• International Journal of Highway Engineering
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• v.13 no.1
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• pp.69-77
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• 2011

This study is to suggest a methodology to overcome the uncertainty and lack of reliability of data. The fuzzy reasoning model and the neural network model were developed in order to overcome the potential lack of reliability which may occur during the process of data collection. According to the result of comparison with the Poisson regression model, the suggested models showed better performance in the accuracy of the accident frequency prediction. It means that the more accurate accident frequency prediction model can be developed by the process of the uncertainty of raw data and the adjustment of errors in data by learning. Among the suggested models, the performance of the neural network model was better than that of the fuzzy reasoning model. The suggested models can evaluate the safety of signalized intersections in operation and/or planning, and ultimately contribute the reduction of accidents.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.105-108
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• 1997

Previous studies in stock market predictions using artificial intelligence techniques such as artificial neural networks and case-based reasoning, have focused mainly on spot market prediction. Korea launched trading in index futures market (KOSPI 200) on May 3, 1996, then more people became attracted to this market. Thus, this research intends to predict the daily up/down fluctuant direction of the price for KOSPI 200 index futures to meet this recent surge of interest. The forecasting methodologies employed in this research are the integration of genetic algorithm and artificial neural network (GAANN) and the integration of genetic algorithm and case-based reasoning (GACBR). Genetic algorithm was mainly used to select relevant input variables. This study adopts the categorical data preprocessing based on expert's knowledge as well as traditional data preprocessing. The experimental results of each forecasting method with each data preprocessing method are compared and statistically tested. Artificial neural network and case-based reasoning methods with best performance are integrated. Out-of-the Model Integration and In-Model Integration are presented as the integration methodology. The research outcomes are as follows; First, genetic algorithms are useful and effective method to select input variables for Al techniques. Second, the results of the experiment with categorical data preprocessing significantly outperform that with traditional data preprocessing in forecasting up/down fluctuant direction of index futures price. Third, the integration of genetic algorithm and case-based reasoning (GACBR) outperforms the integration of genetic algorithm and artificial neural network (GAANN). Forth, the integration of genetic algorithm, case-based reasoning and artificial neural network (GAANN-GACBR, GACBRNN and GANNCBR) provide worse results than GACBR.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.7
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• pp.1351-1362
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• 1992

Qualitative models in model-based expert system needs modeling paradigm which provides intelligent control of modeling assumptions and extracts robust inferences without quantitative information about the system to be modeled. Qualitative reasoning methodologies has proved the property of the completeness but not the soundness to the corresponding quantitative model. We propose new methodology of qualitative reasoning by introducing the concept of Centroid-Oriented Composite Interval to improve the soundness problem. Arithmetic operations and equivalence classes were composed using this definition. Qualitative simulation results were compared to Kuipers's results and the improvements in the soundness problem is verified.

• Research in Mathematical Education
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• v.12 no.2
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• pp.85-108
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• 2008

In this study, we created, implemented, and evaluated the impact of proportional reasoning authentic investigative activities on the mathematical content and pedagogical knowledge and attitudes of pre-service elementary and middle school mathematics teachers. For this purpose, a special teaching model was developed, implemented, and tested as part of the pre-service mathematics teacher training programs conducted in Israeli teacher colleges. The model was developed following pilot studies investigating the change in mathematical and pedagogical knowledge of pre- and in-service mathematics teachers, due to experience in authentic proportional reasoning activities. The conclusion of the study is that application of the model, through which the pre-service teachers gain experience and are exposed to authentic proportional reasoning activities with incorporation of theory (reading and analyzing relevant research reports) and practice, leads to a significant positive change in the pre-service teachers' mathematical content and pedagogical knowledge. In addition, improvement occurred in their attitudes and beliefs towards learning and teaching mathematics in general, and ratio and proportion in particular.

• The Journal of Information Systems
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• v.14 no.1
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• pp.41-65
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• 2005

The credit ration is a significant area of financial management which is of major interest to practitioners, financial and credit analysts. The components of credit rating are identified decision models are developed to assess credit rating an the corresponding creditworthiness of firms an accurately ad possble. Although many early studies demonstrate a priori which of these techniques will be most effective to solve a specific classification problem. Recently, a number of studies have demonstrate that a hybrid model integration artificial intelligence approaches with other feature selection algorthms can be alternative methodologies for business classification problems. In this article, we propose a hybrid approach using rough set theory as an alternative methodology to select appropriate attributes for case-based reasoning. This model uses rough specific interest lies in lthe stable combining of both rough set theory to extract knowledge that can guide dffective retrevals of useful cases. Our specific interest lies in the stable combining of both rough set theory and case-based reasoning in the problem of corporate credit rating. In addition, we summarize backgrounds of applying integrated model in the field of corporate credit rating with a brief description of various credit rating methodologies.

