• Korean Journal of Clinical Pharmacy
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• v.23 no.4
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• pp.327-333
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• 2013

Background: Antihistamine and anti-allergy medications are widely used during pregnancy. Reading label information is one of the easiest ways to get safety information. But there are content gaps among countries. Objective: To compare the risk level and the recommendation level of antihistamine/anti-allergy drug's label information in pregnant women among Korea, the USA, the UK, and Japan. Method: Study drugs of antihistamine/anti-allergy medications were selected according to Korea drug classification codes. Based on the label information of selected product, risk level was classified into 5 categories as follows: 'Definite', 'Probable', 'Possible', and 'Unlikely', 'Unclassified' according to the level of evidence. Recommendation level was classified into 4 categories as follows: 'Contraindicated', 'Cautious', 'Compatible', and 'Unclassified'. Frequency and proportion were presented according to the each category. To estimate agreement of each category among 4 countries, percent agreement and kappa (k) coefficient were calculated. Results: Total 13 drug ingredients were selected for antihistamine/anti-allergy medications. In risk level, Korea (46%) and Japan (69%) were mostly classified in the category of 'Unclassified', but 'Unlikely' category was more frequent in the UK (62%) and the USA (46%). In recommendation level, the proportion of 'Contraindicated' was highest in Korea (46%) compared to other countries. In contrast, the category of 'Cautious' was 77%-85% in the USA, the UK, and Japan. The percent agreement for risk level was highest in the USA-UK (54%). The recommendation level of Korea-USA showed lowest agreement for percent agreement (46%) and kappa coefficient (k=0.02). Conclusion: We confirmed the differences among safety information provided by four different countries. 'Contraindicated' was more likely in Korea compared with other countries.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.1
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• pp.33-52
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• 2020

Some consumers question the safety of imported fashion products. We examine the effects of the use of an ecolabel, product category, and country of origin on consumer responses such as perceived physical risk, attitude towards a product, subjective norm, and intention to purchase imported fashion products. A sample of 508 adults in their 30s to 40s participated in online survey experiments. The survey experiments used 2 (the use of the ecolabel vs no label) × 4 (country of origin: China, Dominican Republic, Norway, and the United States) between-subjects and 4 (product category: men/women's wear, children's wear, underwear, and accessories) within-sub-jects factorial design. A total of 32 product-catalog images (stimuli) and eight versions of the questionnaire were developed. The use of the ecolabel is identified as having a significantly lower perceived physical risk than the no-label. The consumers' perceived physical risk also differs depending on product category and country of origin. Consumers perceive a higher physical risk about children's wear and underwear than other product categories as well as fashion products sourced from developing countries than from developed countries. The reduction of physical risk is found to facilitate consumers' purchase decision-making process.

• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.378-385
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• 2010

The explosive increase in the use of email has made to need email classification efficiently and accurately. Current work on the email classification method have mainly been focused on a binary classification that filters out spam-mails. This methods are based on Support Vector Machines, Bayesian classifiers, rule-based classifiers. Such supervised methods, in the sense that the user is required to manually describe the rules and keyword list that is used to recognize the relevant email. Other unsupervised method using clustering techniques for the multi-category classification is created a category labels from a set of incoming messages. In this paper, we propose a new automatic email multi-category classification method using NMF for automatic category label construction method and dynamic category hierarchy method for the reorganization of email messages in the category labels. The proposed method in this paper, a large number of emails are managed efficiently by classifying multi-category email automatically, email messages in their category are reorganized for enhancing accuracy whenever users want to classify all their email messages.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.1
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• pp.57-62
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• 2010

This paper proposes two methods for the construction of job offer and job hunting information in order to supply an environment that can easily connects to job hunting information. First, the database expansion, category rules and ERD(Entity Relation Diagram) are designed for the construction of job hunting site with real example. Second, the prime number labeling rules are designed for the strong intimacy of label rules. Therefore, according to using the systematic and regular rules when we design and construct a database, the consistency and efficiency are improved in the database being constructed and being operated. And the convenience of application program development and operation are easily provided. In addition, the proposed code-expansion rule can be defined and be standardized in the domestic and foreign job offer and job hunting information provision agency.

• The International Journal of Costume Culture
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• v.2 no.1
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• pp.31-42
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• 1999

The category killer that has been rapidly growing mainly in advanced countries since early 1990's, is a mew distribution model which aims for obtaining market controlling power by surpassing competing businesses in a specific area of products. The domestic situation of category killers is very different from that of advanced ones abroad since it has just been introduced into the Korean market. At the moment, there are only 10 or so companies operating in the market : Geopyung's , Taeheng's , Midopa's , of Sinsegye Department store, adn of Yerim International. The purpose of this study is to examine problems of domestic companies in the present market by analysing the operation status of category killers in domestic markets as well as foreign ones, and to suggest a counter-strategy of category killers for the distribution environment of the 21st century to improve the competitiveness of Korean distribution industry. The competitiveness of category killers lies above all in products lines. Category killers are equipped with the greatest number of products lines among those of competing businesses due to maximized product selections in an limited range. Another source of competitiveness may be found in balanced strategy positioning. That is to say, category killers are in a position where they can adjust policies towards any of the three purposes while aiming at them altogether : prices of discount stores, products range of specialty stores, and customer service level of department stores. It is also necessary for efficient store operation to use information technology such as electronic data interchange (EDI), electronic pose system(EPOS) and electronic funds transfer (EFTPOS). As for the cost structure, category killers can gain an advantage over other business since operating cost of various sections can be saved. There are, however, certain risks that category killers with strong competitiveness may influence on other businesses a great deal and even facilitate their decline. Yet it seems that the growth of category killers will be more viciously restrained by continuous challenges from other businesses. The distribution industry is supposed to develop through such competition and restraint.

