• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.133-148
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• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• The Korean Society of Law and Medicine
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• v.13 no.1
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• pp.199-247
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• 2012

According to the review and analysis of medical cases that are assigned to the Supreme Court and all local High Court in 2011 and that are presented in the media, it was found that the following categories were taken seriously, medical and pharmaceutical product liability, the third principle of trust between medical institutions, negligence and causation estimation, responsibility limit, the meaning of medical records and related judgment of disturbed substantiation, Oriental doctors' duties to explain the procedures, IMS events, whether one can claim for each medical care operated by non-physician health care institutions to the nonmedical domain in the National Health Insurance Corporation, and the basis of norms for each claim. In the cases related to medical pharmaceutical product liability, Supreme Court alleviated burden of proof for accidents with medical and pharmaceutical products prior to the practice of Product Liability Law and onset the point of negative prescription as the time of damage strikes to condition feasibility of the specific situation. In the cases related to the 3rd principle of trust between medical institutions, the Supreme Court refused to sentence the doctor who has trusted the judgment of the same third-party doctors the violations of the care duty. With respect to proof of a causal relationship and damages in a medical negligence case, the Supreme Court decided that it is unjust to deny negligence by the materials of causal relationship rejecting the original verdict and clarified that the causal relationship shall not deny the reasons to limit doctors' responsibilities. In order not put burden on patients with disadvantages in which medical records and the description of the practice or the most fundamental and important evidence to prove negligence and causation are being neglected, the Supreme Court admitted in the hospital's responsibility for the case of the neonate death of suffocation without properly listed fetal heart rate and uterine contraction monitor. On the other hand, the Seoul Western District Court has admitted alimony for altering and forging medical records. With respect to doctors' obligations to description, the Supreme Court decided that it is necessary to explain the foreseen risks by the combination of oriental and western medicines emphasizing the right of patient's self-determination. However, questions have arisen whether it is realistically feasible or not. In a case of an unlicensed doctor performing intramuscular stimulation treatment (IMS), the Supreme Court put off its decision if it was an unlicensed medical practice as to put limitation of eastern and western medical practices, but it declared that IMS practice was an acupuncture treatment therefore the plaintiff's conduct being an illegal act. In the future, clear judgment on this matter should be made. With respect to the claim of bills from non-physical health care institutions, the Supreme Court decided to void it for the implementation of the arrangement is contrary to the commitments made in the medical law and therefore, it is invalid to claim. In addition, contrast to the private healthcare professionals, who are subject to redemption according to the National Healthcare Insurance Law, the Seoul High Court explicitly confirmed that the non-professionals who receive the tort operating profit must return the unjust enrichment and have the liability for damages. As mentioned above, a relatively wide range of topics were discussed in medical field of 2011. In Korea's health care environment undergoing complex changes day by day, it is expected to see more diverse and in-depth discussions striding out to the development in the field of health care.

• Journal of Life Science
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• v.17 no.12
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• pp.1641-1648
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• 2007

Yippee-like proteins, which have been identified as the homolog of Drosophila yippee protein containing a zinc-finger domain, are known to be highly conserved among eukaryotes. However, their functional roles are still poorly understood. Recently we initiated ordered differential display (ODD)-polymerase chain reaction (PCR) to isolate genes of which expressions are altered following activation of human T cells. On the ODD-PCR image, one PCR-product detected in unstimulated T cells was not detectable at the time when the activated T cells traversed near $G_1/S$ boundary following activation by immobilized anti-CD3. Cloning and nucleotide sequence analysis revealed that the PCR-product was yippee-like 5 (YPEL5) gene, which was known as a human homolog of the Drosophila yippee gene. Northern blot analysis confirmed the amount of ${\sim}2.2$ kb YPEL5 mRNA expression detectable in unstimulated T cells was sustained until 1.5 hr after activation and then rapidly declined to undetectable level by 5 hr. Ectopic expression of YPEL5 gene in human cervix epitheloid carcinoma HeLa cells caused a significant reduction in cell proliferation to the level of 47% of the control. Expression of GFP-YPEL5 fusion protein in HeLa cells showed its nuclear localization. These results demonstrated that the expression level of human YPEL5 mRNA was negatively regulated in the early stage of T cell activation, and suggested that YPEL5 might exert an inhibitory effect on the cell proliferation as a nuclear protein.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.125-139
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• 2013

