• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.3
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• pp.419-424
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• 2014

Metabolite profiling of blueberry (cultivar "Spartan") was performed by extraction using different solvents, methanol and ethyl acetate, through metabolomic analysis using LC-MS/MS. Unsupervised classification method (PCA) and supervised prediction model (OPLS-DA) provided good categorization of metabolites according to the extraction solvents. Metabolites of the anthocyanin family, including delphinidin hexoside, delphinidin, 5-O-feruloylquinic acid, malvidin hexoside, malvidin-3-arabinoside, petunidin-3-arabinoside, and petunidin hexoside, were mainly detected in methanol fractions, whereas those of the flavonoid family, including chlorogenic acid, chlorogenic acid dimer, 6,8-di-C-arabinopyranosyl-luteolin, and luteolin were successfully prepared in the ethyl acetate fraction. Thus, metabolomic analysis of blueberry extracts allows for the simple profiling of whole and distinctive metabolites for future applications.

• Food Science of Animal Resources
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• v.43 no.6
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• pp.1055-1066
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• 2023

The cultured meat industry is continuously evolving due to the collective efforts of cultured meat companies and academics worldwide. Though still technologically limited, recent reports of regulatory approvals for cultured meat companies have initiated the standards-based approach towards cultured meat production. Incidents of deception in the meat industry call for fool-proof authentication methods to ensure consumer safety, product quality, and traceability. The cultured meat industry is not exempt from the threats of food fraud. Meat authentication techniques based on DNA, protein, and metabolite fingerprints of animal meat species needs to be evaluated for their applicability to cultured meat. Technique-based categorization of cultured meat products could ease the identification of appropriate authentication methods. The combination of methods with high sensitivity and specificity is key to increasing the accuracy and precision of meat authentication. The identification of markers (both physical and biochemical) to differentiate conventional meat from cultured meat needs to be established to ensure overall product traceability. The current review briefly discusses some areas in the cultured meat industry that are vulnerable to food fraud. Specifically, it targets the current meat and meat product authentication tests to emphasize the need for ensuring the traceability of cultured meat.

• Journal of the Ergonomics Society of Korea
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• v.36 no.5
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• pp.499-523
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• 2017

Objective: This study is to provide a foundation for developing comprehensive ergonomic design guidelines for in-vehicle information systems (IVIS) by giving an overview of existing researches as well as setting further research directions. Background: The drivers get much more information recently while interacting with new safety functions of the cars. To avoid cognitive overload of the drivers, IVIS should be deigned appropriately by considering various human factors and task conditions. Method: We gathered, analyzed, and summarized ergonomic researches concerned with IVIS design conducted inside and outside Korea according to a categorization system proposed in the study. Frequency analysis was conducted for figuring out what kind of issues took major part of the researches, and their trends across time and regions. Results: Compared to domestic researches, those done in overseas tend to deal with more variety of independent, dependent and extraneous variables. The overseas researches also showed a tendency to get ahead in making ergonomic guidelines for IVIS design by adapting and integrating the results from previous researches. Conclusion and Application: There have been many researches regarding with ergonomic IVIS design, but some of their results became old-fashioned as the technology evolved. Not many researches have considered diverse human factors regarding the drivers' characteristics except age and gender. It is expected that researchers and designers take advantage of this study to find and review relevant results and set out issues of their own for more progressive researches of the field.

• Journal of Korean Library and Information Science Society
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• v.53 no.2
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• pp.95-115
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• 2022

The purpose of this study is to transform the sub-categorization terms of the National Science and Technology Standards Classification System into technical keywords by applying a machine learning algorithm. For this purpose, AttentionMeSH was used as a learning algorithm suitable for topic word recommendation. For source data, four-year research status files from 2017 to 2020, refined by the Korea Institute of Science and Technology Planning and Evaluation, were used. For learning, four attributes that well express the research content were used: task name, research goal, research abstract, and expected effect. As a result, it was confirmed that the result of MiF 0.6377 was derived when the threshold was 0.5. In order to utilize machine learning in actual work in the future and to secure technical keywords, it is expected that it will be necessary to establish a term management system and secure data of various attributes.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.113-123
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• 2024

Stock market news sentiment analysis (SA) aims to identify the attitudes of the news of the stock on the official platforms toward companies' stocks. It supports making the right decision in investing or analysts' evaluation. However, the research on Arabic SA is limited compared to that on English SA due to the complexity and limited corpora of the Arabic language. This paper develops a model of sentiment classification to predict the polarity of Arabic stock news in microblogs. Also, it aims to extract the reasons which lead to polarity categorization as the main economic causes or aspects based on semantic unity. Therefore, this paper presents an Arabic SA approach based on the logistic regression model and the Bidirectional Encoder Representations from Transformers (BERT) model. The proposed model is used to classify articles as positive, negative, or neutral. It was trained on the basis of data collected from an official Saudi stock market article platform that was later preprocessed and labeled. Moreover, the economic reasons for the articles based on semantic unit, divided into seven economic aspects to highlight the polarity of the articles, were investigated. The supervised BERT model obtained 88% article classification accuracy based on SA, and the unsupervised mean Word2Vec encoder obtained 80% economic-aspect clustering accuracy. Predicting polarity classification on the Arabic stock market news and their economic reasons would provide valuable benefits to the stock SA field.

