• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.667-679
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• 2014

National Health Insurance Service(NHIS) provide care-plans for beneficiaries in the long-term care insurance(LTCI) systems that help them use LTC services appropriately. The care-plan includes recommendations for the most adequate type of care (gold standard) for beneficiaries. This study develops a decision-support system to determine the appropriate type of care plan. To develop a model, we used a data set that well-trained assessors in the NHIS investigated as a gold standard for beneficiaries: nursing home care, home-visit care, home-visit bathing, home-visit nursing, or day and night care. The decision-support system was established through a decision-tree model, because it may be easy to explain the algorithm of a decision-support system to working groups and policy makers. Our results might be useful in evidence-based care planning in an LTCI system and contribute to the efficient use of LTC services.

• Asia pacific journal of information systems
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• v.20 no.4
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• pp.81-100
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• 2010

This study investigates the impact of website contents on Internet auction success. Based on the marketing concepts of stimuli, consumer behavior, and product involvement, the research model presents the theoretical relationships between the key factors of website contents and Internet auction performance. This study examines particularly four dimensions of website contents including transaction features, auction-specific features, seller's reputation, and information quality, which are deemed to have significant impact on the Internet auction performance, Each dimension of website contents is hypothesized to have a unique impact on a bidder's decision-making, which may vary depending on the bidder's level of involvement in the product. While transaction and auction-specific features serve as necessary components for successful auctions, a seller's reputation and information quality, as parts of satisfactory requirements, acutely affect bidders' decisions, especially those with high involvement to buy the product through a particular auction site. The outcomes of the analysis, in general, support the proposed model. The study results also provide meaningful Implications on ways in which auction websites can be improved for both sellers and auction service providers.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.637-649
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• 2022

Investors aim to increase their profitability by investing in the stock market. An adroit strategy for minimizing related risk lies through diversifying portfolio operationalization. In this paper, we propose a six-step stocks portfolio selection model. This model is based on data mining clustering techniques that reflect the ensuing impact of the political, economic, legal, and corporate governance in Mongolia. As a dataset, we have selected stock exchange trading price, financial statements, and operational reports of top-20 highly capitalized stocks that were traded at the Mongolian Stock Exchange from 2013 to 2017. In order to cluster the stock returns and risks, we have used k-means clustering techniques. We have combined both k-means clustering with Markowitz's portfolio theory to create an optimal and efficient portfolio. We constructed an efficient frontier, creating 15 portfolios, and computed the weight of stocks in each portfolio. From these portfolio options, the investor is given a choice to choose any one option.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3196-3203
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• 2010

The regional strategic industry support has been increasingly developed due to the change of environmental and political conditions for the last 10 years. As the paradigm changed from the central government-sponsored system to regional government-sponsored system, the policy has been diversified into various support systems such as regional strategic industry, specialized industry, supporting technology, supporting business, etc. It is crucial to establish the overall development plans and project strategies for the regional industry in terms of effectiveness and efficiency which will generate an integrated strategic frame to adjust overlapped supporting policies and offer a consumer-oriented service. Not only does the study focus on performance-based outcome regarding to a regional business (supporting policy) portfolio, but it also analyzes and proves an empirical decision-making model to draw out the priority and relative importance according to the regional strategic industry and support cases by analyzing the case of Chungnam area's strategic industry support in 2009. Following a priority list of 20 support cases selected from this research model by the Analytic Hierarchy Process (AHP), the result shows that the diverse support plans are needed according to the different strategic industries or the size of enterprise due to the fact that priority of project type varies upon the characteristics of strategic industry. Thus, the support project needs to generate various strategic industries and develop differentiated support policy to consolidate competitiveness of regional enterprises and revitalize the regional economy.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.1
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• pp.197-209
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• 2010

Supply Chain Management have been introduced and used as strategic weapon for many companies. Large investments in the SCM was made, but many companies are not fully getting the performance from the systems. The purpose of this study is to find the determinants of SCM difussion and performance in the perspective of Innovation and Information technology Acceptance. In developing the research model, The model consists of eight independent variables(Management support, Decision Making concentration, IS strategy, training education, relative advantage, technological compatibility, task compatibility, SCM cost), two moderator variables(interorganizational and intraorganizational diffusion), three dependant variables(efficiency, effectiveness, strategic advantage).

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.99-106
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• 2014

Generation and analysis methods have been proposed in recent years, such as using a natural language and formal language processing, artificial intelligence algorithms based knowledge model is effective meaning. its semantic based knowledge model has been used effective decision making tree and problem solving about specific context. and it was based on static generation and regression analysis, trend analysis with behavioral model, simulation support for macroeconomic forecasting mode on especially in a variety of complex systems and social network analysis. In this study, in this sense, integrating knowledge-based models, This paper propose a text mining derived from the inter-Topic model Integrated formal methods and Algorithms. First, a method for converting automatically knowledge map is derived from text mining keyword map and integrate it into the semantic knowledge model for this purpose. This paper propose an algorithm to derive a method of projecting a significant topic map from the map and the keyword semantically equivalent model. Integrated semantic-based knowledge model is available.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.153-158
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• 2006

The emergence of ubiquitous autonomic computing and network environment will change the service architecture information system which will be a new application area in SCM/logistics systems. In this study we surveyed the technical trend map of ubiquitous and its application in SCM/logistics support system design. We described the evolutional model of ubiquitous computing community for SCM/logistics system. It is consisted of three view points; self-growing, autonomic, and context-aware, which will allow the decision makers to be benefited from web and mobile technology and are useful for proactive SCM/logistics support system. Finally, we suggested a cooperative research planning for the development ubiquitous system between the government research center, university, and industry research activities.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.272-276
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• 2021

Presently, Online / Offline Users are facing cyber attacks every day. These cyber attacks affect user's performance, resources and various daily activities. Due to this critical situation, attention must be given to prevent such users through cyber attacks. The objective of this research paper is to improve the IDS systems by using machine learning approach to develop a hybrid model which controls the cyber attacks. This Hybrid model uses the available KDD 1999 intrusion detection dataset. In first step, Hybrid Model performs feature optimization by reducing the unimportant features of the dataset through decision tree, support vector machine, genetic algorithm, particle swarm optimization and principal component analysis techniques. In second step, Hybrid Model will find out the minimum number of features to point out accurate detection of cyber attacks. This hybrid model was developed by using machine learning algorithms like PSO, GA and ELM, which trained the system with available data to perform the predictions. The Hybrid Model had an accuracy of 99.94%, which states that it may be highly useful to prevent the users from cyber attacks.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.137-149
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• 2019

Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara-Selatan Expressway-NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.263-271
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• 2023

In the (3,3) close combat model based on the Lanchester Square Law, this study proposes a plan to optimally allocate residual combat power after the battle to other battlefields. As soon as the two camps of three units can grasp each other's information and predict the battle pattern immediately after the battle began, the Time Zero Allocation of Force (TZAF) scenario was used to initially allocate combat power to readjust the combat model. It reflects travel time, which is a "field friction" in which physical distance exists from battlefields that support combat power to battlefields that are supported. By developing existing studies that try to examine the effect of travel time on the battlefield through the combat model, this study forms a (3,3) combat model, which is a large number of minimum units. In order to achieve the combat purpose, the principle of optimal combat force operation is presented by examining the aspect that support combat power is allocated to the two battlefields and the consequent battle results. Through this, various scenarios were set in consideration of the travel time and the situation of the units, and differentiated results were obtained. Although the most traditional, it can be used as the basic logic of the training or the commander's decision-making system using the actual war game model.