• Journal of Ginseng Research
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• v.44 no.2
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• pp.282-290
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• 2020

Background: Ginseng is a commonly used herbal medicine in treating various medical conditions. Chronic gut inflammation is a recognized factor for the development of colorectal cancer (CRC). In this project, Asian ginseng berry polysaccharide preparations were used to assess their effects on CRC and related immune regulation mechanisms. Methods: Ginseng berry polysaccharide extract (GBPE) and purified ginseng berry polysaccharide portion (GBPP) were used to evaluate their activities on human HCT-116 and HT-29 CRC cell proliferation. Interleukin-8 secretion analysis was performed on HT-29 cells. Naive CD4 cell isolation and T-helper cell differentiation were performed and determined using flow cytometry for Th1 and Treg in addition to cell cycle and apoptotic investigation. Results: GBPE and GBPP significantly inhibited interleukin-8 secretion and cancer cell proliferation, inhibited CD4⁺IFN-γ⁺ cell (Th1) differentiation, and decreased CD4⁺FoxP3⁺ cell (Treg) differentiation. Compared to the GBPE, GBPP showed more potent antiinflammatory activities on the malignant cells. This is consistent with the observation that GBPP can also inhibit Th1-cell differentiation better, suggesting that it has an important role in antiinflammation, whereas Treg cells hinder the body's immune response against malignancies. Supported by cell cycle and apoptosis data, GBPE and GBPP, at various degrees, remarkably enhanced the anticancer activities of 5-fluorouracil. Conclusion: Data from this project suggested that Asian ginseng berry potentially has clinical utility in managing enteric inflammation and suppressing CRC through immunomodulation mechanisms.

• Journal of the Korea Society of Computer and Information
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• v.21 no.6
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• pp.9-19
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• 2016

In finance literature, stock liquidity showing how stocks can be cashed out in the market has received rich attentions from both academicians and practitioners. The reasons are plenty. First, it is known that stock liquidity affects significantly asset pricing. Second, macroeconomic announcements influence liquidity in the stock market. Therefore, stock liquidity itself affects investors' decision and managers' decision as well. Though there exist a great deal of literature about stock liquidity in finance literature, it is quite clear that there are no studies attempting to investigate the stock liquidity issue as one of decision making problems. In finance literature, most of stock liquidity studies had dealt with limited views such as how much it influences stock price, which variables are associated with describing the stock liquidity significantly, etc. However, this paper posits that stock liquidity issue may become a serious decision-making problem, and then be handled by using data mining techniques to estimate its future extent with statistical validity. In this sense, we collected financial data set from a number of manufacturing companies listed in KRX (Korea Exchange) during the period of 2010 to 2013. The reason why we selected dataset from 2010 was to avoid the after-shocks of financial crisis that occurred in 2008. We used Fn-GuidPro system to gather total 5,700 financial data set. Stock liquidity measure was computed by the procedures proposed by Amihud (2002) which is known to show best metrics for showing relationship with daily return. We applied five data mining techniques (or classifiers) such as Bayesian network, support vector machine (SVM), decision tree, neural network, and ensemble method. Bayesian networks include GBN (General Bayesian Network), NBN (Naive BN), TAN (Tree Augmented NBN). Decision tree uses CART and C4.5. Regression result was used as a benchmarking performance. Ensemble method uses two types-integration of two classifiers, and three classifiers. Ensemble method is based on voting for the sake of integrating classifiers. Among the single classifiers, CART showed best performance with 48.2%, compared with 37.18% by regression. Among the ensemble methods, the result from integrating TAN, CART, and SVM was best with 49.25%. Through the additional analysis in individual industries, those relatively stabilized industries like electronic appliances, wholesale & retailing, woods, leather-bags-shoes showed better performance over 50%.

• The Korean Journal of Financial Management
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• v.26 no.2
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• pp.155-180
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• 2009

This study is to estimate and compare hedging effectiveness in emerging currency and advanced currency futures markets. Emerging currency futures includes Korea won, Mexico peso, and Brazil real and advanced currency futures is Europe euro, British pound, and Japan yen. Hedging effectiveness is measured by comparing hedging performance of the naive hedge model, OLS model, error correction model and constant condintional correlation bivariate GARCH(1, 1) hedge model based on rolling windows. Analysis data is used daily spot and futures rates from January, 2, 2001 to March. 10, 2006. The empirical results are summarized as follows : First, irrespective of hedging period and model, hedging using Korea won/dollar futures reduces spot rate's volatility risk by 97%. Second, Korea won/dollar futures market produces the best hedging performance in emerging and advanced currency futures markets, i.e. Mexico peso, Brazil real, Europe euro, British pound, and Japan yen. Third, there are no difference of hedging effectiveness among hedging models.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.6
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• pp.484-498
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• 2004

