• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.59-69
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• 2012

As exhibition industry, which is a part of 17 new growth engines of the government, is related to other industries such as tourism, transportation and financial industries. So it has a significant ripple effect on other industries. Exhibition is a knowledge-intensive, eco-friendly and high value-added Industry. Over 13,000 exhibitions are held every year around the world which contributes to getting foreign currency. Exhibition industry is closely related with culture and tourism and could be utilized as local and national development strategies and improve national brand image as well. Many countries try various efforts to invigorate exhibition industry by arranging related laws and support system. In Korea, more than 200 exhibitions are being held every year, but only 2~3 exhibitions are hosted with over 400 exhibitors and except these exhibitions most exhibitions have few foreign exhibitors. The main reason of weakness of domestic trade show is that there are no agencies managing exhibitionrelated statistics and there is no specific and reliable evaluation. This might cause impossibility of providing buyer or seller with reliable data, poor growth of exhibitions in terms of quality and thus service quality of trade shows cannot be improved. Hosting a lot of visitors (Public/Buyer/Exhibitor) is very crucial to the development of domestic exhibition industry. In order to attract many visitors, service quality of exhibition and visitor's satisfaction should be enhanced. For this purpose, a variety of real-time customized services through digital media and the services for creating new customers and retaining existing customers should be provided. In addition, by providing visitors with personalized information services they could manage their time and space efficiently avoiding the complexity of exhibition space. Exhibition industry can have competitiveness and industrial foundation through building up exhibition-related statistics, creating new information and enhancing research ability. Therefore, this paper deals with customized service with visitor's smart-phone at the exhibition space and designing mobile framework which enables exhibition devices to interact with other devices. Mobile server framework is composed of three different systems; multi-server interaction, server, client, display device. By making knowledge pool of exhibition environment, the accumulated data for each visitor can be provided as personalized service. In addition, based on the reaction of visitors each of all information is utilized as customized information and so the cyclic chain structure is designed. Multiple interaction server is designed to have functions of event handling, interaction process between exhibition device and visitor's smart-phone and data management. Client is an application processed by visitor's smart-phone and could be driven on a variety of platforms. Client functions as interface representing customized service for individual visitors and event input and output for simultaneous participation. Exhibition device consists of display system to show visitors contents and information, interaction input-output system to receive event from visitors and input toward action and finally the control system to connect above two systems. The proposed mobile framework in this paper provides individual visitors with customized and active services using their information profile and advanced Knowledge. In addition, user participation service is suggested as well by using interaction connection system between server, client, and exhibition devices. Suggested mobile framework is a technology which could be applied to culture industry such as performance, show and exhibition. Thus, this builds up the foundation to improve visitor's participation in exhibition and bring about development of exhibition industry by raising visitor's interest.

• Journal of Korean Home Economics Education Association
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• v.29 no.3
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• pp.141-160
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• 2017

At present, the whole world is characterized by openness, cooperation and integration as a society with information and the world, and it requires creative talents for this. The competitiveness of the nation depends on the cultivation of creative talents. It demands anthropological value by improving the competitiveness of the nation through cultivating creative talents to share and care, not to cultivate creative talents for personal desires and accomplishments. The purpose of this study is to develop a teaching - learning process to develop integrated creative talents by applying the characteristics of integrated education curriculum at the base of the 2015 revised curriculum. To do this, we first extracted the content elements of the Clothing Life area of the revised curriculum from the 1st to 2015 and the contents of the Clothing Life area of the technology and home Economics textbooks to which the 2009 revision curriculum was applied. Five topics were extracted according to content selection criteria And The Theme of 'The selection and management of clothing' was selected. Researcher developed a integrated thematic instruction Home Economics Education Clothing Life teaching - learning process (hereinafter referred to as "the process") focused on the theme of 'selection and management of clothing'. The Process was composed of 5 basic classes [basic inquiry - content 1 - content 2 - depth study - application]. The process considers integration within the curriculum and interdepartmental integration, and aims to improve the personality, creativity, and problem solving ability of consideration and sharing in the course of the integrated class. In detail, the curriculum goals of the home Economics Education curriculum and ITI goals are presented separately in the curriculum developed for each process. At the stage of the class, the element of creativity and the element of personal education were exposed and applied to the class. In Korea, new curriculum has been revised and announced from time to time, making it difficult for the teacher to carry out the class in accordance with the paradigm of change. Home curriculum is closely related to real life, so it is an appropriate subject for an integrated subject. It has the educational significance of the subject in that it allows students to integrate the creative and personal elements that are limited in other subjects. ITI process will be helpful to teachers who need to implement the 2015 revised curriculum in which the theme-centered integrated home Economics education teaching-learning process is newly introduced. It is necessary to follow - up research to verify the effectiveness of how to improve students' problem - solving ability and cultivate creativity.