• Journal of the Korean Society for information Management
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• v.35 no.2
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• pp.37-62
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• 2018

This study examined the factors affecting the performance of automatic classification based on machine learning for domestic journal articles in the field of LIS. In particular, In view of the classification performance that assigning automatically the class labels to the articles in "Journal of the Korean Society for Information Management", I investigated the characteristics of the key factors(weighting schemes, training set size, classification algorithms, label assigning methods) through the diversified experiments. Consequently, It is effective to apply each element appropriately according to the classification environment and the characteristics of the document set, and a fairly good performance can be obtained by using a simpler model. In addition, the classification of domestic journals can be considered as a multi-label classification that assigns more than one category to a specific article. Therefore, I proposed an optimal classification model using simple and fast classification algorithm and small learning set considering this environment.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1053-1065
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• 2019

Land use statistics calculation is very informative data as the activity data for calculating exact carbon absorption and emission in post-2020. To effective interpretation by land use category, This study classify automatically image interpretation by land use category applying forest aerial photography (FAP) to deep learning model and calculate national unit statistics. Dataset (DS) applied deep learning is divided into training dataset (training DS) and test dataset (test DS) by extracting image of FAP based national forest resource inventory permanent sample plot location. Training DS give label to image by definition of land use category and learn and verify deep learning model. When verified deep learning model, training accuracy of model is highest at epoch 1,500 with about 89%. As a result of applying the trained deep learning model to test DS, interpretation classification accuracy of image label was about 90%. When the estimating area of classification by category using sampling method and compare to national statistics, consistency also very high, so it judged that it is enough to be used for activity data of national GHG (Greenhouse Gas) inventory report of LULUCF sector in the future.

• Journal of the Korean Society for information Management
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• v.23 no.2
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• pp.39-59
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• 2006

In this study we proposed a new informational structure and category labels to fully support the functions of school websites as an access tool to its contents. The proposed model was divided into three main aspects. First, main menu structure was the primary guideline to access information embedded in a website. Therefore, The proposed main menu structure consisted of 9 categories that are commonly provided by 17 existing school websites. Second, first-level categories consisted of total 35 categories under 9 main menu categories. Each category was placed under certain categories in main menu based on the relationships with the meaning of the upper level categories. Third, the proposed model adopted general and comprehensive terms as category labels. The terms used as category labels were based on the analysis of existing category labels, and the most frequently used terms were selected from the current school websites.

• Journal of The Korean Society of Clinical Toxicology
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• v.3 no.2
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• pp.71-78
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• 2005

Purpose: The morbidity of pesticides are largely related with accidental ingestion in human. The four principal ideals of clarity, completeness, conformity and consistency of label are important to make a correct usage and prevent unnecessary health risk. The aim of this study is to evaluate the appropriateness of pesticides labelling practice in Korea. Methods: The photographic label images of pesticide products were gathered through visiting thirteen manufacturers that produce pesticide products in Korea. We scored labelling practice by guidelines of Food and Agriculture Organization of the United Nations in 1995. Results: From August 2005 to November 2005, we gathered 1,296 label images of pesticide and $58.3{\%}$ (755/1,296) of images were scored by check lists for reviewing label content. The average score of four check list categories was $71.9{\pm}2.2$. Each categorical score were $91.7{\pm}0.9$ for the information appearing on the label, $31.3{\pm}0.0$ for safety precaution, $77.7{\pm}2.0$ for instructions for use, $87.0{\pm}8.7$ for general configurations. In safety precaution, the sentence of keeping locked up the product and two mandatory safety pictograms were missed in all label images. In general configurations category, there was score difference in product package types between bottle and bag container ($85.1{\pm}9.0$ vs. $90.3{\pm}7.2$, p < 0.01). Conclusion: Although there was no comparable previous data, the score of safety precaution was lowest than other categories because the two mandatory safety pictograms and locked up warning sentence were missed. In general configurations, the colour contrast was more inappropriate in the labels on bottle than bag container.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.9
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• pp.1224-1231
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• 2018

Automatic age estimation has been used in many social network applications, practical commercial applications, and human-computer interaction visual-surveillance biometrics. However, it has rarely been explored. In this paper, we propose an automatic age estimation system, which includes face detection and convolutional deep learning based on an inception module. The latter is a 22-layer-deep network that serves as the particular category of the inception design. To evaluate the proposed approach, we use 4,000 images of eight different age groups from the Adience age dataset. k-fold cross-validation (k = 5) is applied. A comparison of the performance of the proposed work and recent related methods is presented. The results show that the proposed method significantly outperforms existing methods in terms of the exact accuracy and off-by-one accuracy. The off-by-one accuracy is when the result is off by one adjacent age label to the above or below. For the exact accuracy, the age label of "60+" is classified with the highest accuracy of 76%.