In this paper. we propose a novel performance monitoring scheme for heterogeneous SOAP nodes. The scheme is basically based on two-level (kernel-level and user-level) packet filtering of TCP flows. By TCP flow, we mean a sequence of raw packet streams on a TCP transaction. In this scheme, we detect and extract SOAP operations embedded in SOAP messages from TCP flows. Therefore, it becomes possible to monitor heterogeneous SOAP nodes deployed on diverse SOAP-based middlewares such as .Net and Apache AXIS. We present two implementation mechanisms for the proposed scheme. The first mechanism tries to identify SOAP operations by analyzing all fragmented SOAP messages on TCP flows. However, a naive policy would incur untolerable overhead since it needs to copy all packets from kernel to user space. The second mechanism overcomes this problem by selectively copying packets from kernel to user space. For selective copying, we use a kernel-level packet filtering method that makes use of some representative TCP flags.(e.g. SIN, FIN and PSH). In this mechanism, we can detect SOAP operations only from the last fragment of SOAP messages in most cases. Finally, we implement a SOAP monitoring system using a component ca]led SOAP Sniffer that realizes our proposed scheme, and show experimental results. We strongly believe that our system will play a vital role as a tool for various services such as transaction monitoring and load balancing among heterogeneous SOAP nodes.

• Journal of Korea Water Resources Association
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• v.51 no.10
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• pp.839-852
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• 2018

The purpose of this study is to predict monthly agricultural reservoir storage by developing weather data-based Multiple Linear Regression Model (MLRM) with precipitation, maximum temperature, minimum temperature, average temperature, and average wind speed. Using Naïve-Bayes classification, total 1,559 nationwide reservoirs were classified into 30 clusters based on geomorphological specification (effective storage volume, irrigation area, watershed area, latitude, longitude and frequency of drought). For each cluster, the monthly MLRM was derived using 13 years (2002~2014) meteorological data by KMA (Korea Meteorological Administration) and reservoir storage rate data by KRC (Korea Rural Community). The MLRM for reservoir storage rate showed the determination coefficient ($R^2$) of 0.76, Nash-Sutcliffe efficiency (NSE) of 0.73, and root mean square error (RMSE) of 8.33% respectively. The MLRM was evaluated for 2 years (2015~2016) using 3 months weather forecast data of GloSea5 (GS5) by KMA. The Reservoir Drought Index (RDI) that was represented by present and normal year reservoir storage rate showed that the ROC (Receiver Operating Characteristics) average hit rate was 0.80 using observed data and 0.73 using GS5 data in the MLRM. Using the results of this study, future reservoir storage rates can be predicted and used as decision-making data on stable future agricultural water supply.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.10
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• pp.447-454
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• 2015

This paper proposes geographical name denoising by machine learning of event detection based on twitter. Recently, the increasing number of smart phone users are leading the growing user of SNS. Especially, the functions of short message (less than 140 words) and follow service make twitter has the power of conveying and diffusing the information more quickly. These characteristics and mobile optimised feature make twitter has fast information conveying speed, which can play a role of conveying disasters or events. Related research used the individuals of twitter user as the sensor of event detection to detect events that occur in reality. This research employed geographical name as the keyword by using the characteristic that an event occurs in a specific place. However, it ignored the denoising of relationship between geographical name and homograph, it became an important factor to lower the accuracy of event detection. In this paper, we used removing and forecasting, these two method to applied denoising technique. First after processing the filtering step by using noise related database building, we have determined the existence of geographical name by using the Naive Bayesian classification. Finally by using the experimental data, we earned the probability value of machine learning. On the basis of forecast technique which is proposed in this paper, the reliability of the need for denoising technique has turned out to be 89.6%.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.422-427
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• 2010

