• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 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 Wetlands Research
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• v.16 no.1
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• pp.19-32
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• 2014

The purpose of this paper is to obtain reliable rainfall data for runoff simulation and other hydrological analysis by the calibration of gauge rainfall. The calibrated gauge rainfall could be close to the actual value with rainfall on the ground. In order to analyze the wind effect of ground rain gauge, we selected the rain gauge sites with and without a windshield and standard rain gauge data from Chupungryeong weather station installed by standard of WMO. Simple linear regression model and artificial neural networks were used for the calibration of rainfalls, and we verified the reliability of the calibrated rainfalls through the runoff analysis using $Vflo^{TM}$. Rainfall calibrated by linear regression is higher amount of rainfall in 5%~18% than actual rainfall, and the wind remarkably affects the rainfall amount in the range of wind speed of 1.6~3.3m/s. It is hard to apply the linear regression model over 5.5m/s wind speed, because there is an insufficient wind speed data over 5.5m/s and there are also some outliers. On the other hand, rainfall calibrated by neural networks is estimated lower rainfall amount in 10~20% than actual rainfall. The results of the statistical evaluations are that neural networks model is more suitable for relatively big standard deviation and average rainfall. However, the linear regression model shows more suitable for extreme values. For getting more reliable rainfall data, we may need to select the suitable model for rainfall calibration. We expect the reliable hydrologic analysis could be performed by applying the calibration method suggested in this research.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.239-250
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• 2020

The purpose of this paper is to explore the influence of the "contextualization" effect of web celebrity live broadcast on the e-commerce platform on consumers' perception of product value, risk and purchase intention. Live in this paper, using Taobao shopping consumers as the research object, the survey method, questionnaire survey is adopted, the form through the questionnaire and distributed network, a live in order to further validation of web celebrity effect of contextualized actual influence on consumer purchase intention, questionnaire design the Likert scale, seven and recycling questionnaire analysis using the statistical software SPSS 23.0 and AMOS 22.0 after processing the data. After determining the reliability and validity of the questionnaire, the exploratory factor analysis was used to verify the hypothesis and calculate the actual adjustment degree of the "contextualization" effect of web celebrity live broadcasting on consumers' purchase intention. The research results of this paper are summarized as follows :(1) consumers' perceived value of products can significantly positively affect their purchase intention, while perceived risk has a significantly negative impact on their purchase intention; (2) consumers' trust and purchase intention to products are regulated by the "contextualization" of web celebrity live broadcast. Specifically, for web celebrity live broadcasting with good "contextualization" effect, the perceived value of consumer products has a positive impact on product trust, which is higher than that of web celebrity live broadcasting with poor "contextualization" effect. In terms of resolving consumers' perceived risks to products, web celebrity live broadcast with good "contextualization" effect is also significantly better than web celebrity live broadcast with poor "contextualization" effect. Based on empirical analysis, this paper concludes that web celebrity live broadcasting will become a new breakthrough for the sustainable growth of the e-commerce industry, and puts forward Suggestions on the e-commerce marketing mode and the transformation of web celebrity live broadcasting industry.

• Journal of Nutrition and Health
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• v.56 no.5
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• pp.573-588
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• 2023

Purpose: This study aimed to examine social networking service (SNS) use and recommended information utilization (SURU) according to the food-related lifestyles (FRLs) of consumers and analyze how the interaction between the FRL and SURU affects the practice of eating alone and visiting restaurants. Methods: Data on 4,624 adults in their 20s to 50s were collected from the 2021 Consumer Behavior Survey for Food. Statistical methods included factor analysis, K-means cluster analysis, the complex samples general linear model, the complex samples Rao-Scott χ² test, and the general linear model. Results: The following three factors were extracted from the FRL data: Convenience pursuit, rational consumption pursuit, and gastronomy pursuit, and the subjects were classified into three groups, namely the rational consumption, convenient gastronomy, and smart gourmet groups. An examination of the difference in SURU according to the FRL showed that the smart gourmet group had the highest score. The result of analyzing the effects of the FRL and SURU on eating alone revealed that both the main effect and the interaction effect were significant (p < 0.01, p < 0.001). The higher the SURU, the higher the frequency of eating alone in the convenience pursuit, and gastronomy pursuit groups. The main and interaction effects of the FRL and SURU on the frequency of eating out were also significant (p < 0.01, p < 0.001). In all the FRL groups, the higher the SURU level, the higher the frequency of visiting restaurants. Specifically, the two groups with convenience and gastronomic tendencies showed a steeper increase. Conclusion: This study provides important basic data for research on consumer behavior related to food SNS, market segmentation of restaurant consumers, and development of marketing strategies using SNS in the future.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.185-202
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• 2012

