• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.33 no.4
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• pp.251-262
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• 2007

In this study, the antioxidative effects, inhibitory effects on elastase, and components of Aspalathus linearis extracts were investigated. The free radical (1,1-diphenyl-2-picrylhydrazyl, DPPH) scavenging activities ($FSC_{50}$) of extract/fractions of Aspalathus linearis were in the order: 50 % ethanol extract ($11.50\;{\mu}g/mL$) < deglycosylated flavonoid aglycone fraction ($8.47\;{\mu}g/mL$) < ethylacetate fraction ($4.76\;{\mu}g/mL$). Reactive oxygen species (ROS) scavenging activities ($OSC_{50}$) of some Aspalathus linearis extracts on ROS generated in $Fe^{3+}-EDTA/H_2O_2$ system were investigated using the luminol-dependent chemiluminescence assay. The order of ROS scavenging activities were ethylacetate fraction ($OSC_{50},\;4.58\;{\mu}g/mL$) < deglycosylated flavonoid aglycone fraction ($2.20\;{\mu}g/mL$) < 50 % ethanol extract ($1.09\;{\mu}g/mL$). 50 % Ethanol extract showed the most prominent scavenging activity. The protective effects of extract/fractions of Aspalathus linearis on the rose-bengal sensitized photohemolysis of human erythrocytes were investigated. The Aspalathus linearis extracts suppressed photohemolysis in a concentration dependent manner, particularly 50 % ethanol extract exhibited the most prominent celluar protective effect (${\tau}_{50}$, 272.00 min at $50\;{\mu}g/mL$). Aglycone fractions obtained from the deglycosylation reaction of ethylacetate fraction among the Aspalathus linearis extracts, showed 3 bands in TLC and 3 peaks in HPLC experiments (360 nm). Three components were identified as luteolin (composition ratio, 18.24 %), quercetin (58.79), and kaempferol (22.97). TLC chromatogram of ethylacetate fraction of Aspalathus linearis extract revealed 7 bands and HPLC chromatogram showed 9 peaks, which were identified as isoorientin (composition ratio, 14.71 %), orientin (28.84 %), vitexin (5.63 %), rutin and isovitexin (12.73 %), hyperoside (9.24 %), isoquercitrin (5.40 %), luteolin (1.48 %), quercetin (17.61 %) and kaempferol (4.59 %) in the order of elution time. The inhibitory effect of aglycone fraction on elastase ($IC_{50},\;9.08\;{\mu}g/mL$) was very high. These results indicate that extract/fractions of Aspalathus linearis can function as antioxidants in biological systems, particularly skin exposed to UV radiation by scavenging $^1O_2$ and other ROS, and protect cellular membranes against ROS. And component analysis of Aspalathus linearis extract and inhibitory activity on elastase of the aglycone fraction could be applicable to new functional cosmetics for smoothing wrinkles.

• Economic and Environmental Geology
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• v.50 no.4
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• pp.287-302
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• 2017

The carbonate rocks of the Daegi Formation are composed of the limestone at the upper and lower zones, and the dolomite at the middle zone, in which the upper zone has higher CaO content than others. The colors of carbonate rock in the Daegi Formation can be divided into five types; white, light brown, light gray, gray, and dark gray. The white to light gray colored rocks correspond to the high purity limestone with 53.15 ~ 55.64 wt. % CaO, and the light brown colored rocks contain 20.71 ~ 21.67 wt. % MgO. The bleaching of carbonate rocks are not related to CaO composition of the rocks, as light gray rocks tend to be higher in CaO content than those of the white rocks at the lower zone. The pelitic components are also occasionally increased in white limestone than light grey one. $Al_2O_3$ is one of the most difficult content to remove during hydrothermal processes, so the interpretation that the limestone is purified together with hydrothemral bleaching, has little merit. The wide range (over 16 ‰) of ${\delta}^{18}O_{SMOW}$, smaller variation (within 2 ‰) of ${\delta}^{13}C_{PDB}$ are apparent in both the upper and lower zones, which indicate the Daegi Formation had been affected overall by hydrothermal fluids. The K-Ar isotopic age of hydrothermal alteration in the GMI limestone mine is $85.1{\pm}1.7Ma$. Gradual change from grey through light grey to white limestone is accompaned by lower oxygen stable isotope values, which is major evidence that the hydrothermal effect is the main process of the bleaching. Although the Daegi Formation has suffered from hydrothermal activity and increase in whiteness, there is no clear evidence demonstrating the relationship between bleaching and high purity of limestone. The purification of limestone has nothing to do with the hydrothermal activity in this area. Instead, it should be considered that the change of sedimentary environment related to see-level fluctuation which can prevent deposition of pelitic components especially $Al_2O_3$ contrbuted to the formation of the high purity limestone in the upper zone of the Daegi Formation. Considering the evidences such as increase in CaO content of limestone by depth, gradual change from calcite to dolomite at the lower zones, and occurring the high purity limestone at the upper zone, the interpretation of sequence stratigraphic aspect to the formation of the high purity Daegi limestone appears to be more suitable than that of hydrothermal alteration origin.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.39-53
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• 2013