Korean Game industry, especially MMORPG(Massively Multiplayer Online Game) has been rapidly expanding in these days. But As game industry is growing, lots of online game security incidents have also been increasing and getting prevailing. One of the most critical security incidents is 'Game Bots', which are programs to play MMORPG instead of human players. If player let the game bots play for them, they can get a lot of benefic game elements (experience points, items, etc.) without any effort, and it is considered unfair to other players. Plenty of game companies try to prevent bots, but it does not work well. In this paper, we propose a behavior pattern model for detecting bots. We analyzed behaviors of human players as well as bots and identified six game features to build the model to differentiate game bots from human players. Based on these features, we made a Naive Bayesian classifier to reasoning the game bot or not. To evaluated our method, we used 10 game bot data and 6 human Player data. As a result, we classify Game bot and human player with 88% accuracy.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.433-440
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• 2019

Recently, machine learning and data mining have been used for many disease prediction and diagnosis. Chronic diseases account for about 80% of the total mortality rate and are increasing gradually. In previous studies, the predictive model for chronic diseases use data such as blood glucose, blood pressure, and insulin levels. In this paper, world's first research, verifies the relationship between dyslipidemia and facial characteristics, and develops the predictive model using machine learning based facial characteristics. Clinical data were obtained from 5390 adult Korean men, and using hypertriglyceridemia and facial characteristics data. Hypertriglyceridemia is a measure of dyslipidemia. The result of this study, find the facial characteristics that highly correlated with hypertriglyceridemia. FD_43_143_aD (p<0.0001, Area Under the receiver operating characteristics Curve(AUC)=0.652) is the best indicator of this study. FD_43_143_aD means distance between mandibular. The model based on this result obtained AUC value of 0.662. These results will provide a basis for predicting various diseases with only facial characteristics in the screening stage of disease epidemiology and public health in the future.

• Journal of Veterinary Science
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• v.22 no.2
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• pp.16.1-16.13
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• 2021

Background: Preconditioning with inflammatory stimuli is used to improve the secretion of anti-inflammatory agents in stem cells from variant species such as mouse, human, and dog. However, there are only few studies on feline stem cells. Objectives: This study aimed to evaluate the immune regulatory capacity of feline adipose tissue-derived (fAT) mesenchymal stem cells (MSCs) pretreated with interferon-gamma (IFN-γ). Methods: To assess the interaction of lymphocytes and macrophages with IFN-γ-pretreated fAT-MSCs, mouse splenocytes and RAW 264.7 cells were cultured with the conditioned media from IFN-γ-pretreated MSCs. Results: Pretreatment with IFN-γ increased the gene expression levels of cyclooxygenase-2, indoleamine 2,3-dioxygenase, hepatocyte growth factor, and transforming growth factor-beta 1 in the MSCs. The conditioned media from IFN-γ-pretreated MSCs increased the expression levels of M2 macrophage markers and regulatory T-cell markers compared to those in the conditioned media from naive MSCs. Further, prostaglandin E₂ (PGE₂) inhibitor NS-398 attenuated the immunoregulatory potential of MSCs, suggesting that the increased PGE₂ levels induced by IFN-γ stimulation is a crucial factor in the immune regulatory capacity of MSCs pretreated with IFN-γ. Conclusions: IFN-γ pretreatment improves the immune regulatory profile of fAT-MSCs mainly via the secretion of PGE₂, which induces macrophage polarization and increases regulatory T-cell numbers.

• Journal of Platform Technology
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• v.9 no.3
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• pp.52-62
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• 2021

Quality control is critical at manufacturing sites and is key to predicting the risk of quality defect before manufacturing. However, the reliability of manual quality control methods is affected by human and physical limitations because manufacturing processes vary across industries. These limitations become particularly obvious in domain areas with numerous manufacturing processes, such as the manufacture of major nuclear equipment. This study proposed a novel method for predicting the risk of quality defects by using natural language processing and machine learning. In this study, production data collected over 6 years at a factory that manufactures main equipment that is installed in nuclear power plants were used. In the preprocessing stage of text data, a mapping method was applied to the word dictionary so that domain knowledge could be appropriately reflected, and a hybrid algorithm, which combined n-gram, Term Frequency-Inverse Document Frequency, and Singular Value Decomposition, was constructed for sentence vectorization. Next, in the experiment to classify the risky processes resulting in poor quality, k-fold cross-validation was applied to categorize cases from Unigram to cumulative Trigram. Furthermore, for achieving objective experimental results, Naive Bayes and Support Vector Machine were used as classification algorithms and the maximum accuracy and F1-score of 0.7685 and 0.8641, respectively, were achieved. Thus, the proposed method is effective. The performance of the proposed method were compared and with votes of field engineers, and the results revealed that the proposed method outperformed field engineers. Thus, the method can be implemented for quality control at manufacturing sites.