• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.739-742
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• 2011

This paper describes system design and realization to the ubiquitous intelligent lesson management. This system includes students management by utilizing RFID and web-cam, personal security certification by fingerprint recognition, keyboard locking of PC by hooking technology, personal data management by cloud system, internet block access by a packet monitoring. We have design and realize this system, In the future, we will applicate our system to the classes using computer.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.1
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• pp.61-75
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• 2007

The purpose of this paper is to develop a new group classification method based on genetic algorithm and to com-pare its prediction performance with those of existing methods in the area of bond rating. To serve this purpose, we conduct various experiments with pilot and general models. Specifically, we first conduct experiments employing two pilot models : the one searching for the cluster center of each group and the other one searching for both the cluster center and the attribute weights in order to maximize classification accuracy. The results from the pilot experiments show that the performance of the latter in terms of classification accuracy ratio is higher than that of the former which provides the rationale of searching for both the cluster center of each group and the attribute weights to improve classification accuracy. With this lesson in mind, we design two generalized models employing genetic algorithm : the one is to maximize the classification accuracy and the other one is to minimize the total misclassification cost. We compare the performance of these two models with those of existing statistical and artificial intelligent models such as MDA, ANN, and Decision Tree, and conclude that the genetic algorithm-based group classification method that we propose in this paper significantly outperforms the other methods in respect of classification accuracy ratio as well as misclassification cost.