• 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.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.91-121
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• 2008

Electronic commerce, commonly known as e-commerce or eCommerce, has become a major business trend in these days. The amount of trade conducted electronically has grown extraordinarily by developing the Internet technology. Most electronic commerce has being conducted between businesses to customers; therefore, the researches with respect to e-commerce are to find customer's needs, behaviors through statistical methods. However, the statistical researches, mostly based on a questionnaire, are the static researches, They can tell us the dynamic relationships between initial purchasing and repurchasing. Therefore, this study proposes dynamic research model for analyzing the cause of initial purchasing and repurchasing. This paper is based on the System-Dynamic theory, using the powerful simulation model with some restriction, The restrictions are based on the theory TAM(Technology Acceptance Model), PAM, and TPB(Theory of Planned Behavior). This article investigates not only the customer's purchasing and repurchasing behavior by passing of time but also the interactive effects to one another. This research model has six scenarios and three steps for analyzing customer behaviors. The first step is the research of purchasing situations. The second step is the research of repurchasing situations. Finally, the third step is to study the relationship between initial purchasing and repurchasing. The purpose of six scenarios is to find the customer's purchasing patterns according to the environmental changes. We set six variables in these scenarios by (1) changing the number of products; (2) changing the number of contents in on-line shopping malls; (3) having multimedia files or not in the shopping mall web sites; (4) grading on-line communities; (5) changing the qualities of products; (6) changing the customer's degree of confidence on products. First three variables are applied to study customer's purchasing behavior, and the other variables are applied to repurchasing behavior study. Through the simulation study, this paper presents some inter-relational result about customer purchasing behaviors, For example, Active community actions are not the increasing factor of purchasing but the increasing factor of word of mouth effect, Additionally. The higher products' quality, the more word of mouth effects increase. The number of products and contents on the web sites have same influence on people's buying behaviors. All simulation methods in this paper is not only display the result of each scenario but also find how to affect each other. Hence, electronic commerce firm can make more realistic marketing strategy about consumer behavior through this dynamic simulation research. Moreover, dynamic analysis method can predict the results which help the decision of marketing strategy by using the time-line graph. Consequently, this dynamic simulation analysis could be a useful research model to make firm's competitive advantage. However, this simulation model needs more further study. With respect to reality, this simulation model has some limitations. There are some missing factors which affect customer's buying behaviors in this model. The first missing factor is the customer's degree of recognition of brands. The second factor is the degree of customer satisfaction. The third factor is the power of word of mouth in the specific region. Generally, word of mouth affects significantly on a region's culture, even people's buying behaviors. The last missing factor is the user interface environment in the internet or other on-line shopping tools. In order to get more realistic result, these factors might be essential matters to make better research in the future studies.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• Culinary science and hospitality research
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• v.9 no.4
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• pp.163-178
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• 2003

The purpose of this study is to find the effect of restaurant brand image upon service quality, price acceptability, and revisit intention, and to propose the importance of brand image to operators and managers who manage restaurants. To accomplish the purpose of this study, sampling was taken among customers who visit six deluxe hotels and six family restaurants in Seoul. Six hundreds questionnaires were distributed to each hotel and restaurant and 487 valid samples were selected for statistical analysis. The questionnaire consists of 77 items about demographical characteristics, brand image, service quality, revisit intention, price acceptability, and spending patterns. SPSS WIN 10.0 was used for statistical analysis. A research model was built up and three null hypotheses were established. Based on theses research model and three null hypotheses, the test was conducted, and the results are as follows. Brand image has an effect upon service quality, and furthermore this can be preceding variable of service quality. Also Service quality has an effect upon price acceptability and revisit intention.

