• Journal of KIISE
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• v.42 no.9
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• pp.1124-1132
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• 2015

In order to train a supervised machine learning algorithm, it is necessary to have non-biased labels and a sufficient amount of training data. However, it is difficult to collect the required non-biased labels and a sufficient amount of training data to develop an automatic English Composition scoring system. In addition, an English writing assessment is carried out using a multi-faceted evaluation of the overall level of the answer. Therefore, it is difficult to choose an appropriate machine learning algorithm for such work. In this paper, we show that it is possible to alleviate these problems through ensemble learning. The results of the experiment indicate that the ensemble technique exhibited an overall performance that was better than that of other algorithms.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.173-180
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• 2015

The advent of personal healthcare record(PHR) technology has been changing the uses as well as the paradigm of internet services, and emphasizing the importance of services being personalization. But the problem of user's privacy infringement and leaking user's sensitive medical information is increasing with the fusion of PHR technology and healthcare. In this paper, we propose a security labeling scheme for privacy protection in PHR system. In the proposed scheme, PHR data can be labeled also manually based on patient's request or the security labelling rules. The proposed scheme can be used to control access, specify protective measures, and determine additional handling restrictions required by a communications security policy.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.1-8
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• 2023

In this paper, we propose a training method of a recommendation learning model that analyzes the battlefield situation and recommends a suitable hypothesis for the current situation. The proposed learning model uses the preference determined by comparing the two hypotheses as a label data to learn which hypothesis best analyzes the current battlefield situation. Our model is based on Siamese neural network architecture which uses the same weights on two different input vectors. The model takes two hypotheses as an input, and learns the priority between two hypotheses while sharing the same weights in the twin network. In addition, a score is given to each hypothesis through the proposed post-processing ranking algorithm, and hypotheses with a high score can be recommended to the commander in charge.