• Asia pacific journal of information systems
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• v.7 no.1
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• pp.99-111
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• 1997

This paper presents an application of an artificial neural net to the implementation of decision class analysis (DCA), together with the generation of a decision model influence diagram. The diagram is well-known as a good tool for knowledge representation of complex decision problems. Generating influence diagram model is known to in practice require much time and effort, and the resulting model can be generally applicable to only a specific decision problem. In order to reduce the burden of modeling decision problems, the concept of DCA is introduced. DCA treats a set of decision problems having some degree of similarityz as a single unit. We propose a method utilizing a feedforward neural net with supervised learning rule to develop DCA based on influence diagram, which method consists of two phases: Phase l is to search for relevant chance and value nodes of an individual influence diagram from given decision and specific situations and Phase II elicits arcs among the nodes in the diagram. We also examine the results of neural net simulation with an example of a class of decision problems.

• Korean Management Science Review
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• v.27 no.1
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• pp.33-43
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• 2010

This paper presents a method for the estimation and analysis of the economic value of weather forecasts for CRM decision-making problems in the leisure industry. Value is calculated in terms of the customer's satisfaction returned from the user's decision under the specific payoff structure, which is itself represented by a customer's satisfaction ratio model. The decision is assessed by a modified cost-loss model to consider the customer's satisfaction instead of the loss or cost. Site-specific probability and deterministic forecasts, each of which is provided in Korea and China, are applied to generate and analyze the optimal decisions. The application results demonstrate that probability forecasts have greater value than deterministic forecasts, provided that the users can locate the optimal decision threshold. This paper also presents the optimal decision strategy for specific customers with a variety of satisfaction patterns.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.5
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• pp.57-67
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• 2012

This paper presents the Multi Criteria Decision Making (MCDM) to evaluate water resources plan for agricultural reservoir. Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE) and Analytic Hierarchy Process (AHP) were used to estimate weight and priority of alternatives to find out the most reasonable and efficient way of water resources assessment. The 6 criteria that both decision maker and beneficiary are satisfied have been identified to secure agricultural water resources and then the priority of 10 subcriteria was set. An enhanced PROMETHEE-AHP model was used to perform pairwise comparison and find out the priority of each alternative because the existing decision making model have uncertainty and ambiguity. Comparison analysis of decision making models was carried out to find a way of suitable decision making and validity of PROMETHEE-AHP model was suggested.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.82-93
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• 2005

Facility layout problems (FLP) are usually treated as design problems. Lack of systematic and objective tools to compare design alternatives results in decision-making to be dominated by the experiences or preferences of designers or managers. To increase objectivity and effectiveness of decision-making in facility layout selections, a decision support model is necessary. We proposed a decision model, which regards the FLP as a multi-attribute decision making (MADM) problem. We identify sets of attributes crucial to layout selections, quantitative indices for attributes, and methods of ranking alternatives. For a requested facility layout design, many alternatives could be developed. The enormous alternatives, various attributes, and comparison of assigned qualitative values to each attribute, form a complicated decision problem. To treat facility layout selection problems as a MADM problem, we used the linear assignment method to rank before selecting those high ranks as candidates. We modelled the application of the Nemawashi process to simulate the group decision-making procedure and help efficiently achieve agreement. The electronics manufacturing service (EMS) industry has frequent and costly facility layout modifications. Our models are helpful to them. We use an electronics manufacturing service company to illustrate the decision-making process of our models.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.109-118
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• 2023

Data-based analysis methods have become used more for estimating or predicting housing prices, and neural network models and decision trees in the field of big data are also widely used more and more. Neural network models are often evaluated to be superior to existing statistical models in terms of estimation or prediction accuracy. However, there is ambiguity in determining the input feature of the input layer of the neural network model, that is, the type and number of input features, and decision trees are sometimes used to overcome these disadvantages. In this paper, we evaluate the existing methods of using decision trees and propose the method of using decision trees to prioritize input feature selection in neural network models. This can be a complementary or combined analysis method of the neural network model and decision tree, and the validity was confirmed by applying the proposed method to house price estimation. Through several comparisons, it has been summarized that the selection of appropriate input characteristics according to priority can increase the estimation power of the model.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.150-154
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• 2024