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on user's reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.35 no.2
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• pp.159-169
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• 2009

In this study, the antibacterial activity, antioxidative effects, inhibitory effects on tyrosinase, inhibitory effects on elastase, and components of Quercus acutissima Carruth leaf extracts were investigated. MIC values of ethyl acetate fraction from Q. acutissima Carruth leaf on P. acnes, S. aureus, P. ovale, and E. coli were 0.13 %, 0.25 %, 0.13 % and 0.25 %, respectively. The results showed that the antibacterial activity of the ethyl acetate fraction was the highest in the S. aureus, P. acnes, and P. ovale. The free radical (1,1-diphenyl-2-picrylhydrazyl, DPPH) scavenging activity ($FSC_{50}$) of extract/fractions of Q. acutissima Carruth. leaf was in the order: 50 % ethanol extract (12.13 ${\mu}g/mL$) < ethyl acetate fraction (7.07 ${\mu}g/mL$) < deglycosylated flavonoid aglycone fraction (6.20 ${\mu}g/mL$). Reactive oxygen species (ROS) scavenging activities ($OSC_{50}$) of some Q. acutissima Carruth leaf extracts on ROS generated in $Fe^{3+}-EDTA/H_2O_2$ system were investigated using the luminol-dependent chemiluminescence assay. The order of ROS scavenging activity was 50 % ethanol extract ($OSC_{50}$, 1.81 ${\mu}g/mL$) < ethyl acetate fraction (1.70 ${\mu}g/mL$) < deglycosylated flavonoid aglycone fraction (0.70 ${\mu}g/mL$). Deglycosylated flavonoid aglycone fraction showed the most prominent scavenging activity. The protective effects of extract/fractions of Q. acutissima Carruth leaf on the rose-bengal sensitized photohemolysis of human erythrocytes were investigated. The Q. acutissima Carruth leaf extracts suppressed photohemolysis in a concentration dependent manner, particularly deglycosylated flavonoid aglycone fraction exhibited the most prominent celluar protective effect (${\tau}50$, 220.00 min at 25 ${\mu}g/mL$). Aglycone fractions obtained from the deglycosylation reaction of ethyl acetate fraction among the Q. acutissima Carruth leaf extracts, showed 3 bands (QA 1, QA2 and QA3) on TLC. TLC chromatogram of ethyl acetate fraction of Q. Carruth. leaf extract revealed 4 bands (QA 1 ${\sim}$ QA 4), Among them, kaempferol (QA 1), quercetin (QA 2), and gallic acid (QA 3) were identified. The inhibitory effect ($IC_{50}$) of aglycone fraction on tyrosinase was 65.7 ${\mu}g/mL$. The inhibitory effect ($IC_{50}$) of aglycone fraction on elastase was 24.50 ${\mu}g/mL$. These results indicate that extract/fractions of Q. acutissima Carruth. can functionized as antioxidants in biological systems, particularly skin exposed to UV radiation by scavenging $^1O_2$ and other ROS, and protect cellular membranes against ROS. Extract/fractions of Q. acutissima Corruth can be applicable to new functional cosmetics for antioxidant, antiaging, antibacterial activity.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.19-43
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• 2014