• Journal of the Korea Institute of Building Construction
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• v.12 no.2
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• pp.133-141
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• 2012

As we move from the industrial age to the information age, domestic industries are changing rapidly, and rural society is also laying the foundation to make use of information technologies. Through this kind of modernization, the size of agricultural facilities has been increasing on a significant scale. But, in reality, there are many difficulties in the maintenance of agricultural facilities in proportion to their growing number. Accordingly, this research aims to solve the fundamental problems that occur with agricultural facilities in the maintenance stage. In addition, it aims to provide information on how to maintain and manage facilities for farmers. The presentation of the maintenance information was conducted using a case-based reasoning method that solves current problems based on past cases. The tool of case-based reasoning was applied to define the establishment of the base for cases, characteristic variables and maintenance measures. The effectiveness of a CBR model was examined through the case study. The use of the case-based reasoning method is judged to be effective as a tool to support the decisions of farmers regarding maintenance. When the maintenance measures derived through the CBR model are offered to farmers, the fundamental problems of maintaining agricultural facilities will be solved, and the damage to such facilities minimized.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.29 no.1
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• pp.457-463
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• 2005

The use of the internet to facilitate commerce among companies promises vast benefits. Lots of e-marketplaces are building for several industries such as chemistry, airplane, and automobile industries. This study proposed new B2B EC business model for the shipping industry which concerns relatively massive fixed assets to be fully utilized. To be successful the proposed model gives participants to support useful information. To do this the expert system is constructed as the hybrid prediction system of neural network (NN) and memory based reasoning (MBR) with self-organizing map (SOM) and knowledge augmentaton technique using qualitative reasoning (QR). The expert system supports participants useful information coping with dynamic market environment. with this transportation companies are induced to participate in the proposed e-marketplace and helped for exchanges easily. Also participants would utilize their assets fully through B2B exchanges.

• The Journal of Information Systems
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• v.15 no.2
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• pp.95-123
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• 2006

It is needed to evaluate information systems actively which has already developed to improve future performance of the organization and foster the activation of information system. The introduction or development of information system also can bring about a organizational success. To measure exactly the organizational performance of information systems, it is needed to develop a new valuation model for a specific information system from a objective pint of view, as well as to equip a standard methodology using BSC measurement. The information system valuation from a objective point of view is of importance as the basic information for the decision to obtain information system. This paper takes aim at investigating a new information system valuation model and developing a information system valuation system using case-based reasoning for predicting currency value of information system in each organization. A new information system valuation system is developed as a web-enabling base. Using this, users are able to estimate the value of specific information system on a real time efficiently.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.383-386
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• 2005

This study proposes a case based reasoning system with two dimensional reduction techniques. In this study, vertical and horizontal dimensions of the research data are reduced through hybrid feature and instance selection process using genetic algorithms. We applied the proposed model to customer classification model which utilizes customers' demographic characteristics as inputs to predict their buying behavior for the specific product. Experimental results show that the proposed technique may improve the classification accuracy and outperform various optimized models of typical CBR system.

• Journal of Navigation and Port Research
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• v.29 no.4
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• pp.349-355
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• 2005

The use of the internet to facilitate commerce among companies promises vast benefits. Lots of e-marketplaces are building for several industries such as chemistry, airplane, and automobile industries. This study provides the new B2B EC business model for the shipping industry which concerns relatively massive fixed assets to be fully utilized. To be successful the proposed model gives participants useful information. To do this the expert system is constructed with the hybrid prediction system of neural network (NN) and memory based reasoning (MBR) with self-organizing map (SOM) and knowledge augmentation technique using qualitative reasoning (QR). The expert system supports participants useful information coping with dynamic market environment. with this shipping companies are induced to participate in the proposed e-marketplace and helped for exchanges easily. Also participants would utilize their assets fully through B2B exchanges.