• Archives of Pharmacal Research
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• v.27 no.1
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• pp.44-47
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• 2004

Loranthus tanakae Fr. et Sav. (Loranthaceae) is a species of mistletoe, a semiparasitic plant growing on the branches of Quercus and Betula species as host trees. In our ongoing search for bioactive compounds from endemic species in Korea, we have investigated to isolate the chemical constituents responsible for the antitumor effect of the MeOH extract of L. tanakae. The ethyl acetate soluble part of the MeOH extract demonstrated a marginal inhibition on the proliferation of the tumor cell lines such as A549 (non small cell lung), SK-OV-3 (ovary), SK-MEL-2 (melanoma), XF498 (central nerve system), and HCT-15 (colon) in vitro. Thus, the activity-guided isolation procedure upon the ethyl acetate soluble part of the extract has been carried out and finally four flavonoid rhamnopyranosides (1-4) were isolated as active principle. The structures of 1-4 were elucidated by the physicochemical and spectral data as rhamnetin 3-O-$\alpha$-L-rhamnoside (1), quercetin 3-O-$\alpha$-L-rhamnoside (2), rhamnocitrin 3-O-$\alpha$rhamnoside (3), and kaempferol 3-O-$\alpha$-L-rhamnoside (4).

• Journal of Animal Science and Technology
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• v.63 no.3
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• pp.603-613
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• 2021

This research improved the growth potential of Bifidobacterium animalis subsp lactis strain JNU306, a commercial medium that is appropriate for large-scale production, in yeast extract, soy peptone, glucose, L-cysteine, and ferrous sulfate. Response surface methodology (RSM) was used to optimize the components of this medium, using a central composite design and subsequent analyses. A second-order polynomial regression model, which was fitted to the data at first, significantly lacked fitness. Thus, through further analyses, the model with linear and quadratic terms plus two-way, three-way, and four-way interactions was selected as the final model. Through this model, the optimized medium composition was found as 2.8791% yeast extract, 2.8030% peptone soy, 0.6196% glucose, 0.2823% L-cysteine, and 0.0055% ferrous sulfate, w/v. This optimized medium ensured that the maximum biomass was no lower than the biomass from the commonly used blood-liver (BL) medium. The application of RSM improved the biomass production of this strain in a more cost-effective way by creating an optimum medium. This result shows that B. animalis subsp lactis JNU306 may be used as a commercial starter culture in manufacturing probiotics, including dairy products.

• International Journal of Industrial Entomology and Biomaterials
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• v.42 no.2
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• pp.25-32
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• 2021

Mulberry fruit (Morus alba L.) contains phytochemicals, including 1-deoxynojirimycin, quercetin-glucoside, kaempferol-glucoside, and anthocyanins, which have antioxidant effects. In this study, mulberry fruit extract was prepared at various temperatures (25-100℃) and water/ethanol solvent concentrations (0%-100% ethanol). Fourier-transform infrared spectroscopy (FT-IR) and circular dichroism (CD) data indicated that the content of bioactive compounds such as polyphenols and flavonoids was lower in 100% ethanolic extracts than in 30%-50% ethanolic extracts. Radical scavenging activity determined using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-di-3-ethylbenzthiazoline sulfonic acid (ABTS) assays was highly correlated with polyphenol and flavonoid content. In conclusion, 30%-50% ethanolic extracts contained the highest contents of bioactive compounds and exhibited high levels of radical scavenging activity. These findings may inform the use of mulberry fruit extract as a functional food.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.1
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• pp.132-146
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• 2022

To explore the potential use of Ecklonia cava as a natural dye for textiles, silk fabric was dyed with Ecklonia cava extracted by hot water under varied conditions, including temperature, duration, dye concentration, mordanting, and pH adjustment. The fabric was also evaluated for fastness, sun-protective property, and deodorization. Ecklonia cava extract was estimated by FT-IR to have polyphenol as a main functional colorant in plants, while the existence of phlorotannins through the UV-spectrum method was also confirmed. The fabric was optimized for maximum dye uptake at a temperature of 80℃ for a duration of 50 minutes along all dye concentrations. The dyed fabric showed a hue of Yellow Red under all dyeing conditions while additional color tones, such as grayish and dark, were generated through mordanting and pH adjustment. Both good sun-protective properties and a positive deodorization rate were also recorded, with more than 20% (owf) Ecklonia cava extract on silk fabric. These results imply that Ecklonia cava has great potential to be used as an eco-friendly natural dye and in fashion goods with skin-health functions made of silk.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3730-3744
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• 2020

This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.

