• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Korean Security Journal
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• no.10
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• pp.323-354
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• 2005

As with the vast array of computer technology and its rapid development, along with the entry of the internet as one of the necessities of life, the so-called cyber space has become a vital component of our modern day living. While such cyber space has provided the society with much convenience and utility as to the gathering and acquiring of information, crimes involving cyber space has accordingly increased in both number and form, Nevertheless, the conventional law as existed before the development of the cyber space were unable to meet the demands of this new breed of crime, which inevitably led to the gap in the government ability to punish such criminals, Thus, in response to the rising number of cyber crimes, a large number of nations have either created or is in the process of committing human and financial resources to strengthen the investigative powers relating to cyber crimes and creating a new area of prohibiting such crimes. As a overview of cyber crime, (1)defines the terms, describes features of cyber crime, (2)explains the international prevention necessity of cyber crime, and (3)the necessity of legislating the cyber crime Fundamental Act (4)the recognition of the evidential values on the confiscated electronic records and reviews types of cyber crime including cyberterror. Lastly, emphasizes necessity on international cooperation for prevention of cyber crime as usual.

• Journal of Periodontal and Implant Science
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• v.46 no.6
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• pp.382-395
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• 2016

Purpose: Alendronate has been proposed as a local and systemic drug treatment used as an adjunct to scaling and root planing (SRP) for the treatment of periodontitis. However, its effectiveness has yet to be conclusively established. The purpose of the present meta-analysis was to assess the effectiveness of SRP with alendronate on periodontitis compared to SRP alone. Methods: Five electronic databases were used by 2 independent reviewers to identify relevant articles from the earliest records up to September 2016. Randomized controlled trials (RCTs) comparing SRP with alendronate to SRP with placebo in the treatment of periodontitis were included. The outcome measures were changes in bone defect fill, probing depth (PD), and clinical attachment level (CAL) from baseline to 6 months. A fixed-effect or random-effect model was used to pool the extracted data, as appropriate. Mean differences (MDs) with 95% confidence intervals (CIs) were calculated. Heterogeneity was assessed using the Cochrane ${\chi}^2$ and $I^2$ tests. Results: After the selection process, 8 articles were included in the meta-analysis. Compared with SRP alone, the adjunctive mean benefits of locally delivered alendronate were 38.25% for bone defect fill increase (95% CI=33.05%-43.45%; P<0.001; $I^2=94.0%$), 2.29 mm for PD reduction (95% CI=2.07-2.52 mm; P<0.001; $I^2=0.0%$) and 1.92 mm for CAL gain (95% CI=1.55-2.30 mm; P<0.001; $I^2=66.0%$). In addition, systemically administered alendronate with SRP significantly reduced PD by 0.36 mm (95% CI=0.18-0.55 mm; P<0.001; $I^2=0.0%$) and increased CAL by 0.39 mm (95% CI=0.11-0.68 mm; P=0.006; $I^2=6.0%$). Conclusions: The collective evidence regarding the adjunctive use of alendronate locally and systemically with SRP indicates that the combined treatment can improve the efficacy of non-surgical periodontal therapy on increasing CAL and bone defect fill and reducing PD. However, precautions must be exercised in interpreting these results, and multicenter studies evaluating this specific application should be carried out.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.681-689
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• 2019

Web usage pattern discovery is an advanced means by using web log data, and it's also a specific application of data mining technology in Web log data mining. In education Data Mining (DM) is the application of Data Mining techniques to educational data (such as Web logs of University, e-learning, adaptive hypermedia and intelligent tutoring systems, etc.), and so, its objective is to analyze these types of data in order to resolve educational research issues. In this paper, the Web log data of a university are used as the research object of data mining. With using the database OLAP technology the Web log data are preprocessed into the data format that can be used for data mining, and the processing results are stored into the MSSQL. At the same time the basic data statistics and analysis are completed based on the processed Web log records. In addition, we introduced the Apriori Algorithm of Web usage pattern mining and its implementation process, developed the Apriori Algorithm program in Python development environment, then gave the performance of the Apriori Algorithm and realized the mining of Web user access pattern. The results have important theoretical significance for the application of the patterns in the development of teaching systems. The next research is to explore the improvement of the Apriori Algorithm in the distributed computing environment.