The Pareto principle, also known as the 80-20 rule, states that roughly 80% of the effects come from 20% of the causes for many events including natural phenomena. It has been recognized as a golden rule in business with a wide application of such discovery like 20 percent of customers resulting in 80 percent of total sales. On the other hand, the Long Tail theory, pointing out that "the trivial many" produces more value than "the vital few," has gained popularity in recent times with a tremendous reduction of distribution and inventory costs through the development of ICT(Information and Communication Technology). This study started with a view to illuminating how these two primary business paradigms-Pareto principle and Long Tail theory-relates to the success of virtual knowledge collaboration. The importance of virtual knowledge collaboration is soaring in this era of globalization and virtualization transcending geographical and temporal constraints. Many previous studies on knowledge sharing have focused on the factors to affect knowledge sharing, seeking to boost individual knowledge sharing and resolve the social dilemma caused from the fact that rational individuals are likely to rather consume than contribute knowledge. Knowledge collaboration can be defined as the creation of knowledge by not only sharing knowledge, but also by transforming and integrating such knowledge. In this perspective of knowledge collaboration, the relative distribution of knowledge sharing among participants can count as much as the absolute amounts of individual knowledge sharing. In particular, whether the more contribution of the upper 20 percent of participants in knowledge sharing will enhance the efficiency of overall knowledge collaboration is an issue of interest. This study deals with the effect of this sort of knowledge sharing distribution on the efficiency of knowledge collaboration and is extended to reflect the work characteristics. All analyses were conducted based on actual data instead of self-reported questionnaire surveys. More specifically, we analyzed the collaborative behaviors of editors of 2,978 English Wikipedia featured articles, which are the best quality grade of articles in English Wikipedia. We adopted Pareto ratio, the ratio of the number of knowledge contribution of the upper 20 percent of participants to the total number of knowledge contribution made by the total participants of an article group, to examine the effect of Pareto principle. In addition, Gini coefficient, which represents the inequality of income among a group of people, was applied to reveal the effect of inequality of knowledge contribution. Hypotheses were set up based on the assumption that the higher ratio of knowledge contribution by more highly motivated participants will lead to the higher collaboration efficiency, but if the ratio gets too high, the collaboration efficiency will be exacerbated because overall informational diversity is threatened and knowledge contribution of less motivated participants is intimidated. Cox regression models were formulated for each of the focal variables-Pareto ratio and Gini coefficient-with seven control variables such as the number of editors involved in an article, the average time length between successive edits of an article, the number of sections a featured article has, etc. The dependent variable of the Cox models is the time spent from article initiation to promotion to the featured article level, indicating the efficiency of knowledge collaboration. To examine whether the effects of the focal variables vary depending on the characteristics of a group task, we classified 2,978 featured articles into two categories: Academic and Non-academic. Academic articles refer to at least one paper published at an SCI, SSCI, A&HCI, or SCIE journal. We assumed that academic articles are more complex, entail more information processing and problem solving, and thus require more skill variety and expertise. The analysis results indicate the followings; First, Pareto ratio and inequality of knowledge sharing relates in a curvilinear fashion to the collaboration efficiency in an online community, promoting it to an optimal point and undermining it thereafter. Second, the curvilinear effect of Pareto ratio and inequality of knowledge sharing on the collaboration efficiency is more sensitive with a more academic task in an online community.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of the Korean Society of Groundwater Environment
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• v.7 no.3
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• pp.103-115
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• 2000

The formation of brown-colored precipitates is one of the serious problems frequently encountered in the development and supply of groundwater in Korea, because by it the water exceeds the drinking water standard in terms of color. taste. turbidity and dissolved iron concentration and of often results in scaling problem within the water supplying system. In groundwaters from the Pajoo area, brown precipitates are typically formed in a few hours after pumping-out. In this paper we examine the process of the brown precipitates' formation using the equilibrium thermodynamic and kinetic approaches, in order to understand the origin and geochemical pathway of the generation of turbidity in groundwater. The results of this study are used to suggest not only the proper pumping technique to minimize the formation of precipitates but also the optimal design of water treatment methods to improve the water quality. The bed-rock groundwater in the Pajoo area belongs to the Ca-$HCO_3$type that was evolved through water/rock (gneiss) interaction. Based on SEM-EDS and XRD analyses, the precipitates are identified as an amorphous, Fe-bearing oxides or hydroxides. By the use of multi-step filtration with pore sizes of 6, 4, 1, 0.45 and 0.2 $\mu\textrm{m}$, the precipitates mostly fall in the colloidal size (1 to 0.45 $\mu\textrm{m}$) but are concentrated (about 81%) in the range of 1 to 6 $\mu\textrm{m}$in teams of mass (weight) distribution. Large amounts of dissolved iron were possibly originated from dissolution of clinochlore in cataclasite which contains high amounts of Fe (up to 3 wt.%). The calculation of saturation index (using a computer code PHREEQC), as well as the examination of pH-Eh stability relations, also indicate that the final precipitates are Fe-oxy-hydroxide that is formed by the change of water chemistry (mainly, oxidation) due to the exposure to oxygen during the pumping-out of Fe(II)-bearing, reduced groundwater. After pumping-out, the groundwater shows the progressive decreases of pH, DO and alkalinity with elapsed time. However, turbidity increases and then decreases with time. The decrease of dissolved Fe concentration as a function of elapsed time after pumping-out is expressed as a regression equation Fe(II)=10.l exp(-0.0009t). The oxidation reaction due to the influx of free oxygen during the pumping and storage of groundwater results in the formation of brown precipitates, which is dependent on time, $Po_2$and pH. In order to obtain drinkable water quality, therefore, the precipitates should be removed by filtering after the stepwise storage and aeration in tanks with sufficient volume for sufficient time. Particle size distribution data also suggest that step-wise filtration would be cost-effective. To minimize the scaling within wells, the continued (if possible) pumping within the optimum pumping rate is recommended because this technique will be most effective for minimizing the mixing between deep Fe(II)-rich water and shallow $O_2$-rich water. The simultaneous pumping of shallow $O_2$-rich water in different wells is also recommended.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.34 no.3
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• pp.233-244
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• 2008