• Journal of the Korean earth science society
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• v.38 no.7
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• pp.581-597
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• 2017

In this study, we conducted a statistical analysis to see how visitors were satisfied through experiences at the Goseong Dinosaur Museum, which is a representative natural history museum in the Republic of Korea. As a result, during the last 10 years (2005-2014) the total number of visitors to the Goseong Dinosaur Museum was more than 3,410,000 persons. The maximum- and minimum number of visitors per year was more than 440,000 and 300,000 persons, respectively. The annual average number was more than ca. 340,000 persons. Among the visitors, the number of individual visitors was more than 2,800,000 persons (82.0%) and the number of group visitors was about 610,000 persons (18.0%). As a result of the monthly visitor analysis, the maximum number of visitors was about 530,000 persons in August while the minimum number of visitors was about 140,000 persons in February. The visiting patterns of the individual and group visitors were different. There were the largest number of the individual visitors in August and the smallest number of them in December, whereas the largest number of the group visitors in October and the smallest number of them in February. The visitor's residence was generally proportional to the geographical accessibility and the number of people in their residence. The results showed that the degree of visitor's satisfaction using Likert scale was relatively high with the score of 4.1. However, the visitors recommended that some facilities should be improved. Regarding the number of visits and the intention of revisit, 102 persons (53.1%) of 192 made a visit to the museum more than two times, and 178 persons (89.9%) of 198 visitors would like to visit the museum again. It is recommended that the results of this study be used in developing a long term-plan or for the Goseong Dinosaur Museum.

• Journal of Korean Society of Forest Science
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• v.106 no.3
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• pp.310-319
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• 2017

This study categorized the damaged trees by Supervised Classification using time-series-aerial photographs of Bukhan, Cheonggae and Suri mountains because oak wilt disease seemed to be concentrated in the metropolitan regions. In order to analyze the spatial characteristics of the damaged areas, the geographical characteristics such as elevation and slope were statistically analyzed to confirm their strong correlation. Based on the results from the statistical analysis of Moran's I, we have retrieved the following: (i) the value of Moran's I in Bukhan mountain is estimated to be 0.25, 0.32, and 0.24 in 2009, 2010 and 2012, respectively. (ii) the value of Moran's I in Cheonggye mountain estimated to be 0.26, 0.32 and 0.22 in 2010, 2012 and 2014, respectively and (iii) the value of Moran's I in Suri mountain estimated to be 0.42 and 0.42 in 2012 and 2014. respectively. These numbers suggest that the damaged trees are distributed in clusters. In addition, we conducted hotspot analysis to identify how the damaged tree clusters shift over time and we were able to verify that hotspots move in time series. According to our research outcome from the analysis of the entire hotspot areas (z-score>1.65), there were 80 percent probability of oak wilt disease occurring in the broadleaf or mixed-stand forests with elevation of 200~400 m and slope of 20~40 degrees. This result indicates that oak wilt disease hotspots can occur or shift into areas with the above geographical features or forest conditions. Therefore, this research outcome can be used as a basic resource when predicting the oak wilt disease spread-patterns, and it can also prevent disease and insect pest related harms to assist the policy makers to better implement the necessary solutions.

• The korean journal of orthodontics
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• v.38 no.4
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• pp.275-282
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• 2008