• Journal of Information Technology Applications and Management
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• v.15 no.2
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• pp.1-14
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• 2008

The distinguished prosperity of information technologies from great progress of e-business during the last decade has unavoidably made software development for active data mining to discovery hidden predictive information regarding business trends and behavior from vary large databases. Therefore this paper develops an active mining object(ADMO) component, which provides real-time predictive information from web databases. The ADMO component is to extended ADO(ActiveX Data Object) component to active data mining component based on COM(Component Object Model) for application program interface(API). ADMO component development made use of window script component(WSC) based on XML(eXtensible Markup Language). For the purpose of investigating the application environments and the practical schemes of the ADMO component, experiments for diverse practical applications were performed in this paper. As a result, ADMO component confirmed that it could effectively extract the analytic information of classification and aggregation from vary large databases for Web services.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.283-285
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• 2005

To account for the importance of variable in multi-sensor data fusion, random forests are applied to supervised land-cover classification. The random forests approach is a non-parametric ensemble classifier based on CART-like trees. Its distinguished feature is that the importance of variable can be estimated by randomly permuting the variable of interest in all the out-of-bag samples for each classifier. Supervised classification with a multi-sensor remote sensing data set including optical and polarimetric SAR data was carried out to illustrate the applicability of random forests. From the experimental result, the random forests approach could extract important variables or bands for land-cover discrimination and showed good performance, as compared with other non-parametric data fusion algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5321-5334
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• 2019

Data mining technology is frequently used in identifying the intention of users over a variety of information contexts. Since relevant terms are mainly hidden in text data, it is necessary to extract them. Quantification is required in order to interpret user preference in association with other structured data. This paper proposes rating and comments mining to identify user priority and obtain improved ratings. Structured data (location and rating) and unstructured data (comments) are collected and priority is derived by analyzing statistics and employing TF-IDF. In addition, the improved ratings are generated by applying priority categories based on materialized ratings through Sentiment-Oriented Point-wise Mutual Information (SO-PMI)-based emotion analysis. In this paper, an experiment was carried out by collecting ratings and comments on "place" and by applying them. We confirmed that the proposed mining method is 1.2 times better than the conventional methods that do not reflect priorities and that the performance is improved to almost 2 times when the number to be predicted is small.

• Journal of Navigation and Port Research
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• v.27 no.6
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• pp.645-652
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• 2003

A virtual vessel traffic control system is introduced to contribute to prevent a marine accident such as collision and stranding from happening. Existing VTS has its limit. The virtual vessel traffic control system consists of both data acquisition by satellite remote sensing and a simulation of traffic environment stress based on the satellite data, remotely sensed data And it could be used to provide timely and detailed information about the marine safety, including the location, speed and direction of ships, and help us operate vessels safely and efficiently. If environmental stress values are simulated for the ship information derived from satellite data, proper actions can be taken to prevent accidents. Since optical sensor has a high spatial resolution, JERS satellite data are used to track ships and extract their information. We present an algorithm of automatic identification of ship size and velocity. It lastly is shown that based on ship information extracted from JERS data, a qualitative evaluation method of environmental stress is introduced.

• International Journal of Advanced Culture Technology
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• v.5 no.3
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• pp.40-45
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• 2017

This data mining technique was used to extract useful information from percutaneous coronary intervention data obtained from the US public data homepage. The experiment was performed by extracting data on the area, frequency of operation, and the number of deaths. It led us to finding of meaningful correlations, patterns, and trends using various algorithms, pattern techniques, and statistical techniques. In this paper, information is obtained through efficient decision tree and cluster analysis in predicting the incidence of percutaneous coronary intervention and mortality. In the cluster analysis, EM algorithm was used to evaluate the suitability of the algorithm for each situation based on performance tests and verification of results. In the cluster analysis, the experimental data were classified using the EM algorithm, and we evaluated which models are more effective in comparing functions. Using data mining technique, it was identified which areas had effective treatment techniques and which areas were vulnerable, and we can predict the frequency and mortality of percutaneous coronary intervention for heart disease.