In this study, the antioxidative effects, inhibitory effects on elastase and tyrosinase, and component analysis of Psidium guajava leaf extracts were investigated. The free radical (1,1-diphenyl-2-picrylhydrazyl, DPPH) scavenging activities $(FSC_{50})$ of extract/fractions of Psidium guajava leaf were in the order: 50% ethanol extract $(7.05{\mu}g/mL)$ < ethyl acetate fraction $(3.36{\mu}g/mL)$ < deglycosylated flavonoid aglycone fraction $(3.24{\mu}g/mL)$. Reactive oxygen species (ROS) scavenging activities $(OSC_{50})$ of some Psidium guajava leaf extracts on ROS generated in $Fe^{3+}-EDTA/H_2O_2$ system were investigated using the luminol-dependent chemiluminescence assay. The order of ROS scavenging activities were 50% ethanol extract $(OSC_{50},\;2.17{\mu}g/mL)$ < ethyl acetate fraction $(0.64{\mu}g/mL)$ < deglycosylated flavonoid aglycone fraction $(3.39{\mu}g/mL)$. Aglycone fraction showed the most prominent ROS scavenging activity. The protective effects of extract/fractions of Psidium guajava leaf on the rose-bengal sensitized photohemolysis of human erythrocytes were investigated. The Psidium guajava leaf extracts suppressed photohemolysis in a concentration dependent manner $(1{\sim}10{\mu}g/mL)$, particularly deglycosylated flavonoid aglycone fraction exhibited the most prominent celluar protective effect ${\tau}_{50}\;107.5min\;at\;1{\mu}g/mL)$. Aglycone fraction obtained from the deglycosylation reaction of ethyl acetate fraction among the Psidium guajava leaf extracts, showed 1 band in TLC and 1 peak in HPLC experiments (360 nm). One component was identified as quercetin. TLC chromatogram of ethyl acetate fraction of Psidium guajava leaf extract revealed 5 bands and HPLC chromatogram showed 5 peaks, which were identified as quercetin 3-O-gentobioside (10.32%) , quercetin 3-O-${\beta}$-D-glucoside (isoquercitin, 13.30%), quercetin 3-O-${\beta}$-D-galactoside (hyperin, 11.34%), quercetin 3-O-${\alpha}$-L-arabinoside (guajavarin, 19.70%), quercetin 3-O-${\beta}$-L-rhamnoside (quercitrin, 45.33%) in the order of elution time. The inhibitory effect of Psidium guajava leaf extracts on tyrosinase were investigated to assess their whitening efficacy. Finally, their anti-elastase activities were measured to predict the anti-wrinkle efficacy in the human skin. Inhibitory effects $(IC_{50})$ on tyrosinase of some Psidium guajava leaf extracts was 50% ethanol extract $(149.67{\mu}g/mL)$ < ethylacetate fraction $(30.67{\mu}g/mL)$ < deglycosylated aglycone fraction $(17.10{\mu}g/mL)$. Inhibitory effects $(IC_{50})$ on elastase of some Psidium guajava leaf extracts was 50% ethanol extract $(6.60{\mu}g/mL)$ < deglycosylated aglycone fraction $(5.66{\mu}g/mL)$ < ethylacetate fraction $(3.44{\mu}g/mL)$. These results indicate that extract/fractions of Psidium guajava leaf can function as antioxidants in bioloigcal systems, particularly skin exposed to UV radiation by scavenging $^1O_2$ and other ROS, and protect cellular membranes against ROS. And component analysis of Psidium guajava leaf extract and inhibitory activity on elastase of the aglycone fraction could be applicable to new functional cosmetics for smoothing wrinkles.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.