• The Journal of Society for e-Business Studies
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• v.22 no.2
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• pp.123-136
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• 2017

The evaluation and analysis method of information system (IS) is studied from the system perspective, the user perspective, and the management viewpoint. The detailed analysis method performs qualitative evaluation by user questionnaire or expert opinion. In this study, Measures the productivity and the effect of building administrative information systems. In the previous study, qualitative productivity and universal effect indicators were used, but in this study, quantitative productivity indicators and indicators specific to administrative complaints were selected. KONEPS, an administrative service system, used electronic contract records and information recorded in the intermediate process. The information was converted into the number of days, and the productivity based on the input manpower was calculated. The effect analysis analyzed the questionnaire related to civil affairs, which is the goal of the administrative work system. Each factor was divided into reflective structural variable and formal structural variable, and internal consistency and multi-collinearity were diagnosed. In order to verify the model, the influence of the work was set as a hypothesis, the reliability was verified according to the descriptive statistics method, the influence was measured through the regression analysis, and the model was analyzed by the multiple regression model path coefficient. Model validation methods are Chi-square (df, p), RMR, GFI, AGFI, NFI, CFI and GFI as indicators according to CFA.

• Knowledge Management Research
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• v.22 no.3
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• pp.273-293
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• 2021

Due to the COVID-19 pandemic, the size of the e-commerce has been increased rapidly. This pandemic, which made contact-less communication culture in everyday life made the e-commerce market to be opened even to the consumers who would hesitate to purchase and pay by electronic device without any personal contacts and seeing or touching the real products. Consumers who have experienced the easy access and convenience of the online purchase would continue to take those advantages even after the pandemic. During this time of transformation, however, the size of information source for the consumers has become even shrunk into a flat screen and limited to visual only. To provide differentiated and competitive information on products, companies are adopting AR/VR and steaming technologies but the reviews from the honest users need to be recognized as important in that it is regarded as strong as the well refined product information provided by marketing professionals of the company and companies may obtain useful insight for product development, marketing and sales strategies. Then from the consumer's point of view, if the ratings of reviews are widely diverged how consumers would process the review information before purchase? Are non-converged ratings always unreliable and worthless? In this study, we analyzed how consumer's regulatory focus moderate the attitude to process the diverged information. This experiment was designed as a 2x2 factorial study to see how the variance of product review ratings (high vs. low) for cosmetics affects product attitudes by the consumers' regulatory focus (prevention focus vs. improvement focus). As a result of the study, it was found that prevention-focused consumers showed high product attitude when the review variance was low, whereas promotion-focused consumers showed high product attitude when the review variance was high. With such a study, this thesis can explain that even if a product with exactly the same average rating, the converged or diverged review can be interpreted differently by customer's regulatory focus. This paper has a theoretical contribution to elucidate the mechanism of consumer's information process when the information is not converged. In practice, as reviews and sales records of each product are accumulated, as an one of applied knowledge management types with big data, companies may develop and provide even reinforced customer experience by providing personalized and optimized products and review information.

• Journal of Nutrition and Health
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• v.42 no.2
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• pp.119-127
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• 2009

Malnutrition of hospitalized patients can adversely affect clinical outcomes and cost. Several nutritional screening tools have been developed to identify patients with malnutrition risk. However, many of those possess practical pitfalls of requiring much time and labor to administer and may not be highly applicable to a Korean population. This study sought to develop and evaluate a Nutrition Risk Screening Tool (NRST) which is simple and quick to administer and widely applicable to Korean hospitalized patients with various diseases. The study was also designed to generate a screening tool predictable of various clinical outcomes and to validate it against the Nutritional Risk Screening 2002 (NRS 2002). Electronic medical records of 424 patients hospitalized at a general hospital in Seoul during a 14-month period were abstracted for anthropometric, medical, biochemical, and clinical outcome variables. The study employed a 4-step process consisting of selecting NRST components, searching a scoring scheme, validating against a reference tool, and confirming clinical outcome predictability. NRST components were selected by stepwise multiple regression analysis of each clinical outcome (i.e., hospitalization period, complication, disease progress, and death) on several readily available patient characteristics. Age and serum levels of albumin, hematocrit (Hct), and total lymphocyte count (TLC) remained in the last model for any of 4 dependent variables were decided as NRST components. Odds ratios of malnutrition risk based on NRS 2002 according to levels of the selected components were utilized to frame a scoring scheme of NRST. A NRST score higher than 3.5 was set as a cut-off score for malnutrition risk based on sensitivity and specificity levels against NRS 2002. Lastly differences in clinical outcomes by patients' NRST results were examined. The results showed that the NRST can significantly predict the in-hospital clinical outcomes. It is concluded that the NRST can be useful to simply and quickly screen patients at high-nutritional risk in relation to prospective clinical outcomes.