Objective: The purpose of this study was to investigate the effect of using laser for ceramic bracket bonding of porcelain surfaces and to compare it with conventional treatment of porcelain surfaces. Methods: Ninety feldspathic porcelain specimens were divided into 9 groups of 10, with each group having different surface treatments performed. Surface treatment groups were orthophosphoric acid, orthophosphoric acid with silane, hydrofluoric acid, hydrofluoric acid with silane, sandblasted, sandblasted with silane, laser etched, laser etched with silane, and glazed surface served as a control group. In the laser etched groups, the specimens were irradiated with 2-watt superpulse carbon dioxide ($CO_2$) laser for 20 seconds. Ceramic brackets were bonded with light-cure composite resin and all specimens were stored in water at $37^{\circ}C$ for 24 hours. Shear bond strength was determined in megapascals (MPa) by shear test at 1 mm/minute crosshead speed and the failure pattern was assessed. For statistical analysis, one-way ANOVA and tukey test were used. Results: Statistical analysis showed significant differences between the groups. The HFA + S group showed the highest mean shear bond strength ($13.92{\pm}1.94\;MPa$). This was followed by SB + S ($10.16\;{\pm}\;1.27\;MPa$), HFA ($10.09\;{\pm}\;1.07\;MPa$), L + S ($8.25\;{\pm}\;1.24\;MPa$), L ($7.86\;{\pm}\;0.96\;MPa$), OFA + S ($7.22\;{\pm}\;1.09\;MPa$), SB ($3.41\;{\pm}\;0.37\;MPa$), OFA ($2.81\;{\pm}\;0.37\;MPa$), G ($2.46\;{\pm}\;1.36\;MPa$), Bond failure patterns of HFA and silane groups, except L + S, were cohesive modes in porcelain while adhesive failure was observed in the control group and the rest of the groups. Conclusions : A 2-watt superpulse $CO_2$ laser etching of porcelain surfaces can provide a satisfactory result for porcelain surface treatment for ceramic bracket bonding. Laser irradiation may be an alternative conditioning method for the treatment of porcelain surfaces.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.6
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• pp.530-537
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• 2007

Purpose: The aim of this study is to assess the specific patterns of regional cerebral blood flow (rCBF) in patients with the early stage of subcortical vascular dementia (SVaD) and Alzheimer's disease (AD) using Tc-99m HMPAO SPECT, and to compare the differences between the two conditions. Materials and Methods: Sixteen SVaD, 46 AD and 12 control subjects participated in this study. We included the patients with SVaD and AD according to NINCDS-ADRDA and NINDS-AIREN criteria. They were all matched for age, education and clinical dementia rating scores. Three groups were evaluated by Tc-99m HMPAO SPECT using statistical parametric mapping (SPM) for measuring rCBF. The SPECT data of patients with SVaD and AD were compared with those of normal control subjects and then compared with each other. Results: SPM analysis of the SPECT image showed significant perfusion deficits on the right temporal region and thalamus, left insula and superior temporal gyrus, both cingulate gyri and frontal subgyri in patients with SVaD and on the left supramarginal gyrus, superior temporal gyrus, postcentral gyrus and inferior parietal lobule, right fugiform gyrus and both cingulate gyri in AD compared with control subjects (uncorrected p<0.01). SVaD patients revealed significant hypoperfusion in the right parahippocampal gyrus with cingulated gyrus, left insula and both frontal subgyral regions compared with AD (uncorrected p<0.01). Conclusion: Our study shows characteristic and different pattern of perfusion deficits in patients with SVaD and AD, and these results may be helpful to discriminate the two conditions in the early stage of illness.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.664-674
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• 2021