• Journal of Korea Entertainment Industry Association
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• v.15 no.1
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• pp.131-142
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• 2021

Purpose: Clinical practicums via non-face-to-face methods were inevitable due to the COVID-19 pandemic. We developed an online program for emergency nursing care and identified the feasibility of the program and the learning achievements of students. Methods: This was a methodological study. The program was developed by three professors who taught theory and clinical practicum for adult nursing care and clinical experts. Students received four hours of video content and two task activities every week in four-week program. Real-time interactive video conferences were included. Qualitative and qualitative data were collected. Results: A total of 96 students participated in the program. The mean score for overall satisfaction with the online program was 4.72(±1.02) out of 6. Subjects that generally had high learning achievement scores were basic life support care, fall prevention, nursing documentation, infection control, and anaphylaxis care. As a result of a content analysis of 77 reflective logs on the advantages of this program, students reported that "experience in applying nursing process," "case-based learning and teaching method," and "No time and space constraints" were the program's best features. Conclusion: Collaboration between hospitals and universities for nursing is more important than ever to develop online content for effective clinical practicum.

• Korean Chemical Engineering Research
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• v.60 no.3
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• pp.334-346
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• 2022

There is a lack of knowledge and information on the understanding and application of the Process Safety Management (PSM) system, recognized as a major cause of industrial accidents in small-and medium-sized workplaces. Hence, it is necessary to prepare a protocol to secure the practical and continuous levels of implementation for PSM and eliminate human errors through tracking management. However, insufficient research has been conducted on this. Therefore, this study investigated and analyzed the various violations in the administrative measures, based on the regulations announced by the Ministry of Employment and Labor, in approximately 200 small-and medium-sized PSM workplaces with fewer than 300 employees across in korea. This study intended to contribute to the prevention of major industrial accidents by developing a facility maintenance web program that removed human errors in small-and medium-sized workplaces. The major results are summarized as follows. First, It accessed the web via a QR code on a smart device to check the equipment's specification search function, cause of failure, and photos for the convenience of accessing the program, which made it possible to make requests for the it inspection and maintenance in real time. Second, it linked the identification of the targets to be changed, risk assessment, worker training, and pre-operation inspection with the program, which allowed the administrator to track all the procedures from start to finish. Third, it made it possible to predict the life of the equipment and verify its reliability based on the data accumulated through the registration of the pictures for improvements, repairs, time required, cost, etc. after the work was completed. It is suggested that these research results will be helpful in the practical and systematic operation of small-and medium-sized PSM workplaces. In addition, it can be utilized in a useful manner for the development and dissemination of a facility maintenance web program when establishing future smart factories in small-and medium-sized PSM workplaces under the direction of the government.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• 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 Science of Art and Design
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• v.7
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• pp.64-104
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• 2005

