and have turned into a key technology in developing business operations on the Web. Originally, Web Services can be exploited in an isolated form, however when no single Web Service can satisfy the functionality required by a user, there should be a possibility to compose existing services together in order to fulfill the user requirement. However, since the same service may be offered by different providers with different non-functional Quality of Service (QoS), the task of service selection for Web Service composition is becoming complicated. Also, as Web Services are inherently unreliable, how to deliver reliable Web Services composition over unreliable Web Services should be considered while composing Web Services. In this paper, we propose an approach on a QoS-aware Web Service selection for reliable Web Service composition. In our approach, we select and classify Web Services using Decision Tree based on QoS attributes provided by the client. Service classifier will improve selection of relevant Web Services early in the composition process and also provide flexibility to replace a failed Web Services with a redundant alternative Web Services, resulting in high availability and reliability of Web Service composition. We will provide an implementation of our proposed approach along with efficiency measurements through performance evaluation.

• Asia pacific journal of information systems
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• v.30 no.1
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• pp.31-52
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• 2020

Stock markets are popular investment avenues to people who plan to receive premium returns compared to other financial instruments, but they are highly volatile and risky due to the complex financial dynamics and poor understanding of the market forces involved in the price determination. A system that can forecast, predict the stock prices and automatically create a portfolio of top performing stocks is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. In this paper the authors propose a Stock prediction, Portfolio Generation and Selection model based on Machine learning algorithms, Artificial neural networks (ANNs) are used for stock price prediction, Mathematical and Statistical techniques are used for Portfolio generation and Un-Supervised Machine learning based on K-Means Clustering algorithms are used for Portfolio Evaluation and Selection which take in to account the Portfolio Return and Risk in to consideration. The model presented here is limited to predicting stock prices on a long term basis as the inputs to the model are based on fundamental attributes and intrinsic value of the stock. The results of this study are quite encouraging as the stock prediction models are able predict stock prices at least a financial quarter in advance with an accuracy of around 90 percent and the portfolio selection classifiers are giving returns in excess of average market returns.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.135-140
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• 2007

Feature selection is a preprocessing technique commonly used on high dimensional data. Feature selection studies how to select a subset or list of attributes that are used to construct models describing data. Feature selection methods attempt to explore data's intrinsic properties by employing statistics or information theory. The recent developments have involved approaches like correlation method, dimensionality reduction and mutual information technique. This feature selection have become the focus of much research in areas of applications with massive and complex data sets. In this paper, we provide a feature selection method considering data characteristics and generalization capability. It provides a computational approach for feature selection based on fuzzy cluster analysis of its attribute values and its performance measures. And we apply it to the system for classifying computer virus and compared with heuristic method using the contrast concept. Experimental result shows the proposed approach can give a feature ranking, select the features, and improve the system performance.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.4
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• pp.585-592
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• 2016

This study was conducted to analyze the gap between importance and satisfaction of university foodservice attributes as well as to assess customer satisfaction with university foodservice establishments. All statistical analyses were conducted using the SPSS package program (ver. 20.0) for t-test, ANOVA, and Importance-Performance Analysis (IPA). A total of 619 valid responses were used for the data analysis. The results of this study are as follows: the composition of respondents was 53.5% males and 46.5% females. Exactly 85.5% of respondents ate lunch at least five times a week at the on-campus foodservice. The favorite lunch menus of Chinese university students were Chinese food (91.8%), followed by Western food (3.5%), Korean food (2.2%), and Japanese food (1.5%). According to the results of IPA, foodservice selection attributes that were priorities for improvement were food taste, food freshness, menu variety, waiting time for meal, and toilet cleanliness. In addition, five satisfaction factors were extracted by exploratory factor analysis. According to the results of one-way ANOVA, 'physical environment' and 'service quality' factors showed significant differences according to the students' grades and the frequency of eating lunch at on-campus foodservice. On the other hand, 'food quality and menu' and 'convenience and price' factors showed significant differences according to meal cost. In conclusion, in order to enhance customer satisfaction of on-campus foodservice, foodservice managers should offer a varied menu at reasonable prices and improve food quality.

• Journal of the Korean Institute of Landscape Architecture
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• v.41 no.6
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• pp.29-42
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• 2013

The purpose of this study was to derive selection attribute of fishing experience villages, and to analyze the importance and performance on targeting experts and experienced tourists. Consequently, it could suggest implications for the guideline and the evaluation criteria of making fishing experience villages for the revitalization of eco experience tourism. Therefore, in this study, literature investigation and IPA analysis were used by Microsoft Office Excel 2010, SPSS 20.0. As a result, the 'variety of local foods and specialties' was the most urgent attribute to require intensive management strategy. And it is recommended to improvement about 'attributes related residents' because it were insensitived on the recognition by users. Satisfaction of 'appropriateness of coast' was the lowest, but the efficiency was quite high. Finally, not only 'the area of control' but also 'the area of influence' should be reviewed as an important aspects on developing the fishing experience village. The significance of this study is to derive selection attributes of making fishing experience villages for revitalization of eco experience tourism, and to suggest strategy directions based on IPA analysis result by experts and experienced tourists. But in this study, the site was limited to Gyeonggi-do, so it is recommended to expand further.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.471-481
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• 2020

This study aims to investigate the relationship between the factors of RMR product selection attributes, brand image, and satisfaction as the interest and frequency of use of RMR products of dining out consumers increase recently. Convenience sampling was used for consumers with experience in using RMR products launched in catering companies and restaurants. The investigation period was conducted for about 20 days from August 10, 2020. The final 291 copies were used for research analysis, and the SPSS 21.0 statistical package program was used for hypothesis verification. As a result of the analysis, the hygiene (��=.160), menu (��=.203), and packaging (��=.291) of Hypothesis 1 had a significant effect on reliability. Hypothesis 2's menu (��=.270), convenience (��=.201), and packaging (��=.195) were found to have a significant effect on differentiation. The reliability (��=.328) and differentiation (��=.443) of the brand image of Hypothesis 3 were found to have a significant effect on satisfaction (��=.428). Hygiene (��=.388), menu (��=.229), and convenience (��=.243) of Hypothesis 4 were analyzed to have a significant effect on satisfaction. Lastly, this study is expected to be provided as basic research data related to RMR products, and is intended to be presented as a theoretical basis for the use of marketing and direction in RMR product development of food service companies and restaurants.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.258-267
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• 2021

This study attempted an empirical analysis study on the Meal Kit Product, whose interest and demand continued to increase according to the eating out trend in the Untact era. In addition, this study attempted to investigate the relationship between the factors of Home Meal Replacement Meal Kit Product Selection Attributes, Consumers Satisfaction, and Other Recommendation Intention. Convenience sampling was used for consumers with experience in using Meal Kit Products released by food service companies and start-up companies. The investigation period was conducted for about one month from July 01, 2020, and the final 285 copies were used for analysis. The SPSS 21.0 statistical package program was used to verify the hypothesis. As a result of the analysis, the price (β=.241), convenience (β=.317), and diversity (β=.191) of Hypothesis 1 had a significant effect on Consumers Satisfaction. Price (β=.482), convenience (β=.133), and diversity (β=.342) were found to have a significant effect on the intention to recommend others. It was analyzed that Hypothesis 3's Consumers Satisfaction (β=.443) had a significant effect on Other Recommendation Intention. Finally, through this study, we expect to be provided as basic research data related to Meal Kit Product. It is intended to be presented as a theoretical basis for the use of marketing and direction for the development of milk kit products for catering companies and restaurants.