• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.10a
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• pp.28-50
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• 2008

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.354-360
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• 2022

Metabolic disease is a disease with a prevalence of 26% in Korean, and has three of the five states of abdominal obesity, hypertension, hunger glycemic disorder, high neutral fat, and low HDL cholesterol at the same time. This paper links the consumer panel data of the Rural Development Agency(RDA) and the medical care data of the National Health Insurance Service(NHIS) to generate a classification model that can be divided into a metabolic disease group and a control group through food consumption characteristics, and attempts to compare the differences. Many existing domestic and foreign studies related to metabolic diseases and food consumption characteristics are disease correlation studies of specific food groups and specific ingredients, and this paper is logistic considering all food groups included in the general diet. We created a classification model using regression, a decision tree-based classification model, and a classification model using XGBoost. Of the three models, the high-precision model is the XGBoost classification model, but the accuracy was not high at less than 0.7. As a future study, it is necessary to extend the observation period for food consumption in the patient group to more than 5 years and to study the metabolic disease classification model after converting the food consumed into nutritional characteristics.

• The Journal of Information Systems
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• v.26 no.4
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• pp.285-301
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• 2017

Purpose In this paper we verified the reliabilities of healthcare-related information provided by various users on the site of Naver Jisikin, a Korean typical search platform. Based on Q&A contents we validated answers' reliabilities to the asked questions about a lung cancer with the help of professors at a medical school. Design/methodology/approach The content analysis includes that the types of questions are classified into symptom/diagnosis, therapy, prognosis, after-management and so on. The answers contains advice, advertisement, oriental medicine, and religion as well as the above 5 question categories. The validation results of medical evidence about each answer show that only 49% among all answers have medical grounds. Findings We classified the medical grounded answers into three levels; high, medium and low. Among all answers we need to find out the answers including advertisement because the answers can be harmful to patients. We found the method to select the answers containing advertisement contents with the help of text mining research. The selection model presents high performance as 84% classification accuracy.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.24 no.3
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• pp.29-51
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• 2013

The purpose of this study is to develop internal tables in Christian religion in the 5th edition of Korean Decimal Classification. The scope of the Christianity, its structure of various classification schemes, and the concepts of internal tables were analyzed. The contents of several textbooks were analyzed for the scope of the discipline and the classification schemes and internal tables of DDC, UDC, NDC, LCC, Classification of the Library of Union Theological Seminary and the Classification of the Korea Theological Library were compared. And then, internal tables in Bible, sermon, worship, church history were built and those tables were evaluated by librarians and experts in the fields. And finally, internal tables of the Christiainity and new headings were suggested. New internal tables in Christianity will increase the effectiveness of information retrieval and it will provide a foundation for developing internal tables in other disciplines.

• Journal of Digital Contents Society
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• v.15 no.4
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• pp.559-568
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• 2014

Previous polarity classification using sentiment analysis utilizes a sentence rule by product reviews based rating points. It is difficult to be applied to blogs which have not rating of product reviews and is possible to fabricate product reviews by comment part-timers and managers who use web site so it is not easy to understand a product and store reviews which are reliability. Considering to these problems, if we analyze blogs which have personal and frank opinions and classify polarity, it is possible to understand rightly opinions for the product, store. This paper suggests that we extract high frequency vocabularies in blogs by several domains and choose topic words. Then we apply a technique of sentiment analysis and classify polarity about contents of blogs. To evaluate performances of sentiment analysis, we utilize the measurement index that use Precision, Recall, F-Score in an information retrieval field. In a result of evaluation, using suggested sentiment analysis is the better performances to classify polarity than previous techniques of using the sentence rule based product reviews.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.77-86
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• 2019

This paper proposes using machine-learning technology to analyze and classify historical collected documents based on them. Data is collected based on keywords associated with a specific domain and the non-conceptuals such as special characters are removed. Then, tag each word of the document collected using a Korean-language morpheme analyzer with its nouns, verbs, and sentences. Embedded documents using Doc2Vec model that converts documents into vectors. Measure the similarity between documents through the embedded model and learn the document classifier using the machine running algorithm. The highest performance support vector machine measured 0.83 of F1-score as a result of comparing the classification model learned.

