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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Journal of Intelligence and Information Systems
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Korea Inteligent Information System Society
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Volume & Issues
Volume 10, Issue 3 - Dec 2004
Volume 10, Issue 2 - Nov 2004
Volume 10, Issue 1 - Jun 2004
Selecting the target year
Image Emphasis by Histogram Reverse Tracking Alteration
Journal of Intelligence and Information Systems , volume 10, issue 1, 2004, Pages 1~11
It is very important part of pre-processing for get better results by image processing that get emphasized image by processing of source image. Emphasized image is not only good looking image but clear and sharp image. Emphasized images are used very useful data at contour extraction and image recognition in image processing. It have different image recognition by how much represent a origin scene in row quality image. Present algorithms that get emphasized premier image do not get clear picture of degree that want in various kind of images and there is shortcoming that need much process times being proportional size of picture quality or accumulation degree of histogram. In this paper, we propose method to change distribution chart that pixels occupy in histogram as subsequentness in reflex of various kinds as well as that picture quality reflex method to emphasize so that is suitable in practical use purpose originally of premier. Proposed algorithm re-allot histogram distribution by reverse tracking histogram. Experimental images are same result and take less processing time than histogram equalization.
An Optimal Supplier Selection Model with a Sensitivity Analysis in the Online Shopping Environment
Journal of Intelligence and Information Systems , volume 10, issue 1, 2004, Pages 13~25
In the online shopping environment, consumers suffer from the process of selecting an optimal supplier. Although comparison shopping agent-based web sites and consumers' online community sites support the selection process, they have limitations when considering diverse and dynamic purchase conditions as a whole, which is the cause of additional consumer effort for optimal supplier selection. This study provides a decision support model with a sensitivity analysis for selecting an optimal supplier considering purchase conditions as a whole. It screens suppliers with filtering factors and provides optimal suppliers through a sensitivity analysis from a Quadratic Programming model. We implemented a prototype system and showed that it could be an effective decision support system for selecting the optimal supplier in the online shopping environment.
An Approach to Structuralizing Business Information for Internet Shopping Malls
Journal of Intelligence and Information Systems , volume 10, issue 1, 2004, Pages 27~45
While on-line shopping is increasing, the "Consumer Protection Law in Electronic Commerce" obliges each internet shopping mall to provide its business information. Although most internet shopping malls provide their business information in the semi-structured format on the bottom of their homepages, the attributes and expression forms of business information are different each other. It makes consumers difficult to identify their business information and lowers public confidence. Hence this study proposes three approaches - HTML-based structure, XML-based structure, and XML data island-based structure - to structuralizing business information for correct expression. The experiment results showed that the business information extraction time by XML data island-based structure is independent of the size of the web document, while the time by HTML-based structure is dependent on the size. By comparing the business information extraction times, we show that XML data island-based structure is more efficient and effective than HTML-based structure.structure.
Methodology for Automate Negotiation for Order Transaction of Injection Mold Manufacturer
Journal of Intelligence and Information Systems , volume 10, issue 1, 2004, Pages 47~63
Today, there are several markets in cyber space where companies trade electronically due to the development of Information Technology. On the other hand, the most important thing in trades is negotiation. So, in order to support current business practices as well as new ones on the Internet, electronic commerce systems need an ability to negotiate. In this paper, proposed is a method by which a seller can be supported by an agent which plays a role in negotiation process among small and medium companies especially injection mold manufacturer. If the manufacturing capacity cannot afford to produce all orders, the manufacturer may want to extend due dates and the buyers may want to discount prices. The negotiation agent discussed in this paper cooperates with the schedule agent to get due-date information, and performs a role in one (seller)-to-many (buyer) negotiation processes.
Implementation and Design of Artificial Intelligence Face Recognition in Distributed Environment
Journal of Intelligence and Information Systems , volume 10, issue 1, 2004, Pages 65~75
It is notorious that PIN(Personal Identification Number) is used widely for user verification and authentication in networked environment. But, when the user Identification and password are exposed by hacking, we can be damaged monetary damage as well as invasion of privacy. In this paper, we adopt face recognition-based authentication which have nothing to worry what the ID and password will be exposed. Also, we suggest the remote authentication and verification system by considering not only 2-Tier system but also 3-Tier system getting be distributed. In this research, we analyze the face feature data using the SVM(Support Vector Machine) and PCA(Principle Component Analysis), and implement artificial intelligence face recognition module in distributed environment which increase the authentication speed and heightens accuracy by utilizing artificial intelligence techniques.
A Neural Network Approach to Compare Predictive Value of Accounting Versus Market Data
Kim, Choong-Nyoung ; Jun, Sang-gyung ; Kinsun Tam ;
Journal of Intelligence and Information Systems , volume 10, issue 1, 2004, Pages 77~91
This research compares the use of accounting data versus market data in the prediction of bankruptcy. Comparison is made through neural networks so that prediction accuracy is model-independent. Results of this study indicate that both market and accounting data provide useful information on corporate bankruptcies. Interestingly, using market and accounting information together can achieve substantial gain in prediction accuracy.
Usability Test of Website Navigation by Using Spatial Metaphor Concept
Journal of Intelligence and Information Systems , volume 10, issue 1, 2004, Pages 93~107
This study is concerned with proposing a new construct named "spatial metaphor" in the field of user interface design for web. Recently, web has been recognized as an important vehicle of delivering messages and information to customers. Since both hyperlink and multimedia technology are crucial part of web, its user interface requires a new approach to enhance user's acceptance of web. In this sense, we introduced a new concept named "spatial metaphor" instead of hierarchical menus. As a theoretical basis, Davis (1986)'s TAM(Technology Acceptance Model) was used to test the statistical validity of the proposed spatial metaphor. For test web site, we developed a prototype designed by using atomic-web system and spatial metaphor. By using the prototype, we built a web-based questionnaire system so that respondents can use it directly before answering the questionnaire. To prove its statistical validity, we collected valid questionnaires and tested with LISREL. In this way, statistical validity of our proposed approach was proven.approach was proven.
Data Mining using Instance Selection in Artificial Neural Networks for Bankruptcy Prediction
Kim, Kyoung-jae ;
Journal of Intelligence and Information Systems , volume 10, issue 1, 2004, Pages 109~123
Corporate financial distress and bankruptcy prediction is one of the major application areas of artificial neural networks (ANNs) in finance and management. ANNs have showed high prediction performance in this area, but sometimes are confronted with inconsistent and unpredictable performance for noisy data. In addition, it may not be possible to train ANN or the training task cannot be effectively carried out without data reduction when the amount of data is so large because training the large data set needs much processing time and additional costs of collecting data. Instance selection is one of popular methods for dimensionality reduction and is directly related to data reduction. Although some researchers have addressed the need for instance selection in instance-based learning algorithms, there is little research on instance selection for ANN. This study proposes a genetic algorithm (GA) approach to instance selection in ANN for bankruptcy prediction. In this study, we use ANN supported by the GA to optimize the connection weights between layers and select relevant instances. It is expected that the globally evolved weights mitigate the well-known limitations of gradient descent algorithm of backpropagation algorithm. In addition, genetically selected instances will shorten the learning time and enhance prediction performance. This study will compare the proposed model with other major data mining techniques. Experimental results show that the GA approach is a promising method for instance selection in ANN.