The relevance of this scientific research is determined by the negative impact of the COVID-19 pandemic on the current trends and dynamics of world tourism development. This article aims to identify patterns of development of the modern tourist market, analysis of problems and prospects of development in the context of the COVID-19 pandemic. Materials and methods. General scientific methods and methods of research are used in the work: analysis, synthesis, comparison, analysis of statistical data. The analysis of the viewpoints of foreign and domestic authors on the research of the international tourist market allowed us to substantiate the actual directions of tourism development due to the influence of negative factors connected with the spread of a new coronavirus infection COVID-19. Economic-statistical, abstract-logical, and economic-mathematical methods of research were used during the process of study and data processing. Results. The analysis of the current state of the tourist market by world regions was carried out. It was found that tourism is one of the most affected sectors from COVID-19, as, by the end of 2020, the total number of tourist arrivals in the world decreased by 74% compared to the same period in 2019. The consequence of this decline was a loss of total global tourism revenues by the end of 2020, which equaled $1.3 trillion. 27% of all destinations are completely closed to international tourism. At the end of 2020, the economy of international tourism has shrunk by about 80%. In 2020 the world traveled 98 million fewer people (-83%) relative to the same period last year. Tourism was hit hardest by the pandemic in the Asia-Pacific region, where travel restrictions are as strict as possible. International arrivals in this region fell by 84% (300 million). The Middle East and Africa recorded declines of 75 and 70 percent. Despite a small and short-lived recovery in the summer of 2020, Europe lost 71% of the tourist flow, with the European continent recording the largest drop in absolute terms compared with 2019, 500 million. In North and South America, foreign arrivals declined. It is revealed that a significant decrease in tourist flows leads to a massive loss of jobs, a sharp decline in foreign exchange earnings and taxes, which limits the ability of states to support the tourism industry. Three possible scenarios of exit of the tourist industry from the crisis, reflecting the most probable changes of monthly tourist flows, are considered. The characteristics of respondents from Ukraine, Germany, and the USA and their attitude to travel depending on gender, age, education level, professional status, and monthly income are presented. About 57% of respondents from Ukraine, Poland, and the United States were planning a tourist trip in 2021. Note that people with higher or secondary education were more willing to plan such a trip. The results of the empirical study confirm that interest in domestic tourism has increased significantly in 2021. The regression model of dependence of the number of domestic tourist trips on the example of Ukraine with time tendency (t) and seasonal variations (Turˆt = 7288,498 - 20,58t - 410,88∑5) it forecast for 2020, which allows stabilizing the process of tourist trips after the pandemic to use this model to forecast for any country. Discussion. We should emphasize the seriousness of the COVID-19 pandemic and the fact that many experts and scientists believe in the long-term recovery of the tourism industry. In our opinion, the governments of the countries need to refocus on domestic tourism and deal with infrastructure development, search for new niches, formats, formation of new package deals in new - domestic - segment (new products' development (tourist routes, exhibitions, sightseeing programs, special rehabilitation programs after COVID) -19 in sanatoriums, etc.); creation of individual offers for different target audiences). Conclusions. Thus, the identified trends are associated with a decrease in the number of tourist flows, the negative impact of the pandemic on employment and income from tourism activities. International tourism needs two to four years before it returns to the level of 2019.

• The Journal of Korean Academy of Prosthodontics
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• v.60 no.2
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• pp.135-142
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• 2022

Purpose. This study aims to evaluate the combined effect of reduced thickness in different regions on the fracture resistance of monolithic zirconia crowns. Materials and methods. Seven nickel-chromium dies were generated from a 3D model of mandibular first molar using the digital scanner with the following geometries: 1.5 mm occlusal reduction, 1.0 mm deep chamfer. Based on the abutment model, Zirconia blocks (Luxen Zirconia) were selected to fabricate Sixty-three zirconia crowns with occlusal thicknesses of 0.3 mm, 0.5 mm, and 1.5 mm, and different axial thicknesses of 0.3 mm, 0.5 mm, and 1.0 mm. All crowns were cemented by resin cement. Next, the crowns were subjected to load-to-fracture test until fracture using an electronic universal testing machine. In addition, fracture patterns were observed with a scanning electron microscope (SEM). Two-way ANOVA and the Tuckey HSD test for post hoc analysis were used for statistical analysis (P < .05). Results. The mean values of fracture resistancerecorded was higher than the average biting force in the posterior region. The two-way ANOVA showed that the occlusal and axial thickness affected the fracture resistance significantly (P < .05). However, the effect of axial thickness on fracture resistance did not show a statistical difference when thicker than 0.5 mm. The observed failure modes were partial or complete fracture depending on the severity of crack propagation. Conclusion. Within the limitations of the present study, the CAD-CAM monolithic zirconia crown with extremely reduced thickness showed adequate fracture resistance to withstand occlusal load in molar regions. In addition, both occlusal and axial thickness affected the fracture resistance of the zirconia crown and showed different results as combined.