• Journal of Convergence for Information Technology
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• v.9 no.5
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• pp.1-6
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• 2019

As the mobile market expands, a variety of platforms are available to provide multimodal media content. Multimodal media content contains heterogeneous data, accordingly, user requires much time and effort to select preferred content. Therefore, in this paper we propose multimodal media content classification using keyword weighting for recommendation. The proposed method extracts keyword that best represent contents through keyword weighting in text data of multimodal media contents. Based on the extracted data, genre class with subclass are generated and classify appropriate multimodal media contents. In addition, the user's preference evaluation is performed for personalized recommendation, and multimodal content is recommended based on the result of the user's content preference analysis. The performance evaluation verifies that it is superiority of recommendation results through the accuracy and satisfaction. The recommendation accuracy is 74.62% and the satisfaction rate is 69.1%, because it is recommended considering the user's favorite the keyword as well as the genre.

• Journal of Environmental Health Sciences
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• v.22 no.1
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• pp.28-35
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• 1996

In order to investigate the contents of total mercury in zoo animal located near in Seoul, we measured the contents of total mercury in fur and feather collected in zoo animal by the Mercury Analyzer. The contents of total mercury in mammals was $61.56\pm 20.32 \mu g/kg$, but in birds $659.49\pm 162.73 \mu g/kg$. Compared with feeding pattern, the contents of total mercury of omniverous and carniverous were detected higher than those of herbiverous in mammals, and also same as in birds. The contents of total mercury of Cuculidae and Ciconidae were detected highestly among classification of family in zoo animal, but those of Camelidae and Cervidae were detected lowestly. Of carniverous, 30.5% was higher than $1000 \mu g/kg$, but the ratio of omniverous detected less than $100 \mu g/kg$ was 45.5%, and in herbiverous 95.4%.

• Journal of Korean Academy of Nursing Administration
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• v.20 no.5
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• pp.545-557
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• 2014

Purpose: The purpose of this study was to develop a patient classification system based on nursing care intensity for patients with acute stroke-related symptoms and verify its validity and reliability. Methods: Data were collected between November, 2013 and February, 2014. The verification for content validity of the patient classification system was conducted by a group of seven professionals. Both interrater reliability and concurrent validity were verified at stroke units in tertiary hospitals. Results: The intensive nursing care for acute stroke patients consisted of 14 classified domains and 56 classified contents by adding 'neurological assessment and observation' and 'respiratory care': 'hygiene', 'nutrition', 'elimination', 'mobility and exercise', 'education or counselling', 'emotional support', 'communication', 'treatment and examination', 'medication', 'assessment and observation', 'neurological assessment and observation', 'respiratory care', 'coordination between departments', and 'discharge or transfer care'. Each domain was classified into four levels such as Class I, Class II, Class III, and Class IV. Conclusion: The results show that this patient classification system has satisfactory validity for content and concurrent and verified reliability and can be used to accurately estimate the demand for nursing care for patients in stroke units.

• Journal of Internet Computing and Services
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• v.11 no.3
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• pp.15-24
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• 2010

In this paper, we propose an image classification which combines semantic relations of tags with contents of images to improve the satisfaction of image retrieval on application domains as huge image sharing sites. To make good use of image retrieval or classification algorithms on huge image sharing sites as Flickr, they are applicable to real tagged Web images. To classify the Web images by 'bag of visual word' based image content, our algorithm includes training the category model by utilizing the preliminary retrieved images with semantically related tags as training data and classifying the test images based on PLSA. In the experimental results on the Flickr Web images, the proposed method produced the better precision and recall rates than those from the existing method using tag information.