• Journal of Intelligence and Information Systems
• /
• v.16 no.4
• /
• pp.67-84
• /
• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.

• Journal of Intelligence and Information Systems
• /
• v.20 no.2
• /
• pp.93-107
• /
• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• The Journal of Korean Academy of Prosthodontics
• /
• v.47 no.2
• /
• pp.215-221
• /
• 2009

Statement of problem: Recently the populations of patients receiving implant surgery are greatly increase for maintaining oral health. Purpose: This study was conducted for implanted patients to assess the chewing ability patient satisfaction level and changes in quality of life before and after the implant surgery. Material and methods: The current study subjected 109 adult patients, older than 20 years of age, who received implant surgery from December, 2006 to October, 2007 at the 6 dental clinics located at Daegu and Ulsan metropolitan cities. Twice of surveys were conducted for the patients before and after receiving the dental implant surgery. Results: As the motivation of receiving implant surgery, 45.9% of the patients selected the surgery for the chance o "f chewing function recovery", and " failure of treatment and complications" was found to be the most worrisome at the time of surgery by recording 38.5%.The satisfaction level before the implant surgery scored 30.37, while the score was increased to 45.01 after the surgery by showing a significant difference before and after receiving the surgery(P<.001). Regard on the surgery, 91.8% of the patients responded as "Satisfy", and 89% of the study subjects responded that they have willingness to recommend the surgery to their families and friends. The chewing ability score measured by using the surveys on edible foods, the score before the surgery was 15.24, while the score was increased to 19.11 after the surgery by showing a significant difference before and after receiving the surgery(P<.001). The quality of life score was also found to be increased to 11.17 after the surgery from 9.99 before the surgery by showing a significant difference(P<.001). Conclusion: In a future, the studies on the numbers of implanted loss teeth and the location of tooth loss are necessary, more long-term follow study are needed, and it is thought to be necessary to enlarge the sample size of subjects in conducting the studies.

• Korean Security Journal
• /
• no.43
• /
• pp.205-230
• /
• 2015

This study aims to compare the differences of crucial factors that are associated with probation warning tickets between sex offenders and non-sex offenders in South Korea. Serious high-profile cases have occurred in recent years which resulted in public and political conners for successful sex offender management and monitoring strategy through community corrections. The official response has been to initiate a series of legislative probation and parole measures by using GPS electronic monitoring system, chemical castration, and sex offender registry and notification. In this context, the current study is designed to explore the major factors that could affect the failure of probation by comparing the differences between sex offenders and non-sex offenders in terms of their major factors which are related to the failure of probation. The failure of probation is measured by the number of warning tickets which would be issued when there is the violation of probation conditions. The data is obtained from Seoul Probation office from January, 29, 2014 to February, 28, 2014. The sample number of sex offenders is 144 and the number of non-sex offenders is 1,460. The data includes the information regarding the offenders who completed their probation order after they were assigned to Seoul Probation in 2013. Furthermore, this study uses the chi-square and logistic regression analysis by using SPSS statistical package program. The result demonstrated that only prior criminal history was statistically significant factor that was related to the number of warning tickets in the sex offender group when other variables were controlled($X^2=25.15$, p<0.05, Nagelkerke $R^2=0.23$)(b=0.19, SE=0.08, p<0.05). By contrast, there were various factors that were associated with the number of warning tickets in non-sex offender group. Specifically, the logistic regression analysis for the non-sex offenders showed that demographic variable(marital status and employment type), offender-victim relationships, alcohol addiction, violent behavior, prior criminal history, community service order, and attendance order were statistically significant factors that were associated with the odds of warning tickets. Further policy implication will be discussed.

• Journal of the Korean Society of Radiology
• /
• v.4 no.1
• /
• pp.5-9
• /
• 2010

In recent, a digital x-ray detector attracted worldwide attention and there are many studies to commercialize. There are two methods in digital x-ray detector. This method is an Indirect method and Direct method. This study is to see the differences between the digital x-ray detector based on a-Se used in the existing indirect conversion method and an x-ray conversion material that has better SNR(Signal-to-noise ratio) and property than the a-Se. To solve the problem that is difficult to make a large area film using Screen-Print method, we used a Screen-Print method. In this study, we used a polyclystal $HgI_2$ as x-ray conversion material and a sample thickness is $150{\mu}m$ and an area is $3cm{\times}3cm$. ITO(Indium-Tin-Oxide) electrode was used as top electrode using a Magnetron Sputtering System and each area is $3cm{\times}3cm$, $2cm{\times}2cm$ and $1cm{\times}1cm$ and then we evaluated darkcurrent, sensitivity and SNR of the $HgI_2$ film are measured, then we evaluated the electrical properties. And we used a current integration mode when I-V test. This experiment shows that the sensitivity increases in accordance with the area of the electrode but the SNR is decreased because of the high darkcurrent. Through fabricating of various thicknesses and optimal electrodes, we will optimize SNR in the future work.

