• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.23 no.2
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• pp.69-75
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• 2012

Objectives : The current study was conducted in order to investigate teachers' recognition of school bullying using a nationwide database of adolescents in middle and high school in Korea. Methods : Students in the 7th to 12th grades at 23 secondary schools participated in the current study during the fall of 2009. Subjects completed the self-report form of the Adolescent Mental Health and Problem Behavior Screening Questionnaire-II (AMPQ-II) and Symptom Checklist-90 Revised (SCL-90-R). In addition, relevant teachers used the teachers' rating scale of the AMPQ-II to report their students' status. Differences in the number of bullied students between teachers' recognition and students' report were explored. Results : A total of 2270 subjects provided relevant responses to the questionnaire. While the one-month prevalence of victimization according to students' self-reports was 28.9%, the recognized prevalence by teachers was only 10.6%. For prediction of the presence of school bullying according to students' self reports on the AMPQ-II, item 7 of the teachers' report on the AMPQ-II showed a sensitivity of 16%, a specificity of 92%, a positive predictability of 44%, a negative predictability of 72%, a false positive rate of 8%, a false negative rate of 84%, and an accuracy of 69%, respectively. No significant differences in subscores of students' self reports of the AMPQ-II and SCL-90-R were observed between bullied students who were recognized by teachers and those who were not recognized. In stepwise discriminant analysis, classification of teachers' item 2 and item 7 on the AMPQ-II with respect to school bullying according to students' reports showed an accuracy of 63.4%. Using this model, 75.2% of non-victimized subjects were classified correctly, while only 35.2% of victimized subjects were classified correctly. Conclusion : Despite the high prevalence in Korea, teachers' recognition of school violence among their students remains low. Pre-professional and continuing education to improve teachers' understanding of school bullying and knowledge of effective classroom-based prevention activities should be encouraged.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Cartoon and Animation Studies
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• s.29
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• pp.129-149
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• 2012

The purpose of this study was to investigate the clustering and Big Five Personality domains in discriminating groups by level of school-related adjustment, as experienced by Social Sims game users. Social Games are based on web that has simple rules to play in fictional time and space background. This paper is to analyze the relationships between social networks and user behaviors through the social games . In general, characteristics of social games are simple, fun and easy to play, popular to the public, and based on personal connections in reality. These features of social games make themselves different from video games with one player or MMORPG with many unspecific players. Especially Social Game show a noticeable characteristic related to social learning. The object of this research is to provide a possibility that game that its social perspective can be strengthened in social game environment and analyze whether it actually influences on problem solving of real life problems, therefore suggesting its direction of alternative play means and positive simulation game. Data was collected by administering 4 questionnaires (the short version of BFI, Satisfaction with life, Career Decision-.Making Self-.Efficacy, Depression) to the participants who were 20 people in Seoul and Daejeon. For the purposes of the data analysis, both Stepwise Discriminant analysis and Cluster analysis was employed. Neuroticism, Openness, Conscientiousness within the Big Five Personality domains were seen to be significant variables when it came to discriminating the groups. These findings indicated that the short version of the BFI may be useful in understanding for game user behaviors When it comes to cultural research, digital game takes up a significant role. We can see that from the fact that game, which has only been considered as a leisure activity or commercial means, is being actively research for its methodological, social role and function. Among digital game's several meanings, one of the most noticeable ones is the research on its critical, social participating function. According to Jame Paul gee, the most important merit of game is 'projected identity'. This means that experiences from various perspectives is possible.[1] In his recent autobiography , he described gamer as an active problem solver. In addition, Gonzalo Francesca also suggested an alternative game developing method through 'game that conveys critical messages by strengthening critical reasons'. [2] They all provided evidences showing game can be a strong academic tool. Not only does a genre called social game exist in the field of media and Social Network Game, but there are also some efforts to positively evaluate its value Through these kinds of researches, we can study how game can give positive influence along with the change in its general perception, which would eventually lead to spreading healthy game culture and enabling fresh life experience. This would better bring out the educative side of the game and become a social communicative tool. The object of this game is to provide a possibility that the social aspect can be strengthened within the game environment and analyze whether it actually influences the problem solving of real life problems. Therefore suggesting it's direction of alternative play means positive game simulation.

• Journal of Korean Public Health Nursing
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• v.4 no.1
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• pp.37-55
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• 1990

Every country in the world has been trying to expand the basic health right for the peoples as W.H.O has established the goal 'health for AU' by the year of 2000. Related to this goal, our government authority has establish the policy 'the insurance of health for all' into effect from July 198\). Recently hospitalshave been making a ceaseless effort for the plan for the rationalization of its management the academic World is making it a subject of discussion by doing the secure of manpower at a resonable level and the increase of productivity by the manpower. As a result of the efforts the study was established to secure the numbers of nursing manpower at a resonable level and use the unskilled persons at the utilizing field and seek the possible area of their activity for more efficient service through the investigation of ablity of simple nursing activities of regiestered nurses and nurse aides for rational function according to the educational levels and talents. The method of study was established by the registered nurses and nurse and nurse aides(R.N 229, N.A 226) who are working in 15 hospitals with over 200 beds. This surrey was conducted from Mar 29, 1989 to April 8, 1989. The method to test the degree of importance, difficulties, and the abillity of performance of a simple nursing activities was classified into 35 activities on the basis of references on this field. The degree of importance was composed from point l(Not so important) to 5(Very important). the degree of difficulties. was composed as follows; very easy - Point 1 very difficult and complicated - Point 5. and the ability of performance was composed from point 1 to 5. The materials gathered through the survey were analyzed with frequency, mean standard deviation, percentage. t-test, Anova, pearson's coefficient of correlation, stepwise multiple regression. factor analysis, discriminant analysis. The obtained results are summarized as follows: 1. The recognition values of the simple nursing activities of each group of registered nurse and nurse aides show; The degree of importance; 4.04 and 4.26 The degree of difficulties; 2.72 and 2.94 The ability of performance; 2.07 and 2.38 The brief summary shows there are little differences between who two groups the simple nursing activities turned out to be easy and simple work. 2. Regardless of the degree of importance, and difficulties, the ability of performance the important in fluencing of the degree of the simple nursing activities between the registered nurses and nurse aides was the order of educational level, hospital career, working career in wards and ages of the registered nurses and ages and hospital creer of nurse aides. The result was that the simple nursing activities could easily be familiar through the training of their working environment and period of experience. 3. Among the 35 simple nursing activities the items capable of resonable entrusting to the nurse aides are 5 that is helping bed-bathing, 8itz Bath, using bed pan, care while delivering patient, accompaying patient when visitor's check. There wasn't and differences between RN and nurse aides in performing the above 5 items. In anywhere. so we can say obviosuly that this nursing activities should be performed under the nursing system of which chief of nurse are supposed to supervise nurse aides as a possible function to be entrusted. In view of the above mentioned results, therefore, this partial functional job of the simple nursing activities can able be entrusted to the nurse aides through the regular training course. In case of these functional activities could be entrusted under, the responsibility of registered nurse, we can able suggest to for that there are the following advantages: 1.. In the nursing activities-affairs, the qualified guarantee of the nursing services can be kept and increased or promotoed with accommodation of the required nursing service and roles being expanded presently. 2. In the productivity of the hospital manpower, therefore, we have comt to view and consider in favourly that when an automational administration times would be come in the near future time to hospital affairs as a reality, to utilize the existing nures aides is better rather than investing so as to develop the other source manpowers or seek its for the efficient business management in the operational strategy or its policy.