Visual media transmits image and information reproduced in large quantities, such as a photography, film, television, video, advertisement, or computer image. Correspondence to the students' reception and recognition of culture in the future. arrangements for the field of studies of visual culture. 'Visual Culture' implies cultural phenomena of visual images via visual media, which includes not only the categories of traditional arts like a painting, sculpture, print, or design, but the performance arts including a fashion show or parade of carnival, and the mass and electronic media like a photography, film, television, video, advertisement, cartoon, animation, or computer image. In the world of visual media, Image' functions as an essential medium of communication. Therefore, people call the culture of today fra of Image Culture', which has been converted from an alphabet convergence era to an image convergence one. Image, via visual media, has become a dominant means for communication in large part of human life, so we can designate an Image' as a typical aspect of visual culture today. Image, as an essential medium of communication, plays an important role in contemporary society. The one way is the conversion of analogue image like an actual picture, photograph, or film into digital one through the digitalization of digital camera or scanner as 'an analogue/digital commutator'. The other is a way of process with a computer drawing, or modeling of objects. It is appropriate to the production of pictorial and surreal images. Digital images, produced by the other, can be divided into the form of Pixel' and form of Vector'. Vector is a line linking the point of departure to the point of end, which organizes informations. Computer stores each line's standard location and correlative locations to one another Digital image shows for more 'Perfectness' than any other visual media. Digital image has been evolving in the diverse aspects, such as a production of geometrical or organic image compositing, interactive art, multimedia art, or web art, which has been applied a computer as an extended trot of painting. Someone often interprets digitalized copy with endless reproduction of original even as an extension of a print. Visual af is no longer a simple activity of representation by a painter or sculptor, but now is intimately associated with a matter of application of media. There is some problem in images via visual media. First, the image via media doesn't reflect a reality as it is, but reflects an artificial manipulated world, that is, a virtual reality. Second, the introduction of digital effect and the development of image processing technology have enhanced a spectacle of destructive and violent scenes. Third, a child intends to recognize the interactive images of computer game and virtual reality as a reality, or truth. Education needs not only to point out an ill effect of mass media and prevent the younger generation from being damaged by it, but also to offer a knowledge and know-how to cope actively with social, cultural circumstances. Visual media education is one of these essential methods for the contemporary and future human being in the overflowing of image informations. The fosterage of 'Visual Literacy' can be considered as a very purpose of visual media education. This is a way to lead an individual to the discerning, active consumer and producer of visual media in life as far as possible. The elements of 'Visual Literacy' can be divided into a faculty of recognition related to the visual media, a faculty of critical reception, a faculty of appropriate application, a faculty of active work and a faculty of creative modeling, which are promoted at the same time by the education of 'visual literacy'. In conclusion, the education of 'Visual Literacy' guides students to comprehend and discriminate the visual image media carefully, or receive them critically, apply them properly, or produce them creatively and voluntarily. Moreover, it leads to an artistic activity by means of new media. This education can be approached and enhanced by the connection and integration with real life. Visual arts and education of them play an important role in the digital era depended on visual communications via image information. Visual me야a of day functions as an essential element both in daily life and in arts. Students can soundly understand visual phenomena of today by means of visual media, and apply it as an expression tool of life culture as well. A new recognition and valuation visual image and media education is required to cultivate the capability of active, upright dealing with the changes of history of civilization. 1) Visual media education helps to cultivate a sensibility for images, which reacts to and deals with the circumstances. 2) It helps students to comprehend the contemporary arts and culture via new media. 3) It supplies a chance of students' experiencing a visual modeling by means of new media. 4) There are educational opportunities of images with temporality and spaciality, and therefore a discerning person becomes to increase. 5) The modeling activity via new media leads students to be continuously interested in the school and production of plastic arts. 6) It raises the ability of visual communications dealing with image information society. 7) An education of digital image is significant in respect of cultivation of man of talent for the future society of image information as well. To correspond to the changing and developing social, cultural circumstances, and the form and recognition of students' reception of them, visual arts education must arrange the field of studying on a new visual culture. Besides, a program needs to be developed, which is in more systematic and active level in relation to visual media education. Educational contents should be extended to the media for visual images, that is, photography, film, television, video, computer graphic, animation, music video, computer game and multimedia. Every media must be separately approached, because they maintain the modes and peculiarities of their own according to the conveyance form of message. The concrete and systematic method of teaching and the quality of education must be researched and developed, centering around the development of a course of study. Teacher's foundational capability of teaching should be cultivated for the visual media education. In this case, it must be paid attention to the fact that a technological level of media is considered as a secondary. Because school education doesn't intend to train expert and skillful producers, but intends to lay stress on the essential aesthetic one with visual media under the social and cultural context, in respect of a consumer including a man of culture.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.83-105
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• 2016