• Journal of Food Hygiene and Safety
• /
• v.19 no.1
• /
• pp.1-8
• /
• 2004

This study was executed to determine the cumulative dietary risk of PAHs exposed by food ingestion. Food samples including barbecued beef, barbecued pork, grilled chicken, ham, bacon and vegetable oil which were collected from food markets. These samples were saponified, extracted and cleaned up to purify PAHs, and then the purified sample solutions were analyzed by HPLC-FL. Generally, the levels of total PAHs in barbecued beef (0.2 ppb), bacon (0.3 ppb), barbecued pork (0.7 ppb), ham (0.8 ppb), and vegetable oil (1.2 ppb) were low, whereas the level of total PAHs in grilled chicken (9.3 ppb) was significantly high. For the exposure assessment of PAHs due to food ingestion, PAHs levels converted into TEQ$_{BaP}$, the average body weight for 20-73 age group and consumed levels of food proposed from report on the National Health and Nutrition Survey were used. The estimated lifetime average daily intake of dietary PAHs was 4.32${\times}$10$^{-4}$ $\mu\textrm{g}$-TEQ$_{BaP}$kg/day as the mean value. The dietary risk adjusted to cancer potency of benzo(a)pyrene as 7.3 (mg/kg/day)$^{-1}$ was 3.44${\times}$10$^{-6}$ based on current data.ata.

• Journal of Food Hygiene and Safety
• /
• v.29 no.4
• /
• pp.268-277
• /
• 2014

This study assessed microbiological hazards at postharvest stage of dropwort farms (A, B, C, D, E, F, G, H, I) located in 4 different areas in Korea. The samples were assessed for sanitary indication bacteria (total aerobic bacteria, coliform, and Escherichia coli) and pathogenic bacteria (Escherichia coli O157:H7, Listeria monocytogenes, Staphylococcus aureus and Bacillus cereus). Total aerobic bacteria and coliform in 9 dropwort farms were detected at the levels of 0~7.00 and 0~4.25 log CFU/g, mL, of $100cm^2$. In particular, microbial contamination in worker's hand showed higher than cultivation environment factors. Escherichia coli was detected in several farms of soil, irrigation water, washing water and worker's hand and also, dropwort in these farms was contaminated with E. coli (positive reaction). In case of pathogenic bacteria, B. cereus was detected at the highest levels in soil. S. aureus was detected qualitatively from only one sample of dropwort washed by water. E. coli O157:H7 and L. monocytogenes were not detected. Although dropwort pass through 2 process (trimming and washing), the microbial contamination was not differ significantly before and after which indicates that current washing system was not effect on reduction of microorganism. From these results, the postharvest environment and workers have been considered as cross-contamination factors. Thus, processing equipments and personal hygiene should be managed to reduce the microbial contamination of dropwort. Accordingly management system such as good agricultural practices (GAP) criteria is needed for the safety of dropwort

• Journal of The Geomorphological Association of Korea
• /
• v.20 no.1
• /
• pp.11-20
• /
• 2013

The pollen analysis was performed targeting the valley plain alluvium of Jangjae-ri, Asan area in order to clarify the climate and vegetation environment of the Last glacial maximum and the Late glacial in terms of Gokgyo River Watershed In Asan-City, Korea. The sample collection point gets included in the current deciduous broadleaf forest zone (south cool temperate zone). The results are as follows. (1) The vegetation environment of about 19,300-14,100yrB.P. at the investigation area is mainly classified into YJ-I period and YJ-II period while YJ-Ia period is classified once again into YJ-Ia period and YJ-Ib period. YJ-Ia period (19,300-17,500yrB.P.) is correlated with the Last Glacial Maximum while the vegetation at the time has relatively a little wide distribution area of grassland compared to the forest and the forest vegetation of this time period is the mixed conifer and deciduous broad-leaved forest. YJ-Ib period (15,400-14,750yrB.P.) is correlated with the Late glacial (or the Last Glacial Maximum) and the distribution area of grassland became wider compared to the forest. While the forest vegetation of this time period is the mixed conifer and deciduous broad-leaved forest, a difference exists in terms of the dominant tree species. YJ-II period (about 14,650-14,100yrB.P.) is correlated with the Last glacial while the distribution area of grassland became even wider than the forest compared to the YJ-Ib in case of the vegetation at the time and the forest vegetation of this time period is the coniferous forest. (2) Both YJ-I period and YJ-II period were relatively cold and dry compared the End of Late Glacial (about 12,000-10,000yrB.P.)~Early Holocene (10,000-8,500yrB.P.), Also, YJ-II period was relatively colder than the YJ-I period and the YJ-Ib period was relatively more humid than the YJ-Ia period.

• Journal of Korean Home Economics Education Association
• /
• v.33 no.1
• /
• pp.1-16
• /
• 2021

The purpose of this study was to investigate the needs of middle school students for the practical reasoning in a distance learning environment, to verify the needs differences based on the learner's personal characteristics, student-teacher interaction, and student-student interaction, and to investigate the relationships among student-teacher interaction, voluntary participation of students, and the students' perception of the extent to which practical reasoning is implemented in distance learning. For this purpose, 1,842 middle school students from seven schools in Gyeonggi, Daejeon, Chungbuk, and Sejong areas were surveyed online to investigate the importance of the practical reasoning questions and the how much practical reasoning is implemented in current distance learning. Among them, 1,095 responses were used for final analysis and descriptive statistics, independent sample t-test, one-way ANOVA, and path analysis were conducted. As a result of the study, first, middle school students acknowledged that the practical reasoning was important with the importance average 3.76. Based on the locus for focus model, the priorities of the needs in home economics class were examined, and the values and importance of the problem, and the ramification of the solution were considered to be of high priority. Second, characteristics of middle school students, student-teacher interaction and student-student interaction were found to have positive influence on needs for practical reasoning, while no difference were found by gender or voluntary participation in distance learning. Third, the voluntary participation of students and the student-teacher interaction in distance learning had a positive (+) significant effect on perceived implementation of practical reasoning, yet negative (-) significant effect on needs for practical reasoning.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.7
• /
• pp.271-278
• /
• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.