• Journal of Digital Convergence
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• v.11 no.5
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• pp.371-384
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• 2013

The purpose of this study was to develop the risk-adjusted mortality model using Korean Hospital Discharge Injury data and US National Hospital Discharge Survey data and to suggest some ways to manage hospital mortality rates through comparison of Korea and United States Hospital Standardized Mortality Ratios(HSMR). This study used data mining techniques, decision tree and logistic regression, for developing Korea and United States risk-adjustment model of in-hospital mortality. By comparing Hospital Standardized Mortality Ratio(HSMR) with standardized variables, analysis shows the concrete differences between the two countries. While Korean Hospital Standardized Mortality Ratio(HSMR) is increasing every year(101.0 in 2006, 101.3 in 2007, 103.3 in 2008), HSMR appeared to be reduced in the United States(102.3 in 2006, 100.7 in 2007, 95.9 in 2008). Korean Hospital Standardized Mortality Ratios(HSMR) by hospital beds were higher than that of the United States. A two-aspect approach to management of hospital mortality rates is suggested; national and hospital levels. The government is to release Hospital Standardized Mortality Ratio(HSMR) of large hospitals and to offer consulting on effective hospital mortality management to small and medium hospitals.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.245-250
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• 2017

Flash floods is defined as the flooding of intense rainfall over a relatively small area that flows through river and valley rapidly in short time with no advance warning. So that it can cause damage property and casuality. This study is to establish the flash-flood warning system using 38 accident data, reported from the National Disaster Information Center and Land Surface Model(TOPLATS) between 2009 and 2012. Three variables were used in the Land Surface Model: precipitation, soil moisture, and surface runoff. The three variables of 6 hours preceding flash flood were reduced to 3 factors through factor analysis. Decision tree, random forest, Naive Bayes, Support Vector Machine, and logistic regression model are considered as big data methods. The prediction performance was evaluated by comparison of Accuracy, Kappa, TP Rate, FP Rate and F-Measure. The best method was suggested based on reproducibility evaluation at the each points of flash flood occurrence and predicted count versus actual count using 4 years data.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.333-336
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• 2006

E-marketplace is a kind of B2B e-Business system that supports business transactions among companies. If e-marketplace is revitalized, we expect not only the development of related industry but also decrease of transaction cost among companies. It is necessary for the introduction and revitalization of e-marketplace in tourist industry from this point of view. Participants of tour e-marketplace are tour-related companies(travel agencies, lodging enterprises, shipping enterprises, etc.). Also tourists want to search a variety of tour products or contents. So tour e-marketplace has characteristics of B2C e-Business systems as well as B2B e-Business systems at once. The purpose of this study is to classify success factors that determine characteristics of tour e-marketplace through statistics survey from e-marketplace factors related tourism websites. First of all, we analyze success factors of B2B and B2C e-marketplace. Then we will set up influence factors of tour e-marketplace and conduct a survey about success factors of tour e-marketplace. Therefore, we could expect to find these good attributes in tour e-marketplace success through logistic regression and decision tree analysis from source data.

• Journal of the Korean Home Economics Association
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• v.41 no.10 s.188
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• pp.101-116
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• 2003

The study was conducted to establish hygienic standards of salad items for pizza restaurant located in Seoul by applying HACCP system during the summer of 2000. The study measured temperature, time, pH, Aw and microbial assessments. The hygienic conditions of the kitchen and workers were on the average(1.21, 1.0 out of 3 pts.), but some improvement should be made: separate use of trash can and leftover disposal, separate use of knives and cutting boards, habits for hand washing and wearing hygienic gloves. For salad production, all procedures were peformed under food safety danger zone ($5{\~}60^{\circ}C$). The ingredients were mostly above pH 5.0 and high in Aw($0.94{\~}0.99$). Microbial assessments for salad production revealed that TPC($1.8{\times}10^3{\~}1.0{\times}10^{10}CFU/g$) and coliforms($1.5{\times}10{\~}5.2{\times}10^5 CFU/g$) exceeded the standards by Solberg et al.(TPC: $10^6CFU/g$, coliforms: $10^3CFU/g$). S. aureus was not detected but Salmonella was found in three food items(egg, macaroni and macaroni salad). Moreover, the workers' hands contained 3.1 104 CFU/g of TPC and 4.2 102 CFU/g of S. aureus requiring further remedy since it exceeded the safety standards suggested by Harrigan and McCance (500 CFU/g of TPC per $100cm^2$ and 10 CFU/g of coliforms per $100cm^2$). According to the critical control point(CCP) decision tree analysis, vegetable receiving, vegetable holding, mixing, display on coleslaw, macaroni draining, display on macaroni salad, egg peeling & cutting, apple cutting, and display on salad bar were determined as CCPs. From the findings it would be suggested that purchase of Quality materials, short holding and display time, storing food at right temperature, using sanitary cooking utensils, and improvement of workers' food handing practices are needed to ensure the safe salad production in this specific pizza restaurant.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.83-90
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• 2004

Strategic games are missing special qualities of genre these days. Game engines neither reason about behaviors of computer objects nor have learning ability that can prepare countermeasure in variously command user's strategy. This paper suggests a strategic game engine that applies non-monotonic reasoning and inductive machine learning. The engine emphasizes three components -“user behavior monitor”to abstract user's objects behavior,“learning engine”to learn user's strategy,“behavior display handler”to reflect abstracted behavior of computer objects on game. Especially, this paper proposes two layered-structure to apply non-monotonic reasoning and inductive learning to make behaviors of computer objects that learns strategy behaviors of user objects exactly, and corresponds in user's objects. The engine decides actions and strategies of computer objects with created information through inductive learning. Main contribution of this paper is that computer objects command excellent strategies and reveal differentiation with behavior of existing computer objects to apply non-monotonic reasoning and inductive machine learning.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.148-160
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• 2003

