• Journal of Korean Medical classics
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• v.23 no.1
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• pp.133-142
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• 2010

Preconditions for the separation of prescribing and dispensing medicinal herbs in Traditional Korean Medicine are classification of medicinal herbs for general public and special medical uses, establishment of national medicinal herb distribution company of governmental base, restriction in purchase of medicinal herbs for special medical use, partnership between doctors and pharmacists of Traditional Korean Medicine, and coverage of herbal medicine-based medication in national health insurance, etc. The number of Traditional Korean Medicine Pharmacists which was born during 'the herbal medicine conflict' initiated in 1993, goes over 1,000 and will increase by 120 annually. The number of Traditional Korean Medical Doctors is over 17,000 and increases by 850 annually. So in order to engage partnership between two groups, the government have to arrange the number of outputs of each group. Standardization and classification of diagnosis and diseases in Traditional Korean Medicine is a matter of course in the separation of prescribing and dispensing medicinal herbs. Related societies and academies need to do researches with governmental fund first. After these works, we can launch a task force team for implementation of process for the separation of prescribing and dispensing medicinal herbs in Traditional Korean Medicine properly. Entering the national health insurance system for full coverage of Korean Medicine care service will be essential for the patients. Implementation the separation of prescribing and dispensing medicinal herbs in Traditional Korean Medicine would be the core of health insurance coverage for medication.

• Journal of Fashion Business
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• v.6 no.5
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• pp.136-150
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• 2002

In this study, we proposed a method to be standing customers as the supporting system for the improvement of fashion garment industry which was the marginal growth getting into full maturity of market. As for the customer creation method of Fashion garment company is developing a marketing program to be standing customer as customer scoring to estimate a existing customer‘s buying power, and figure out minimum fixed sales of company to use a future purchasing predict. This study was a result of data from total sixty thousands data to be created for the 11 months from september. 2000 to July. 2001. The data is part of which the company leading the Korean fashion garment industry has a lot of a customer purchasing history data. But this study used only 48,845 refined purchased data to discriminate from sixty thousands data and 21,496 customer case with the exception of overlapping purchased data among of those. The software used to handle sixty thousands data was SAS e-miner. As the analysis process is put in to operation the analysis of the purchasing customer’s profile firstly, and the second come into basket analysis to consider the buying associations for Association goods, the third estimate the customer grade of Customer loyalty by 3 ways of logit regression analysis, decision tree, Artificial Neural Network. The result suggested a method to be estimate the customer loyalty as 3 independent variables, 2 coefficients. The 3 independent variables are total purchasing amount, purchasing items per one purchase, payment amount by one purchasing item. The 2 coefficients are royal and normal for customer segmentation. The result was that this model use a logit regression analysis was valid as the method to be estimate the customer loyalty.

• Journal of Digital Convergence
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• v.11 no.3
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• pp.389-395
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• 2013

More sales of food and beverage coupons have been made using SNS on social commerce recently. If one buys coupons on social commerce, he/she can enjoy products at a lower price; however, there are drawbacks that one must consider such as location, service hours, and discount rate. Thus, this paper suggests a system that recommends food and beverage coupons on social commerce for users that considers a user's personal context of location, time, and purchase history. In order to reflect a user's context awareness and continuous preference, this paper suggests a method based on the Bayesian network. In order to reflect personalized weighting on the standard of coupon selection to match a user's preference, a measurement and classification of weighting preferences is performed on the basis of AHP. 20 experiments in one month involving 12 students were carried out to verify the effectiveness of the system, resulting in an 80% satisfaction level.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1306-1325
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• 2019

As the world is moving towards digitization, data is generated from various sources at a faster rate. It is getting humungous and is termed as big data. The financial sector is one domain which needs to leverage the big data being generated to identify financial risks, fraudulent activities, and so on. The design of predictive models for such financial big data is imperative for maintaining the health of the country's economics. Financial data has many features such as transaction history, repayment data, purchase data, investment data, and so on. The main problem in predictive algorithm is finding the right subset of representative features from which the predictive model can be constructed for a particular task. This paper proposes a correlation-based method using submodular optimization for selecting the optimum number of features and thereby, reducing the dimensions of the data for faster and better prediction. The important proposition is that the optimal feature subset should contain features having high correlation with the class label, but should not correlate with each other in the subset. Experiments are conducted to understand the effect of the various subsets on different classification algorithms for loan data. The IBM Bluemix BigData platform is used for experimentation along with the Spark notebook. The results indicate that the proposed approach achieves considerable accuracy with optimal subsets in significantly less execution time. The algorithm is also compared with the existing feature selection and extraction algorithms.

• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.231-240
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• 2018

Recently, personal information processing are becoming more important in the behavioral advertising based on online and mobile platform. The behavioral advertising analyzes and utilizes individual's search & purchase history, hobbies, and tendency based on the personal behavior information collected using the automatic collection device. Therefore, it collects and stores other types of personal information which did't defined in Privacy Act and can analyze personal behavior. This characteristics may cause disclosure of personal information and exposure to intrusion. In this paper, we investigate and analyze the privacy policy of the advertising agencies, and discussded the measures to be taken in collecting, storing and using personal information suitable for behavior information.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.185-190
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• 2006

Most systems in ubiquitous computing analyze context information of users which have similar propensity with demographics methods and collaborative filtering to provide personalized recommendation services. The systems have mostly used static context information such as sex, age, job, and purchase history. However the systems have limitation to analyze users' propensity accurately and to provide personalized recommendation services in real-time, because they have difficulty in considering users situation as moving path. In this paper we use users' moving path of dynamic context to consider users situation. For the prediction accuracy we complete with a path completion algorithm to moving path which is inputted to RSOM. We train the moving path to be completed by RSOM, analyze users' moving pattern and predict a future moving path. Then we recommend the nearest product on the prediction path with users' high preference in real-time. As the experimental result, MAE is lower than 0.5 averagely and we confirmed our method can predict users moving path correctly.

