• Korean Journal of Plant Resources
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• v.32 no.5
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• pp.423-432
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• 2019

In order to select potential plant resources as functional materials and natural antioxidants, we evaluated antioxidant activity and serotonin derivatives of safflower germplasm collected from five countries. N-(p-Coumaroyl) serotonin (CS) and N-feruloylserotonin (FS) were analyzed by using Ultra Performance Liquid Chromatography (UPLC). Total polyphenol content (TPC) was determined by Folin-Ciocalteu method and antioxidant activities were estimated by 2,2-diphenyl-1-picryl-hydrazil (DPPH), 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) diammonium salt (ABTS), Ferric reducing antioxidant power (FRAP) and Reducing power (RP) assays. The TPC showed a range of 28.25 to $90.53{\mu}g$ gallic acid equivalent (GAE)/mg dried extract (DE). DPPH, ABTS, FRAP and RP assay were in the range of 18.76 to 93.98, 48.91 to 163.73, 3.80 to 132.29 and 26.32 to $80.08{\mu}g$ ascorbic acid equivalent (ASCE)/mg DE, respectively. Among the five countries, safflower seed collected from Iran had the highest levels of serotonin derivatives and antioxidant activities than other countries (p<0.05). CS showed high correlation with TPC, ABTS and DPPH (r=0.673,0.727,0.820), and FS showed high correlation with DPPH (r=0.740). Accessions IT321214 and IT321215 could be useful for development of new functional materials and could be used as a source of valuable natural antioxidant materials.

• Journal of Sericultural and Entomological Science
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• v.47 no.2
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• pp.68-77
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• 2005

A characteristic of subtropical zones region MK-T2 compares with an gaeryangppong, and the 9-10 schedule the times when a leaf blooms to are fast, and ratio that a branch edge by the colds becomes lean showed 5.7%, and a growth of the new branch which went out delivers 67.2 cm, mulberry loaves of the new branch which went out, and 18.6, a form of a leaf is the 1.10 that length of a leaf grew more a bit than width of a leaf up. Thickness of a leaf is $228.2{\mu}m$, and an area is more similar than gaeryangppong as $225.6cm^2$. in plant taxonomy, the hair whom the style exists short with 0.7 mm, and go to the pistil head inside so as to be rare is distributed, and belong to Dolichostylae Pubescentes. The new branch cutting which executed without remedy processes was independent of a thickness of a case branch, and the form and 100% root was said, and an gaeryangppong compared with the fact that 10% root went out of 15 mm ideal, and was excellent very, and looked, a root went out a root the soil and water, all showed a characteristic to go out at central of a branch bases at 45% ratio. Length was 24.6 mm, and were water rate 78.8%, and mulberry of MK-T2 was carrying together sweetness and acidity to pH 4.7 while, besides, arrival was 19.21 Brix%. A larva period and pupa ratio, cocoon thickness ratio are almost similar to gaeryangppong, or weight of one cocoon, cocoon thickness, 20,002 cocoon quantity shows some results to drop, and be soft of a leaf, and feed value certifications are comparatively top-ranking. As a result of having analyzed amino acid of the 3rd day of 5th silkworm larva which bred to MK-T2, a collation absorbing an gaeryangppong went, and looked, but compared with a collation in case of tests to eat MK-T2, and looked, and the lie collations were not detected a difference at Leu, but MK-T2 tests were detected mutual almost similar amino acid creation. medical efficacy of the 3rd day of 5th silkworm larva ethanol extract which bred to MK-T2 and black results, histologic a case did not appear at HE dyeing about the kidney organization which extracted form the rats which ate a silkworm ethanol extract and dyeing all chemical organization immunity, and one step protein revelation became lower with almost unidentified levels.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.57-71
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• 2013

Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.38 no.3
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• pp.225-236
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• 2012

In this study, the cellular protective effects on HaCaT cells and human erythrocytes and antioxidative effects of P. tricuspidata stem extracts were investigated. The ethyl acetate ($50{\mu}g/mL$) and aglycone fraction ($25{\mu}g/mL$) of P. tricuspidata stem extracts doesn't show any characteristics of cytotoxicity. When HaCaT cells were treated with 10 mM $H_2O_2$ and $30{\mu}M$ rose bengal, the ethyl acetate ($6.25{\sim}50{\mu}g/mL$) and aglycone ($6.25{\sim}25{\mu}g/mL$) fraction protected the cells against the oxidative damage in a concentration dependent manner. The P. tricuspidata stem extracts showed more prominent cellular protective effect than (+)-${\alpha}$-tocopherol, known as lipid antioxidant at $10{\mu}g/mL$. The ethylacetate fraction of P. tricuspidata stem extracts ($18.5{\mu}g/mL$) showed more free radical (1,1-diphenyl-2-picrylhydrazyl, DPPH) scavenging activity ($FSC5_{50}$). Reactive oxygen species (ROS) scavenging activity ($OSC_{50}$) of P. tricuspidata stem extracts on ROS generated in $Fe^{3+}$-EDTA/$H_2O_2$ system was investigated using the luminol-dependent chemiluminescence assay. The ethyl acetate ($1.72{\mu}g/mL$) and the aglycone fraction ($1.53{\mu}g/mL$) showed similar ROS scavenging activity of L-ascorbic acid ($1.50{\mu}g/mL$). These results indicate that extract/fractions of P. tricuspidata stem extracts can function as natural cytoprotective agents and antioxidants in biological systems, particularly skin exposed to UV radiation by protecting cellular membrane against ROS.

