• Proceedings of the KAIS Fall Conference
• /
• 2009.05a
• /
• pp.325-328
• /
• 2009

Recently, as importance of knowledge property right is rising, a patent is being issue. A patent is exclusive rights of knowledge or technique, and it must be registered for approval of rights. Therefore, prediction of patent registration can be important information for company or individuals which gain profit using a patent. In this paper, we proposed a method for prediction of patent registration using text mining and a algorithm for constructing database.

• Review of Korean Society for Internet Information
• /
• v.14 no.3
• /
• pp.52-57
• /
• 2013

Information and Communication Technologies(ICTs) have substantially enlarged both the opportunities to realize one's human rights but have also resulted in the emergence of new challenges. ICTs are so deeply embedded and cental to almost all aspects of human activity. And ICTs are assuming an increasingly central role in all aspects of human and societal development across the world. But this is especially true of the right to privacy, which faces challenges such as profiling and data mining for public(including national security) and private purposes. ICTs access is a fundamental right for all humans in the information age. So we have need for regulation based on human rights in the digital age. And governments have a responsibility to protect individuals against violations of human rights and data protection by public authorities, but also by private entities. In addition, internet governance and multi-stakeholder principle have to be stressed on all of the internet issues because internet governance is included in the principle of democracy which have bottom-up communication and equality. So it is very importance that Internet Governance Forum is the space for a meaningful discussion on public policy issues relating to the internet.

• Korea Trade Review
• /
• v.44 no.3
• /
• pp.271-287
• /
• 2019

This paper reviews and analyzes the ethical issues of multinational corporations (MNCs) in Africa. First, we find that the transparency and institutions of a host country have a negative relationship with the number of ethical violations of the MNCs. Second, this covers the effects of industry characteristics on each category of ethical issues such as the human rights and the environment. Based on the database of "Ethical Consumer", we show that the Auto, Chemical, Finance, and Telecommunication industries are more likely to violate human rights issues, and that Mining, Oil, Cosmetics, and Chemical industries are more likely to pollute the environments. Further, the country of origin does matter: the US and Asian companies are more likely to be involved with the business ethics violations than are their European counterparts.

• Journal of Digital Convergence
• /
• v.18 no.7
• /
• pp.299-306
• /
• 2020

The purpose of this study is to find out specific measures that can help the management strategy of patient-centered medical institutions by conducting research on patient experience surveys of convergence outpatient medical services using data mining techniques according to changes in patient-centered medical culture. Using the raw data of the 2018 Medical Service Experience Survey, 8,843 people over the age of 15 who had patient experience in outpatient medical services were analyzed. Decision tree analysis was performed. The determinants of satisfaction with outpatient medical services patient experience were the doctor's area and patient's rights protection area, and the determinants of intention to recommend outpatient medical services were the doctor's area and facilities comfort. Women evaluated the experience positively in overall satisfaction as compared to men, and those over the age of 60 positively evaluated the overall satisfaction and intention to recommend. It is significant that the outpatient experience decision-making model is presented, and that the doctor's area, patient's rights protection area, and facility comfort are important factors. Long-term research on the 'Medical Service Experience Survey' is needed, and research on the inpatient medical service experience is needed.

• Fashion & Textile Research Journal
• /
• v.24 no.5
• /
• pp.545-556
• /
• 2022

To overcome the pandemic, a new strategy for innovation is in demand throughout the value chains of the fashion industry that emphasize the importance of fashion technology. Accordingly, as various viewpoints and fields of debate are unfolding to consider the direction of change led by fashion technology, it is necessary to make an active value judgment precedent by understanding the differences between various opinions. This study aims to derive keywords from fashion technology used during the pandemic, to infer the characteristics of each type of perspective and to understand their characteristics. For the research, this study combines text mining analysis and content analysis. Text mining analysis is used to find statistical patterns by collecting keywords from big data from online media, and content analysis is used to interpret the data qualitatively. After analyzing the results of this study, the following observations are made. First, the perspective of positive acceptance seeks to maximize the perception and sensory action of fashion through technology; this amplifies experience, an opportunity for innovation and efficiency. Second, critical vigilance highlights the side effects of radical changes in fashion technology, characterized by concerns about capital-centered polarization, threats to human rights, and infringement of creative thinking. Lastly, the perspective of gradual adoption is the gradual convergence of technologies, characterized by the pursuit of an appropriate balance.

• Journal of Intelligence and Information Systems
• /
• v.18 no.4
• /
• pp.59-77
• /
• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• The Journal of Bigdata
• /
• v.2 no.2
• /
• pp.17-31
• /
• 2017

With the development of the big data environment, public institutions also have been providing big data infrastructures. Public data is one of the typical examples, and numerous applications using public data have been provided. One of the cases is related to the employment insurance. All employers have to make contracts for the employment insurance for all employees to protect the rights. However, there are abundant cases where employers avoid to buy insurances. To overcome these challenges, a data-driven approach is needed; however, there are lacks of methodologies to integrate, manage, and analyze the public data. In this paper, we propose a methodology to build a predictive model for identifying whether employers have made the contracts of employment insurance based on public data. The methodology includes collection, integration, pre-processing, analysis of data and generating prediction models based on process mining and data mining techniques. Also, we verify the methodology with case studies.

• Journal of Convergence for Information Technology
• /
• v.11 no.9
• /
• pp.17-27
• /
• 2021

The aim of this research was to identify the issue of the work permit system and consciousness of the people on the system, and to suggest some ideas on the government policies on it. To achieve the aim of research, this research used text mining based on social data. This research collected 1,453,272 texts from 6,217 units of online documents which contained 'work permit system' from January to December, 2020 using Textom, and did text-mining and social network analysis. This research extracted 100 key words frequently mentioned from the analyses of data top-level key word frequency, and degree centrality analysis, and constituted job problem, importance of policy process, competitiveness in the respect of industries, and improvement of living conditions of foreign workers as major key words. In addition, through semantic network analysis, this research figured out major awareness like 'employment policy', and various kinds of ambient awareness like 'international cooperation', 'workers' human rights', 'law', 'recruitment of foreigners', 'corporate competitiveness', 'immigrant culture' and 'foreign workforce management'. Finally, this research suggested some ideas worth considering in establishing government policies on the work permit system and doing related researches.

• Journal of forensic and investigative science
• /
• v.1 no.2
• /
• pp.5-19
• /
• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.4
• /
• pp.1458-1467
• /
• 2010

Recently, imitation and infringement rights of an intellectual property are being recognized as impediments to nation's industrial growth. To prevent the huge loss which comes from theses impediments, many researchers are studying protection and efficient management of an intellectual property in various ways. Especially, the prediction of patent registration is very important part to protect and assert intellectual property rights. In this study, we propose the patent document analysis method by using text mining to predict whether the patent is registered or rejected. In the first instance, the proposed method builds the database by using the word frequencies of the rejected patent documents. And comparing the builded database with another patent documents draws the similarity value between each patent document and the database. In this study, we used k-means which is partitioning clustering algorithm to select criteria value of patent rejection. In result, we found conclusion that some patent which similar to rejected patent have strong possibility of rejection. We used U.S.A patent documents about bluetooth technology, solar battery technology and display technology for experiment data.