.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold ${\varepsilon}$) to the model prediction. Using SVR, we tried to build a model that can measure the level of arousal and valence from the facial features. To validate the usefulness of the proposed model, we collected the data of facial reactions when providing appropriate visual stimulating contents, and extracted the features from the data. Next, the steps of the preprocessing were taken to choose statistically significant variables. In total, 297 cases were used for the experiment. As the comparative models, we also applied MRA and ANN to the same data set. For SVR, we adopted '${\varepsilon}$-insensitive loss function', and 'grid search' technique to find the optimal values of the parameters like C, d, ${\sigma}^2$, and ${\varepsilon}$. In the case of ANN, we adopted a standard three-layer backpropagation network, which has a single hidden layer. The learning rate and momentum rate of ANN were set to 10%, and we used sigmoid function as the transfer function of hidden and output nodes. We performed the experiments repeatedly by varying the number of nodes in the hidden layer to n/2, n, 3n/2, and 2n, where n is the number of the input variables. The stopping condition for ANN was set to 50,000 learning events. And, we used MAE (Mean Absolute Error) as the measure for performance comparison. From the experiment, we found that SVR achieved the highest prediction accuracy for the hold-out data set compared to MRA and ANN. Regardless of the target variables (the level of arousal, or the level of positive / negative valence), SVR showed the best performance for the hold-out data set. ANN also outperformed MRA, however, it showed the considerably lower prediction accuracy than SVR for both target variables. The findings of our research are expected to be useful to the researchers or practitioners who are willing to build the models for recognizing human emotions.

• The Journal of Korean Academy of Prosthodontics
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• v.54 no.3
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• pp.193-202
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• 2016

Purpose: This study was performed in order to assess the effect of the surface treatment methods and the use of bonding agent on the shear bond strength (SBS) between the aged CAD-CAM (computer aided design-computer aided manufacturing) hybrid materials and added composite resin. Materials and methods: LAVA Ultimate (LU) and VITA ENAMIC (VE) specimens were age treated by submerging in a $37^{\circ}C$ water bath filled with artificial saliva (Xerova solution) for 30 days. The surface was ground with #220 SiC paper then the specimens were divided into 9 groups according to the combination of the surface treatment (no treatment, grinding, air abrasion with aluminum oxide, HF acid) and bonding agents (no bonding, Adper Single Bond 2, Single Bond Universal). Each group had 10 specimens. Specimens were repaired (added) using composite resin (Filtek Z250), then all the specimens were stored for 7 days in room temperature distilled water. SBS was measured and the fractured surfaces were observed with a scanning electron microscope (SEM). One-way ANOVA and Scheffe test were used for statistical analysis (${\alpha}=.05$). Results: Mostly groups with bonding agent treatment showed higher SBS than groups without bonding agent. Among the groups without bonding agent the groups with aluminum oxide treatment showed higher SBS. However there was no significant difference between groups except two subgroups within LU group, which revealed a significant increase of SBS when Single Bond Universal was used on the ground LU specimen. Conclusion: The use of bonding agent when repairing an aged LAVA Ultimate restoration is recommended.