• Journal of the Korean Society of Food Science and Nutrition
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• v.33 no.3
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• pp.480-486
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• 2004

Despite many therapeutic advances in the understanding of the processes in carcinogenesis, overall mortality statistics are unlikely to change until there is reorientation of the concepts for the use of natural products as new anticarcinogenic agents. In this study, we investigated the anticarcinogenic activity, antioxidant and DPPH scavenging activity of Sargassum fulvellum (SF). SF was extracted with methanol, which was further fractionated into five different types: hexane (SFMH), ethylether (SFMEE), ethyl acetate (SFMEA), butanol (SFMB) and aqueous (SFMA) partition layers. We determined the cytotoxic effect of these layers on human cancer cells by MTT assay. Among various partition layers of SF, at starting concentration of 100 $\mu\textrm{g}$/mL, SFMEE showed very high cytotoxicity which were 92, 90 and 84% and kept high throughout 5 concentration levels sparsed by 100 $\mu\textrm{g}$/mL against all three human cancer cell lines: HepG2, HT-29 and HeLa. SFMEA showed a low cytotoxicity at the beginning concentration level, but as the concentration became denser, growth inhibition effect of cancer cell lines started to increase and at 500 $\mu\textrm{g}$/mL, it hit the highest, which were 91, 96 and 98% against the same three cell lines as above. We observed QR induced effect in all fraction layers of SF. SFMEE showed similar tendensy of QR induced effect as did against cytotoxicity. The QR induced effect of SFMEE on HepG2 cells at 25 $\mu\textrm{g}$/mL concentration indicated 3 times higher than the control value of 1.0 and SFMH tended to be concentration-dependent on HepG2 cells. At 100 $\mu\textrm{g}$/mL, the QR induced effects resulted a ratio, which was 2.5 times higher than the control value. In search for antioxidation effects of SF extract and partition layer, the reducing activity on the 1, 1-diphenyl-2-picryl hydrazyl (DPPH) radical scavenging potential was sequentially screened. The SFM has similar antioxidant activity as to BHT and vitamin C groups.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.5
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• pp.680-689
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• 2016

The present study evaluated the validation method for isoflavone contents of fermented soybean extracts by bioconversion as well as their antioxidant activities. Our results show that the total isoflavone contents of non-fermented and fermented soybean extract ranged between 119.8 to $637.7{\mu}g/g$ and between 567.3 to $2,074.6{\mu}g/g$, respectively. Moreover, fermented soybean extracts had higher contents of isoflavone aglycones, including daidzein, glycitein, and genistein than non-fermented soybean extracts as well as lower contents of isoflavone glucosides such as daidzin, glycitin, and genistin. FRAP and ORAC values ranged between 0.15 to 0.22 and between 195.24 to $753.79{\mu}M$ Trolox equivalents/g in non-fermented and fermented soybean extracts, respectively. These results indicate that fermented soybean extracts had higher total isoflavone contents and antioxidant activities than non-fermented soybean extracts. Bioconversion process in this study may have the potential to produce isoflavone-enriched natural antioxidant agents with high added value from soybean matrices.

• Journal of the Korean Society of Food Science and Nutrition
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• v.38 no.7
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• pp.839-845
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• 2009

Colorectal cancer is the third most common malignant neoplasm in the world. Much attention has been focused on reducing colon cancer risk through medical properties of natural compound that could act as anticarcinogens. In this study, we evaluated the antioxidant and antigenotoxic effects of Styela plicata (S. plicata) from in vitro experiments. S. plicata extracts showed antioxidant activity measured by TRAP assay and antigenotoxic effect in $200{\mu}M$ $H_2O_2$ induced DNA damage in human leukocytes. Especially, freeze-dried S. plicata extracted with methanol showed the highest level of TRAP (0.225 mM) and inhibition of DNA damage (66.8%). Additionally we observed the effect of S. plicata on the formation of aberrant crypt foci (ACF) induced by dimethylhydrazine (DMH) and DMH induced DNA damage (by comet assay) in male SD rats. The animals were divided into three groups and fed high-fat and low fiber diet (100 g lard+20 g cellulose/kg diet) without (normal control and DMH control) or with a 3% (w/w) of lyophilized S. plicata powder (DMH+S. plicata). One week after beginning the diets, rats were treated with DMH (30 mg/kg, s.c.) for 6 weeks except for normal control group, which was treated saline instead; dietary treatments were continued for the entire experiment. Nine weeks after DMH injection, administration of S. plicata resulted in reduction of ACF numbers, to 82.7% of the carcinogen control value ($7.67{\pm}2.04$ vs. $1.33{\pm}0.53$: p<0.01). S. plicata supplementation induced antigenotoxic effect on DMH-induced DNA damage in the blood cell (% tail intensity: $6.79{\pm}0.26$ vs. $6.13{\pm}0.22$). These data indicate that S. plicata extract has antigenotoxic and anticarcinogenic effects from in vitro experiments and S. plicata exerts a protective effect on the process of colon carcinogenesis, possibly by suppressing the DMH-induced DNA damage in blood cell and the development of preneoplastic lesions in colon.