Big data refers to the data that cannot be processes with conventional contemporary data technologies. As smart devices and social network services produces vast amount of data, big data attracts much attention from researchers. There are strong demands form governments and industries for bib data as it can create new values by drawing business insights from data. Since various new technologies to process big data introduced, academic communities also show much interest to the big data domain. A notable advance related to the big data technology has been in various fields. Big data technology makes it possible to access, collect, and save individual's personal data. These technologies enable the analysis of huge amounts of data with lower cost and less time, which is impossible to achieve with traditional methods. It even detects personal information that people do not want to open. Therefore, people using information technology such as the Internet or online services have some level of privacy concerns, and such feelings can hinder continued use of information systems. For example, SNS offers various benefits, but users are sometimes highly exposed to privacy intrusions because they write too much personal information on it. Even though users post their personal information on the Internet by themselves, the data sometimes is not under control of the users. Once the private data is posed on the Internet, it can be transferred to anywhere by a few clicks, and can be abused to create fake identity. In this way, privacy intrusion happens. This study aims to investigate how perceived personal information overload in SNS affects user's risk perception and information privacy concerns. Also, it examines the relationship between the concerns and user resistance behavior. A survey approach and structural equation modeling method are employed for data collection and analysis. This study contributes meaningful insights for academic researchers and policy makers who are planning to develop guidelines for privacy protection. The study shows that information overload on the social network services can bring the significant increase of users' perceived level of privacy risks. In turn, the perceived privacy risks leads to the increased level of privacy concerns. IF privacy concerns increase, it can affect users to from a negative or resistant attitude toward system use. The resistance attitude may lead users to discontinue the use of social network services. Furthermore, information overload is mediated by perceived risks to affect privacy concerns rather than has direct influence on perceived risk. It implies that resistance to the system use can be diminished by reducing perceived risks of users. Given that users' resistant behavior become salient when they have high privacy concerns, the measures to alleviate users' privacy concerns should be conceived. This study makes academic contribution of integrating traditional information overload theory and user resistance theory to investigate perceived privacy concerns in current IS contexts. There is little big data research which examined the technology with empirical and behavioral approach, as the research topic has just emerged. It also makes practical contributions. Information overload connects to the increased level of perceived privacy risks, and discontinued use of the information system. To keep users from departing the system, organizations should develop a system in which private data is controlled and managed with ease. This study suggests that actions to lower the level of perceived risks and privacy concerns should be taken for information systems continuance.

• Journal of Korean Medical Science
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• v.33 no.49
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• pp.323.1-323.12
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• 2018

Background: Vitamin K antagonist (VKA) to prevent thromboembolism in non-valvular atrial fibrillation (NVAF) patients has limitations such as drug interaction. This study investigated the clinical characteristics of Korean patients treated with VKA for stroke prevention and assessed quality of VKA therapy and treatment satisfaction. Methods: We conducted a multicenter, prospective, non-interventional study. Patients with $CHADS_2{\geq}1$ and treated with VKA (started within the last 3 months) were enrolled from April 2013 to March 2014. Demographic and clinical features including risk factors of stroke and VKA treatment information was collected at baseline. Treatment patterns and international normalized ratio (INR) level were evaluated during follow-up. Time in therapeutic range (TTR) > 60% indicated well-controlled INR. Treatment satisfaction on the VKA use was measured by Treatment Satisfaction Questionnaire for Medication (TSQM) after 3 months of follow-up. Results: A total of 877 patients (age, 67; male, 60%) were enrolled and followed up for one year. More than half of patients (56%) had $CHADS_2{\geq}2$ and 83.6% had $CHA_2DS_2-VASc{\geq}2$. A total of 852 patients had one or more INR measurement during their follow-up period. Among those patients, 25.5% discontinued VKA treatment during follow-up. Of all patients, 626 patients (73%) had poor-controlled INR (TTR < 60%) measure. Patients' treatment satisfaction measured with TSQM was 55.6 in global satisfaction domain. Conclusion: INR was poorly controlled in Korean NVAF patients treated with VKA. VKA users also showed low treatment satisfaction.

