• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.123-132
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• 2013

As the smartphones are equipped with various sensors such as the accelerometer, GPS, gravity sensor, gyros, ambient light sensor, proximity sensor, and so on, there have been many research works on making use of these sensors to create valuable applications. Human activity recognition is one such application that is motivated by various welfare applications such as the support for the elderly, measurement of calorie consumption, analysis of lifestyles, analysis of exercise patterns, and so on. One of the challenges faced when using the smartphone sensors for activity recognition is that the number of sensors used should be minimized to save the battery power. When the number of sensors used are restricted, it is difficult to realize a highly accurate activity recognizer or a classifier because it is hard to distinguish between subtly different activities relying on only limited information. The difficulty gets especially severe when the number of different activity classes to be distinguished is very large. In this paper, we show that a fairly accurate classifier can be built that can distinguish ten different activities by using only a single sensor data, i.e., the smartphone accelerometer data. The approach that we take to dealing with this ten-class problem is to use the ensemble of nested dichotomy (END) method that transforms a multi-class problem into multiple two-class problems. END builds a committee of binary classifiers in a nested fashion using a binary tree. At the root of the binary tree, the set of all the classes are split into two subsets of classes by using a binary classifier. At a child node of the tree, a subset of classes is again split into two smaller subsets by using another binary classifier. Continuing in this way, we can obtain a binary tree where each leaf node contains a single class. This binary tree can be viewed as a nested dichotomy that can make multi-class predictions. Depending on how a set of classes are split into two subsets at each node, the final tree that we obtain can be different. Since there can be some classes that are correlated, a particular tree may perform better than the others. However, we can hardly identify the best tree without deep domain knowledge. The END method copes with this problem by building multiple dichotomy trees randomly during learning, and then combining the predictions made by each tree during classification. The END method is generally known to perform well even when the base learner is unable to model complex decision boundaries As the base classifier at each node of the dichotomy, we have used another ensemble classifier called the random forest. A random forest is built by repeatedly generating a decision tree each time with a different random subset of features using a bootstrap sample. By combining bagging with random feature subset selection, a random forest enjoys the advantage of having more diverse ensemble members than a simple bagging. As an overall result, our ensemble of nested dichotomy can actually be seen as a committee of committees of decision trees that can deal with a multi-class problem with high accuracy. The ten classes of activities that we distinguish in this paper are 'Sitting', 'Standing', 'Walking', 'Running', 'Walking Uphill', 'Walking Downhill', 'Running Uphill', 'Running Downhill', 'Falling', and 'Hobbling'. The features used for classifying these activities include not only the magnitude of acceleration vector at each time point but also the maximum, the minimum, and the standard deviation of vector magnitude within a time window of the last 2 seconds, etc. For experiments to compare the performance of END with those of other methods, the accelerometer data has been collected at every 0.1 second for 2 minutes for each activity from 5 volunteers. Among these 5,900 ($=5{\times}(60{\times}2-2)/0.1$) data collected for each activity (the data for the first 2 seconds are trashed because they do not have time window data), 4,700 have been used for training and the rest for testing. Although 'Walking Uphill' is often confused with some other similar activities, END has been found to classify all of the ten activities with a fairly high accuracy of 98.4%. On the other hand, the accuracies achieved by a decision tree, a k-nearest neighbor, and a one-versus-rest support vector machine have been observed as 97.6%, 96.5%, and 97.6%, respectively.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.109-131
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• 2014