• Journal of Information Technology Applications and Management
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• v.21 no.4_spc
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• pp.415-429
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• 2014

With the advent of smart devices, users are able to connect to each other through the Internet without the constraints of time and space. Because the Internet has become increasingly important to users in their everyday lives, reliance on it has grown. As a result, the number of web sites constantly increases and the competition between these sites becomes more intense. Even those sites that operate successfully struggle to establish new strategies for customer retention and customer development in order to survive. Many companies use various customer information in order to establish marketing strategies based on customer group segmentation A method commonly used to determine the customer groups of individual sites is to infer customer characteristics based on the customers' demographic information. However, such information cannot sufficiently represent the real characteristics of customers. For example, users who have similar demographic characteristics could nonetheless have different interests and, therefore, different buying needs. Hence, in this study, customers' interests are first identified through an analysis of their Internet news inquiry records. This information is then integrated in order to identify each web category. The study then analyzes the possibilities for the practical use of the proposed methodology through its application to actual Internet news inquiry records and web site browsing histories.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.386-391
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• 2015

This paper proposed the optimal attributes for text classification based on Korean and Chinese linguistic features. The experiments committed to discover which is the best feature among n-grams which is known as language independent, morphemes that have language dependency and some other feature sets consisted with n-grams and morphemes showed best results. This paper used SVM classifier and Internet news for text classification. As a result, bi-gram was the best feature in Korean text categorization with the highest F1-Measure of 87.07%, and for Chinese document classification, 'uni-gram+noun+verb+adjective+idiom', which is the combined feature set, showed the best performance with the highest F1-Measure of 82.79%.

• Journal of Korea Game Society
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• v.5 no.2
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• pp.3-9
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• 2005

Interactive real-time 3D enables users to explore virtual three dimensional environments and also experience contents in an absorbing fashion. Unlike other media, Interactive real-time 3D users take an active role in the process of "real-time fashion" where action and reaction occur instantly in a digital 3D structure. Once the components and origins of interactive real-time 3D is made, it is possible making principles of the beauty that help decide success or failure of real-time 3D in two way system. Substantial real-time 3D has not yet passed 10 years so it was unable to make sufficient precedents of fundamental artistic value based upon the credibility of the media. The goal is to explain the new form of design in relation to general principles of arts at the same time to understand the technical definition better. Concepts of historical documentation are explained with an example of categorization of recent video game and recent technology. This thesis concludes with rough forecast on the future interactive real time 3D. Since the medium began relatively recently and is developing in the rapid pace, recent analyses, though clear forecast is difficult, tend to investigate potential directions to some level the field allows.

• Asia pacific journal of information systems
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• v.4 no.2
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• pp.214-246
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• 1994

As the use of information technology continues to bring a dramatic increase in the amount of data available to managers, researchers have noted that having too much data can be as much of a problem as having too little. It becomes very important to understand the effects of "information explosion" on the way managers perform their work. This study examines the effect of the amount of available data on the process and outcome of thinking within a context where managers are equipped with computing tools. The purpose of this study is to better understand how managers respond cognitively to increased information availability. In this experiment with 104 MBAs three groups of subjects were asked to identify high and low potential market categories for effective direct mail sales based on three different amount of computer-based socioeconomic data designed based on existing research on cognition and information overload. Analyses of the outcomes showed that the group with medium amount of data used data and computer-based analysis tools most effectively and efficiently. We expect that the study will provide us a base to relate future MIS research to theories on cognition in such related fields as psychology and organizational behavior.

• Journal of Korea Multimedia Society
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• v.12 no.6
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• pp.867-875
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• 2009

More and richer information sources and services are available on the web everyday. However, harmful information, such as adult content, is not appropriate for all users, notably children. Since internet is a worldwide open network, it has a limit to regulate users providing harmful contents through each countrie's national laws or systems. Additionally it is not a desirable way of developing a certain system-specific classification technology for harmful contents, because internet users can contact with them in diverse ways, for example, porn sites, harmful spams, or peer-to-peer networks, etc. Therefore, it is being emphasized to research and develop context-based core technologies for classifying harmful contents. In this paper, we propose an efficient text filter for blocking harmful texts of web documents using context-based technologies and examine which algorithms for feature selection, the process that select content terms, as features, can be useful for text categorization in all content term occurs in documents, are suitable for classifying harmful contents through implementation and experiment.