• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.4
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• pp.251-258
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• 2005

Automatic document classification may differ significantly according to the characteristics of documents that are subject to classification, as well as classifier's performance. This research identifies e-mail document's characteristics to apply a three-step preprocessing algorithm that can minimize e-mail document's atypical characteristics. In the first 5go, uncertain based sampling algorithm that used Mean Absolute Deviation(MAD), is used to address the question of selection learning document for the rule generation at the time of classification. In the subsequent stage, Weighted vlaue assigning method by attribute is applied to increase the discriminating capability of the terms that appear on the title on the e-mail document characteristic level. in the third and last stage, accuracy level during classification by each category is increased by using Naive Bayesian Presumptive Algorithm's Dynamic Threshold. And, we implemented an E-Mail Recommendtion System using a three-step preprocessing algorithm the enable users for direct and optimal classification with the recommendation of the applicable category when a mail arrives.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.309-319
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• 2023

Recently, the frequency and intensity of meteorological disasters have increased due to climate change. In South Korea, there are regional differences in vulnerability and response capability to cope with climate change because of regional climate characteristics. In particular, drought results from various factors and is linked to extensive meteorological, hydrological, and agricultural impacts. Therefore, in order to effectively cope with drought, it is necessary to use a composite drought index that can take into account various factors, and to evaluate future droughts comprehensively considering climate change. This study evaluated hydrologic risk(${\bar{R}}$) of future drought in the Nakdong River basin based on the Dynamic Naive Bayesian Classification (DNBC)-based composite drought index, which was calculated by applying Standardized Precipitation Index (SPI), Streamflow Drought Index (SDI), Evaporate Stress Index (ESI) and Water Supply Capacity Index (WSCI) to the DNBC. The indices used in the DNBC were calculated using observation data and climate scenario data. A bivariate frequency analysis was performed for the severity and duration of the composite drought. Then using the estimated bivariate return periods, hydrologic risks of drought were calculated for observation and future periods. The overall results indicated that there were the highest risks during the future period (2021-2040) (${\bar{R}}$=0.572), and Miryang River (#2021) had the highest risk (${\bar{R}}$=0.940) on average. The hydrologic risk of the Nakdong River basin will increase highly in the near future (2021-2040). During the far future (2041-2099), the hydrologic risk decreased in the northern basins, and increased in the southern basins.