As the demand of nuclear power plant equipment is continuously growing worldwide, the importance of handling nuclear strategic materials is also increasing. While the number of cases submitted for the exports of nuclear-power commodity and technology is dramatically increasing, preadjudication (or prescreening to be simple) of strategic materials has been done so far by experts of a long-time experience and extensive field knowledge. However, there is severe shortage of experts in this domain, not to mention that it takes a long time to develop an expert. Because human experts must manually evaluate all the documents submitted for export permission, the current practice of nuclear material export is neither time-efficient nor cost-effective. Toward alleviating the problem of relying on costly human experts only, our research proposes a new system designed to help field experts make their decisions more effectively and efficiently. The proposed system is built upon case-based reasoning, which in essence extracts key features from the existing cases, compares the features with the features of a new case, and derives a solution for the new case by referencing similar cases and their solutions. Our research proposes a framework of case-based reasoning system, designs a case-based reasoning system for the control of nuclear material exports, and evaluates the performance of alternative keyword extraction methods (full automatic, full manual, and semi-automatic). A keyword extraction method is an essential component of the case-based reasoning system as it is used to extract key features of the cases. The full automatic method was conducted using TF-IDF, which is a widely used de facto standard method for representative keyword extraction in text mining. TF (Term Frequency) is based on the frequency count of the term within a document, showing how important the term is within a document while IDF (Inverted Document Frequency) is based on the infrequency of the term within a document set, showing how uniquely the term represents the document. The results show that the semi-automatic approach, which is based on the collaboration of machine and human, is the most effective solution regardless of whether the human is a field expert or a student who majors in nuclear engineering. Moreover, we propose a new approach of computing nuclear document similarity along with a new framework of document analysis. The proposed algorithm of nuclear document similarity considers both document-to-document similarity (${\alpha}$) and document-to-nuclear system similarity (${\beta}$), in order to derive the final score (${\gamma}$) for the decision of whether the presented case is of strategic material or not. The final score (${\gamma}$) represents a document similarity between the past cases and the new case. The score is induced by not only exploiting conventional TF-IDF, but utilizing a nuclear system similarity score, which takes the context of nuclear system domain into account. Finally, the system retrieves top-3 documents stored in the case base that are considered as the most similar cases with regard to the new case, and provides them with the degree of credibility. With this final score and the credibility score, it becomes easier for a user to see which documents in the case base are more worthy of looking up so that the user can make a proper decision with relatively lower cost. The evaluation of the system has been conducted by developing a prototype and testing with field data. The system workflows and outcomes have been verified by the field experts. This research is expected to contribute the growth of knowledge service industry by proposing a new system that can effectively reduce the burden of relying on costly human experts for the export control of nuclear materials and that can be considered as a meaningful example of knowledge service application.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• Journal of Applied Biological Chemistry
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• v.57 no.3
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• pp.267-277
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• 2014

Objective of this paper is to assess the available data on the pesticide uses and regulations in the golf courses, and provide the nationwide systematic management options. Numbers of golf courses in Korea are rapidly increasing from 2000s and reached at 421 sites by the end of 2011. Accordingly pesticide usage has been increased with years in direct proportion to the increasing number of golf courses. Amounts of pesticide applied in 2011 were 118,669 kg as of an active ingredient and were in the orders of fungicides (54.9%) > insecticides (24.4%) > herbicides (13.3%) > growth regulators (0.1%). Average pesticide usages in 2011 were 280.9 kg per golf course and $5.4kg\;ha^{-1}$. Frequencies of the residual pesticide detections in green and turf were higher than those in fairway and soil, respectively. Residue of highly toxic pesticides was not detected in golf courses. Ministry of Environment in 2010 has developed the 'golf course pesticide monitoring and management system' which is the advanced online registry for kind and amount of pesticides applied in each golf course. This system is intended for monitoring of the pesticide uses and residual levels and protecting the environmental pollution from pesticides in the golf course. In 2009, management of pesticides in the golf courses became the task of Ministry of Environment, being merged from many federal agency and ministries. The protocol for the site-specific best management practices, on which to base results from the risk assessment, should be set for pesticides in the golf to minimize the environmental impacts.

• Journal of The Korean Association For Science Education
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• v.36 no.1
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• pp.45-61
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• 2016

The purpose of this study is to explore students' epistemic goals and considerations in designing an experiment task and to investigate how a shift in the students' epistemology affected their argumentation. Four 7th grade students were selected as a focus group. According to the results, when they designed their own experiment, their epistemic goal was 'scientific sense-making' and their epistemic considerations - the perception of the nature of the knowledge product was 'this experiment should explain how something happened', the perception of the justification was 'we need to use our interpretation of the data' and the perception of the audience was 'constructor' - contributed to designing their experiment actively. When students tried to select one argument, their epistemic goal shifted to 'winning a debate', showing 'my experiment is better than the others' with the perception of the audience, 'competitor'. Consequently, students only deprecated the limits of different experiment so that they did not explore the meaning of each experiment design deeply. Eventually, student A's experiment design was selected due to time restrictions. When they elaborated upon their result, their epistemic goal shifted to 'scientific sensemaking', reviewing 'how this experiment design is scientifically valid' through scientific justification - we need justification to make members accept it - acting as 'cooperator'. Consequently, all members engaged in a productive argumentation that led to the development of the group result. This study lays the foundation for future work on understanding students' epistemic goals and considerations to prompt productive argumentation in science classrooms.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.683-690
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• 2009