• Journal of Soil and Groundwater Environment
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• v.12 no.5
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• pp.39-53
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• 2007

Generally, fractured medium can be described with some key parameters, such as hydraulic conductivities or random field of hydraulic conductivities (continuum model), spatial and statistical distribution of permeable fractures (discrete fracture network model). Investigating the practical applicability of the well-known conceptual models for the description of groundwater flow in fractured media, various types of hydraulic tests were applied to studies on the highly fractured media in Geumsan, Korea. Results from single-hole packer test show that the horizontal hydraulic conductivities in the permeable media are between $7.67{\times}10^{-10}{\sim}3.16{\times}10^{-6}$ m/sec, with $7.70{\times}10^{-7}$ m/sec arithmetic mean and $2.16{\times}10^{-7}$ m/sec geometric mean. Total number of test interval is 110 at 8 holes. The number of completely impermeable interval is 9, and the low permeable interval - below $1.0{\times}10^{-8}$ m/sec is 14. In other words, most of test intervals are permeable. The vertical distribution of hydraulic conductivities shows apparently the good correlation with the results of flowmeter test. But the results from the cross-hole test show some different features. The results from the cross-hole test are highly related to the connectivity and/or the binary properties of fractured media; permeable and impermeable. From the viewpoint of the connection, the application of the general stochastic approach with a single continuum model may not be appropriate even in the moderately or highly permeable fractured medium. Then, further studies on the investigation method and the analysis procedures should be required for the reasonable and practical design of the conceptual model, with which the binary properties, including permeable/impermeable features, can be described.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Journal of the Mineralogical Society of Korea
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• v.22 no.4
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• pp.313-324
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• 2009

We explore the effect of particle shape and size on 3-dimensional (3D) network and pore structure of porous earth materials composed of glass beads and silica gel using NMR micro-imaging in order to gain better insights into relationship between structure and the corresponding hydrologic and seismological properties. The 3D micro-imaging data for the model porous networks show that the specific surface area, porosity, and permeability range from 2.5 to $9.6\;mm^2/mm^3$, from 0.21 to 0.38, and from 11.6 to 892.3 D (Darcy), respectively, which are typical values for unconsolidated sands. The relationships among specific surface area, porosity, and permeability of the porous media are relatively well explained with the Kozeny equation. Cube counting fractal dimension analysis shows that fractal dimension increases from ~2.5－2.6 to 3.0 with increasing specific surface area from 2.5 to $9.6\;mm^2/mm^3$, with the data also suggesting the effect of porosity. Specific surface area, porosity, permeability, and cube counting fractal dimension for the natural mongolian sandstone are $0.33\;mm^2/mm^3$, 0.017, 30.9 mD, and 1.59, respectively. The current results highlight that NMR micro-imaging, together with detailed statistical analyses can be useful to characterize 3D pore structures of various porous earth materials and be potentially effective in accounting for transport properties and seismic wave velocity and attenuation of diverse porous media in earth crust and interiors.

• Korean Journal of Psychosomatic Medicine
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• v.23 no.1
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• pp.36-46
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• 2015

Objectives: To understand the risk factors of demographic data in geriatric depression scale, and suicidality among in elderly who live alone at one urban region. Methods:In 2009, 589 elderly who live alone(age${\geq}$65) were carried out a survey about several socio-demographic data, Korean version of the Geriatric Depression Scale(SGDS-K) and Suicidal Ideation Questionnaire (SIQ). Statistical analysis was performed for the collected data. Results: Mean age of elderly who live alone is 75.69(SD 6.17). 40.1% of participants uneducated, 31.4% graduate from elementary school, 12.9% graduate from high school, 11.7% graduate from middle school, 3.2% graduate from university. Religionless, having past history of depression or physical diseases, low subjective satisfaction of family situation, and not having any social group activity have significance to depressive symptoms of elderly who live alone. Having past history of depression, religionless, low subjective satisfaction of family situation have significance to suicidality. Especially, low subjective satisfaction of family situation and having past history of depression are powerful demographic factor both depressive symptoms and suicidality of elderly who live alone. Conclusions: When we take care elderly who live alone, we should consider many things, but especially the social support network such as family satisfaction and past history of depression for reducing or preventing their depression and suicide both elderly depression and suicide who live alone.