TMA(Tissue Mineral Analysis) is very popular method in hair mineral analysis for health care professionals in over 48 countries medical center. Assesment of nutritional minerals and toxic elements in the hair is very important not only for determining adequacy, deficiencies and unbalance, but also for assessing their relative relationships in a body. In Korea, there are some problems in TMA method. Because of not haying a medical information database which is suitable for korean to do analyze, the requested TMA has to send to TEI-USA. However, as the TMA results from TEI-USA is composed of English documents and graphic files prohibited to open, its usability is very low and a lot of dollars has to be payed. Also, it can make some problems in the reliability of the TMA results, since the TMA results are based on the database of western health and mineral standards, To solve these problems, I developed the first Medical Information System of TMA in Korea here. The system can analyze the complex tissue mineral data with multiple stage decision tree classifier. It is also constructed with multiple fuzzy database and hence analyze the TMA data by fuzzy inference methods. The effectiveness test of this systems can be shown the increased business efficiency and satisfaction rate 86% and 92% respectively.

• Information Systems Review
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• v.10 no.2
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• pp.137-158
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• 2008

Rapid development of various information technologies creates new opportunities in online and offline markets. In this changing market environment, customers have various demands on new products and services. Therefore, their power and influence on the markets grow stronger each year. Companies have paid great attention to customer relationship management. Especially, personalized product recommendation systems, which recommend products and services based on customer's private information or purchasing behaviors in stores, is an important asset to most companies. CRM is one of the important business processes where reliable information is mined from customer database. Data mining techniques such as artificial intelligence are popular tools used to extract useful information and knowledge from these customer databases. In this research, we propose a recommendation system that predicts customer's purchase intention. Then, customer's purchasing intention of specific product is predicted by using data mining techniques using receipt data set. The performance of this suggested method is compared with that of other data mining technologies.

• The Journal of Engineering Geology
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• v.30 no.3
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• pp.315-325
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• 2020

This study was performed to develop a model to predict landslides and determine the variable importance of landslides susceptibility factors based on the probabilistic prediction of landslides occurring on slopes along the road. Field survey data of 30,615 slopes from 2007 to 2020 in Korea were analyzed to develop a landslide prediction model. Of the total 131 variable factors, 17 topographic factors and 114 geological factors (including 89 bedrocks) were used to predict landslides. Automated machine learning (AutoML) was used to classify landslides and non-landslides. The verification results revealed that the best model, an extremely randomized tree (XRT) with excellent predictive performance, yielded 83.977% of prediction rates on test data. As a result of the analysis to determine the variable importance of the landslide susceptibility factors, it was composed of 10 topographic factors and 9 geological factors, which was presented as a percentage for each factor. This model was evaluated probabilistically and quantitatively for the likelihood of landslide occurrence by deriving the ranking of variable importance using only on-site survey data. It is considered that this model can provide a reliable basis for slope safety assessment through field surveys to decision-makers in the future.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.153-177
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• 2001

Nowadays, product recommendation is one of the important issues regarding both CRM and Internet shopping mall. Generally, a recommendation system tracks past actions of a group of users to make a recommendation to individual members of the group. The computer-mediated marketing and commerce have grown rapidly and thereby automatic recommendation methodologies have got great attentions. But the researches and commercial tools for product recommendation so far, still have many aspects that merit further considerations. To supplement those aspects, we devise a recommendation methodology by which we can get further recommendation effectiveness when applied to Internet shopping mall. The suggested methodology is based on web log information, product taxonomy, association rule mining, and decision tree learning. To implement this we also design and intelligent Internet shopping mall support system based on agent technology and develop it as a prototype system. We applied this methodology and the prototype system to a leading Korean Internet shopping mall and provide some experimental results. Through the experiment, we found that the suggested methodology can perform recommendation tasks both effectively and efficiently in real world problems. Its systematic validity issues are also discussed.

• Journal of Korea Technology Innovation Society
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• v.19 no.3
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• pp.511-544
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• 2016

Standards boost technological innovation by promoting information sharing, compatibility, stability and quality. Identifying groups of companies that particularly benefit from these functions of standards in their technological innovation and commercialization helps to customize planning and implementation of standards-related policies for demand groups. For this purpose, this study engages in profiling of SMEs whose R&D objective is to respond to standards as well as those who need to implement standards system for technological commercialization. Then it suggests a prediction model that can distinguish such companies from others. To this end, decision tree analysis is conducted for profiling of characteristics of subject SMEs through data mining. Subject SMEs include (1) those that engage in R&D to respond to standards (Group1) or (2) those in need of product standard or technological certification policies for commercialization purposes (Group 2). Then the study proposes a prediction model that can distinguish Groups 1 and 2 from others based on several variables by adopting discriminant analysis. The practicality of discriminant formula is statistically verified. The study suggests that Group 1 companies are distinguished in variables such as time spent on R&D planning, KoreanStandardIndustryClassification (KSIC) category, number of employees and novelty of technologies. Profiling result of Group 2 companies suggests that they are differentiated in variables such as KSIC category, major clients of the companies, time spent on R&D and ability to test and verify their technologies. The prediction model proposed herein is designed based on the outcomes of profiling and discriminant analysis. Its purpose is to serve in the planning or implementation processes of standards-related policies through providing objective information on companies in need of relevant support and thereby to enhance overall success rate of standards-related projects.