The purpose of this study is to evaluate the usefulness of high-b-values diffusion weighted magnetic resonance imaging for the preoperative detection of focal rectum cancers. 60patients with diffusion weighted imaging were evaluated for the presence of rectal cancers. Forty were male and twenty were female, and their ages ranged from 38 to 71 (mean, 56) years. Used equipment was 1.5Tesla MRI((GE, General Electric Medical System, Excite HD). Examination protocols were used the fast spin echo T2, T1 weighted imaging. All examination protocols were performed by the same location with diffusion weighted imaging for accuracy detection. The b-values used in DWI were 250, 500, 750, 1000. 1500, 2000$(s/mm^2)$. The rectum, bladder to tumor contrast-to-noise ratio (CNR) of MR images were quantitativlely analyzed using GE software Functool tool, four experienced radiologists and three radiotechnologists qualitatively evaluated image quality in terms of image artifacts, lesion conspicuity and rectal wall. These data were analysed by using ANOVA and Freedman test with each b-value(p<0.05). Contrast to noise ratio of rectum, bladder and tumor in b-value 1000 were 27.21, 24.44, respectively(p<0.05) and aADC value was $0.73\times10^{-3}$. As a qualitative analysis, the conspicuity and discrimination from the rectal wall of lesions were high results as $4.0\pm0.14$, $4.4\pm0.16$ on b-value 1000(p<0.05), image artifacts were high results as $4.8\pm0.25$ on b-value 2000(p<0.05). In conclusion, DWI was provided useful information with depicting the pre-operative detection of rectal cancers, High-b-value 1000 image was the most excellent DWI value.

• Journal of Korean Home Economics Education Association
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• v.22 no.3
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• pp.1-18
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• 2010

The purpose of this study was to develop a teaching-learning process plan for sustainable residing creating with neighbors and to apply it to the housing section of Technology-Home Economics according to the 2007 Revised Curriculum. Teachinglearning method solving practical problems was used for the teaching-learning process plans of 6-session lessons according to the ADDIE model. In the development stage, 17 activity materials and 15 teaching learning materials (6 reading texts, 6 moving pictures, 2 internet and 1 image materials) were developed. for the 6-session lessons, based on the stages of solving practical problems. The plans applied to the 3 classes of 8, 9, and 10th grade of the H. junior and senior high school in Myun district in Kyungbook during Sept. 1st to 14th, 2009. The results showed that students actively participated when the contents and materials were related to their own experience. The 6-session lessons about sustainable residing creating with neighbors was significantly increased the sense of community between before and after. Each of the 4 stages of the teachinglearning method solving practical problems were highly participated by the students. The satisfaction with the contents and methods of the 6-session lessons were evaluated over medium to somewhat higher levels. The practical activities to solve the community space and programs were got positive comments. Problem solving process and presentation and discussion were needed to learn more. Those results might support that the teachinglearning process plan this research developed. would be appropriate to the lessons for sustainable residing creating with neighbors.

• Journal of Hospice and Palliative Care
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• v.10 no.3
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• pp.137-144
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• 2007

Purpose: The purpose of this study is to identify and assess the needs of the cancer patients and their families and provide basic data to meet with their needs. Methods: This is a descriptive study using questionnaire method. Questionnaire were collected by mail from 76 discharged patients from a hospice ward from May until the end of October, 2004, and data were analyzed by SPSS 10.0. Results: Admitted patients had needs of pain control (85.5%), non-pain symptoms (63.2%) such as vomiting, dyspnea, ascites, etc, and emotional and spiritual problem solving (28.9%, 14.5%). Interests of patients were health care of himself/herself (65.8%), concern for their spouses left alone (32.9%), and future of their children (15.8%). In families' needs of care of 5 areas, "information on patient's status and treatment/nursing care" was shown most high score ($3.48{\pm}0.62$). In detailed questions, they request most 'to inform the prognosis of patients' and the next is 'to inform the reasons that nursing care was required'. The next highest score was to 'inform family roles' ($3.39{\pm}0.64$), and next was spiritual support ($3.11{\pm}0.79$), and emotional support ($3.08{\pm}0.72$). Expectations of family on the treatment were comfortable dying (73.4%) scored the highest. Patients' families were satisfied with volunteer service most in service area (97.4%). The next was pain control (89.5%) and nursing service (77.6%). Conclusion: Health care staff should identify the actual needs of families caring cancer patients and they should operate realistic programme which can give continuous and assistance by reflecting individual needs and characteristics. With these srategies, the quality of life of patients and families can be improved. And then the intervention programme should be developed to measure subjective nursing care needs of terminally ill cancer patients and their families.

• Korean Journal of Health Education and Promotion
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• v.27 no.5
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• pp.79-89
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• 2010

Objectives: This study aims to identify health status and health risks among female marriage immigrants to Korea and to provide a basis for public health strategies to address their health issues. Methods: The participants of the study were 3,069 immigrant wives. The health examination was conducted by the Korea Association of Health Promotion (KAHP) in 2008. The participants also completed self-administered questionnaires on demographic characteristics, health-related behaviors and mental health. Results: Patterns of immigrant women's health problems differed by age and country of origin. Behavior patterns also differed by their heritage, age, and years of residence in Korea. Generally Vietnamese women fell in lower ranges of disease prevalence and health risk factors in the participant group and Japanese women presented most healthy eating habits. Filipina women showed relatively high disease prevalence than any other group. Conclusion: Immigration to Korea by marriage is relatively a new phenomenon, thus continuing surveillance and research are needed to identify health risks, behavior patterns, and their relationships. Interventions and policies for the health of migrant wives, their children and families are required.