• Fashion & Textile Research Journal
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• v.23 no.3
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• pp.358-369
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• 2021

This study aims to classify the dimensions of the sales information of secondhand clothing goods on the C2C secondhand trading platform and to systematically analyze the components of each dimension. To this end, the NVivo 12.0 qualitative data analysis software was used. The content analysis showed that the sales information of secondhand clothing goods was classified into four dimensions: detailed information of the sale goods, information specific to secondhand clothing goods, seller opinion information, and service information. The components of each dimension were as follows. The detailed information of the sale goods included size, sale price, item, design, brand name, material, color, wearing season, fit, gender, etc. The information specific to secondhand clothing goods included the number of times the item was worn, its purchase history, and product condition. Seller opinion information included product review, sales motivation, notes for the transaction, coordination proposal, and usage proposal. The service information included the transaction mode, exchange·return·refund, and promotion. The frequency analysis showed that the highest frequencies were sale goods(37.47%), information specific to secondhand clothing goods(24.63%), seller opinion information(20.54%), and service information(17.37%). This study will help C2C secondhand trading platform managers or sellers establish clear standards for presenting sales information and developing ideas toward constructing differentiated platform contents.

• Agribusiness and Information Management
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• v.10 no.1
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• pp.16-25
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• 2018

Fig is a fruit of which the flesh is very sweet, and it is a tree which has been grown for fruit in Korea since long time ago. However, since the flesh of fig tends to be easily softened, commercial cultivation of this fruit began later than that of other fruit trees grown for profit, however, the cultivation and demand of fig tend to be increased steadily due to the development of technology for storage and distribution since the 2000s. In addition, as the domestic dining culture is getting diversified, the dishes cooked by using fig as a food material are introduced through diverse foods including dessert, and it is possible to intake fig in diverse ways, but not through the traditional processed food. Therefore, it is necessary to establish a measure of expanding the consumption of fig as a processed food, and it will be possible to overcome the limitation of short storage period, while securing the competitiveness of the fig industry. In this research, we have studied the history of domestic fig cultivation, current status of it and status of processed foods through related documents and materials, and the characteristics of the consumers who purchase figs. Fig is a traditional fruit, however, we could find out the fact that the consumers tend not to recognize it as a traditional one. Therefore, if we could add fig to various processed foods utilizing its sweet taste, rather than increasing the consumption of fresh fruits, it may increase the consumption of it.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.127-140
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• 2021

Recently, sequence data containing time information, such as sensor measurement data and purchase history, has been generated in various applications. So far, many methods for finding sequences that are significantly different from other sequences among given sequences have been proposed. However, most of them have a limitation that they consider only the order of elements in the sequences. Therefore, in this paper, we propose a new anomalous sequence detection method that considers both the order of elements and the time interval between elements. The proposed method uses an extended LSTM autoencoder model, which has an additional layer that converts a sequence into a form that can help effectively learn both the order of elements and the time interval between elements. The proposed method learns the features of the given sequences with the extended LSTM autoencoder model, and then detects sequences that the model does not reconstruct well as anomalous sequences. Using experiments on synthetic data that contains both normal and anomalous sequences, we show that the proposed method achieves an accuracy close to 100% compared to the method that uses only the traditional LSTM autoencoder.

• Acta Via Serica
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• v.7 no.1
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• pp.53-82
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• 2022

The Hebrew word for silk, meshi, is mentioned in the Bible only once and there is a possibility that the item to which it referred was made of local wild silk. Although Jewish historical sources from the Roman and Byzantine periods mention silk many times, only a few silk textiles have been discovered at a sited dated to the Byzantine period (4^th-7^th centuries CE). The word "silk" occurs in the New Testament, although only once. A turning point in the history of the Negev (Southern Israel) occurred around 400 CE when it underwent a period of prosperity related to the advent of Christianity and pilgrimage, which enabled the purchase of imported silk textiles. The Early Islamic period (7-8th centuries CE) yielded four (out of 310) silk textiles from Nahal 'Omer on the Spice Routes joining Petra, in the Edom Mountains of modern Jordan, and the mercantile outlets on the Mediterranean Sea, notably Gaza and El Arish. The most important silk textile assemblage in the Southern Levant was found near Jericho at Qarantal Cave 38 and dates to the medieval period (9th-13th centuries CE). Linen textiles decorated with silk tapestry originating in Egypt date back to the 10-11th centuries CE. Mulham textiles - silk warp with hidden cotton wefts - were discovered in the medieval fortress on Jazirat Fara'un (Coral Island) in the Red Sea, 14 kilometers south of Elat and today located in Egypt. Mulham is mentioned in literary sources of the ninth century in Iraq and Iran, whence it spread through the Islamic world. The article will present aspects of the origin, transmission, and exchange of these textiles.