• Journal of agriculture & life science
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• v.45 no.6
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• pp.73-80
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• 2011

To investigate the characteristics of plant growth and flower quality of gerbera 'Sunny Lemon' by amount of nutrient solution, young seedling plants, 'Sunny Lemon' were transplanted to rock-wool and medium of peat moss and perlite mixed with 1 to 2 and they were acclimatized in greenhouse during about 1 month. Nutrient solution supplied to the plants is sonneveld solution of 1/2 concentration and treatments launched June 24, 2010 when average plant height was $20{\pm}1cm$. Nutrient contents as a standard for starting point of irrigation by time domain reflectometry (TDR) were determined with 60-65%, 70-75%, and 80-85%. Results of growth during vegetative growth, plant height, leaf width and leaf number increased by 10% in rockwool, but they were not significantly different. As for plant growth depending on nutrient content, 80-85% treatment showed the highest values. Leaf number increased by 60%, and leaf width and plant height had a about 40% increase than initial growth. Effectiveness for flower quality, yield and days to flowering were superior when nutrient content of media was higher than in the others. Especially, average days to flowering in 80-85% content was advanced by 7-10 days compared to the day in 60-65% treatment. The total amount of nutrient supply per plant was higher in mixed medium than in rockwool, but change patterns of EC and pH were enhanced in rockwool. Based on our results, we recommended that growth, cut flower, and yield of gerbera 'Sunny Lemon' were more effective when nutrient content of mixed medium was maintained at 80-85%.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.804-811
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• 2010

The lower portion of sloping paddy fields normally contains excessive moisture and the higher water table caused by the inflow of ground water from the upper part of the field resulting in non-uniform water content distribution. Four drainage methods namely Open Ditch, Vinyl Barrier, Pipe Drainage and Tube Bundle for multiple land use were installed within 1-m position from the lower edge of the upper embankment of sloping alluvial paddy fields. Knowledge of the spatial variability of soil water properties is of primary importance for management of agricultural lands. This study was conducted to evaluate the effect of drainage in the soil on spatial variability of soil water content using the geostatistical analysis. The soil water content was collected by a TDR (Time Domain Reflectometry) sensor after the installation of subsurface drainage on regular square grid of 80 m at 20 m paddy field located at Oesan-ri, Buk-myeon, Changwon-si in alluvial slopping paddy fields ($35^{\circ}22^{\prime}$ N, $128^{\circ}35^{\prime}$). In order to obtain the most accurate field information, the sampling grid was divided 3 m by 3 m unit mesh by four drainage types. The results showed that spatial variance of soil water content by subsurface drainage was reduced, though yield of soybean showed the same trends. Value of "sill" of soil water content with semivariogram was 9.7 in Pipe Drainage, 86.2 in Open Ditch, and 66.8 in Vinyl Barrier and 15.7 in Tube Bundle.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Korean Association of Arts Management
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• no.58
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• pp.5-47
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• 2021

This paper is a story about five middle-aged men in their 50s who suddenly began their photographic activities as they reached middle age. In the perspective of Borudieu's cultural capital theory, this study observes five men in their 50s by implementing in-depth interviews about the motivation behind taking photographs, the experience of photography activities, and the rewards of these activities. The theory has undergone a theoretical revision with the criticism that factors other than the class can be influential. Based on these ideas, I have proceeded my study by preferentially grasping the notion of the 'field' in accordance with the specific history of Korean society. Therefore, this study sought to more specifically understand the various photographic activities of middle-class men in their 50s by referring Coskuner-Balli and Thompson's argument(2013), which revised 2018's cultural captial theory and proposed the concept of 'subordinate cultural capital' and 'leisure capital' who proposed by Backlund, E. A. & Kuentzel, W. F.(2013). As a middle-class men in their 50s, research participants have grown up and worked in a social atmosphere where economic capital is recognized as an individual's ability. However, they are faced with the value that the knowledge and taste towards culture and arts is one's identity. In addition to the subjective deprivation that arises from this situation, the lifespan characteristic of their age that it is on the brink of the old age appeared to have influenced them to put their psychological motivation immediately into practice. Economic capital was the main conversion terms to move form interest to practice, which includes 'time' as a resource as well as money. With the cultural practices being expanded since their creation of photographs, the reason that these expansions can be maintained more actively lies in their identity as 'cultural artist' that is consolidated in new relationships in the sharing of photographic activities. In this way, photographic activities grant a symbolic status of 'a middle-aged man who actively builds and expresses his identity' through the conversion of accumulating cultural capital and the conversion into social capital. Furthermore, the recognized scope of the symbolic capital acquired by the research participants is in the domain of the private life that is family and acquaintance. Especially, they were gaining a great psychological reward from their children's recognition that they are not just a 'breadwinner' but 'dad who cultivates himself with a culture and arts'. Accordingly, by considering that 'generation' other than class can be a meaningful discussion point when understanding Korea society from the perspective of cultural theory, this study is meaningful that a more flexible understanding of cultural theory can give a glimpse into the possibility of a more specific and diverse approach that will arise in the discussion of culture and arts education.