In recent years, there are extensive researches on the applications of machine learning to the automation and decision support for medical experts during disease detection. However, the performance of machine learning still needs improvement so that machine learning model produces result that is more accurate and reliable for disease detection. Selecting the hyper-parameter that could produce the possible maximum classification accuracy on medical dataset is the most challenging task in developing decision support systems with machine learning algorithms for medical dataset classification. Moreover, selecting the features that best characterizes a disease is another challenge in developing machine-learning model with better classification accuracy. In this study, we have proposed an optimized decision tree model for heart disease classification by using heart disease dataset collected from kaggle data repository. The proposed model is evaluated and experimental test reveals that the performance of decision tree improves when an optimal number of features are used for training. Overall, the accuracy of the proposed decision tree model is 98.2% for heart disease classification.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.8B
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• pp.562-568
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• 2005

The content of this paper is concerned with the pricing decision model when the new telecommunication service enter into market. The pricing decision model of firm level is based on the problems of the previous service pricing model that are abstracted from the literature survey. We suggest a new pricing model and prove the model's fitness using the way of empirical simulation study. We empirically apply the proposed model to obtain the price level of such a new service as the convergence service between mobile communication service and broadcasting service. finally, we prove that the proposed model is successful because we get the new . service price based on the pricing decision model suggested in this paper.

• Journal of Korea Game Society
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• v.11 no.6
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• pp.61-70
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• 2011

In this paper, we propose a defense NPC control model in the soccer game by applying the Decision Tree learning algorithm. The proposed model extracts the direction patterns and the action patterns generated by many soccer game users, and applies these patterns to the Decision Tree learning algorithm. Then, the proposed model decides the direction and the action according to the learned Decision Tree. Experimental results show that the proposed model takes some time to learn the Decision Tree while the proposed model takes 0.001-0.003 milliseconds to decide the direction and the action based on the learned Decision Tree. Therefore, the proposed model can control NPC in the soccer game system in real time. Also, the proposed model achieves higher accuracy than a previous model (Letia98); because the proposed model can utilize current state information, its analyzed information, and previous state information.

• Korean Journal of Organic Agriculture
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• v.26 no.2
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• pp.181-202
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• 2018

Most of eco-friendly agrifood processing enterprises in Korean rural area are small and medium-sized business. For this reason, it's hard for eco-friendly agrifood processing enterprises to neither analyze business performance for efficient business management nor establish their own business plan for rational decision-making. Therefore it's necessary to design effective mathematical programming model and to make practical application which can support rational management decision-making ensuring the stable business activity of eco-friendly agrifood processing enterprises. Accordingly this paper focuses on the designing and its application of multi-objective mathematical programming model using goal programming to support rational decision-making of eco-friendly agrifood processing enterprise. Hansalimanseongmachum Food Inc. which runs soy bean processing business making tofu based on regional-based soybean farms around Anseong City will be the specific case to apply multi-objective mathematical programming model in practice. And it will suggest measures to support rational management decision-making of other eco-friendly agrifood processing enterprises.

• Journal of Korea Game Society
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• v.14 no.4
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• pp.37-44
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• 2014

In this paper, we propose a NPC decision making model based on Prospect Theory which tries to model real-life choice, rather than optimal decision. For this purpose, we analyse the problems of reference point setting, diminishing sensitivity and loss aversion which are known as limitations of the utility theory and then apply these characteristics into the decision making in game. Dynamic Terrain Analysis is utilized to evaluate the proposed model and experimental result shows the method have effects on inducing diverse personality and emergent behavior on NPC.