Measuring an individual's subjective wellbeing in an accurate, unobtrusive, and cost-effective manner is a core success factor of the wellbeing support system, which is a type of medical IT service. However, measurements with a self-report questionnaire and wearable sensors are cost-intensive and obtrusive when the wellbeing support system should be running in real-time, despite being very accurate. Recently, reasoning the state of subjective wellbeing with conventional sentiment analysis and unstructured data has been proposed as an alternative to resolve the drawbacks of the self-report questionnaire and wearable sensors. However, this approach does not consider contextual polarity, which results in lower measurement accuracy. Moreover, there is no sentimental word net or ontology for the subjective wellbeing area. Hence, this paper proposes a method to extract keywords and their contextual polarity representing the subjective wellbeing state from the unstructured text in online websites in order to improve the reasoning accuracy of the sentiment analysis. The proposed method is as follows. First, a set of general sentimental words is proposed. SentiWordNet was adopted; this is the most widely used dictionary and contains about 100,000 words such as nouns, verbs, adjectives, and adverbs with polarities from -1.0 (extremely negative) to 1.0 (extremely positive). Second, corpora on subjective wellbeing (SWB corpora) were obtained by crawling online text. A survey was conducted to prepare a learning dataset that includes an individual's opinion and the level of self-report wellness, such as stress and depression. The participants were asked to respond with their feelings about online news on two topics. Next, three data sources were extracted from the SWB corpora: demographic information, psychographic information, and the structural characteristics of the text (e.g., the number of words used in the text, simple statistics on the special characters used). These were considered to adjust the level of a specific SWB. Finally, a set of reasoning rules was generated for each wellbeing factor to estimate the SWB of an individual based on the text written by the individual. The experimental results suggested that using contextual polarity for each SWB factor (e.g., stress, depression) significantly improved the estimation accuracy compared to conventional sentiment analysis methods incorporating SentiWordNet. Even though literature is available on Korean sentiment analysis, such studies only used only a limited set of sentimental words. Due to the small number of words, many sentences are overlooked and ignored when estimating the level of sentiment. However, the proposed method can identify multiple sentiment-neutral words as sentiment words in the context of a specific SWB factor. The results also suggest that a specific type of senti-word dictionary containing contextual polarity needs to be constructed along with a dictionary based on common sense such as SenticNet. These efforts will enrich and enlarge the application area of sentic computing. The study is helpful to practitioners and managers of wellness services in that a couple of characteristics of unstructured text have been identified for improving SWB. Consistent with the literature, the results showed that the gender and age affect the SWB state when the individual is exposed to an identical queue from the online text. In addition, the length of the textual response and usage pattern of special characters were found to indicate the individual's SWB. These imply that better SWB measurement should involve collecting the textual structure and the individual's demographic conditions. In the future, the proposed method should be improved by automated identification of the contextual polarity in order to enlarge the vocabulary in a cost-effective manner.

• Microbiology and Biotechnology Letters
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• v.44 no.2
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• pp.208-217
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• 2016

Pinus densiflora Sieb. et Zucc. (P. densiflora) contains several phenolic compounds that exhibit biological activities, such as antimicrobial, antioxidant, and antihypertensive effects. However, the anti-inflammatory effect of P. densiflora on skin has rarely been reported. Malassezia furfur (M. furfur) is a commensal microbe that induces skin inflammation and is associated with several chronic disorders, such as dandruff, seborrheic dermatitis, papillomatosis, and sepsis. The aim of our study was to identify the anti-inflammatory effects of P. densiflora needle extracts on skin health subjected to M. furfur-induced inflammation. The methanolic extract of the pine needles was partitioned into n-hexane, EtOAc, n-BuOH, and water layers. We measured the anti-inflammatory effects (in macrophages) as well as the antioxidant, antifungal, and tyrosinase inhibitory activity of each of these layers. The antioxidant activity of the individual layers was in the order EtOAc layer > n-BuOH layer > water layer. Only the n-BuOH, EtOAc, and n-hexane layers showed antifungal activity. Additionally, all the layers possessed tyrosinase inhibition activity similar to that of ascorbic acid, which is used as a commercial control. The EtOAc layer was not cytotoxic toward the RAW 264.7 cell line. Interleukin 1 beta and tumor necrosis factor (TNF)-α expression levels in M. furfur-stimulated RAW 264.7 cells treated with the EtOAc layer were decreased markedly compared to those in cells treated with the other layers. Taken together, we believe that the needle extracts of P. densiflora have potential application as alternative anti-inflammatory agents or cosmetic material for skin health improvement.