• The Korean Journal of Food & Health Convergence
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• v.5 no.1
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• pp.1-11
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• 2019

Smoking has become one of the public health harzard affecting the world. In the UK, smoking is responsible for around one in five deaths. The illnesses caused by smoking extend beyond the well-reported links with cancer, heart disease and respiratory illnesses. Hence the research to determine the awareness of the predisposing factor to smoking among adults in sokoto metropolis. A cross-sectional form of descriptive survey research design was used for this study. This is because descriptive studies are used when the characteristics of a population are either unknown or partially known (Hennekens & Buring, 2007), and it was used by Ganley and Rosario (2013) in a related research this justified the use of similar design in a study of similar nature.Two hundred and seventy returned questionnaire was collected, analyzed using descriptive statistic of frequency count, normative percentage and grand mean; as well as inferential statistics of chi-square (${\chi}^2$). The level of significant was fixed at 0.05. Appropriate degrees of freedom were worked out. There was statistical significant influence or relationship with marital status on the predisposing factors of smoking chi-square of 19716.516 greater than the critical value 43.77297at df 30 p<0.05. There were statistical significance chi-square =27468.348 which is greater than the critical value 43.77297 at df= 30. These show that there is a relationship on gender awareness of predisposing factors to smoking rejecting the null hypotheses. The respondents across different lever/year higher institution shows that the awareness of predisposing factors of smoking there were a statistical significance difference chi-square =7168.429 (df=88) greater than critical value 102.342 rejecting the null hypotheses. There is consistent evidence that links exposure to depictions of smoking in movies and initiation of smoking in young people. Over the years television shows and films have effectively built up associations between smoking and glamour, sex and risk-taking. Social learning theory describes how we learn by example from others. We are strongly influenced by our parents, and other people we look up to, such as peers, actors and pop stars. This can lead us to emulate their behaviour and try smoking.

• Cartoon and Animation Studies
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• s.50
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• pp.239-273
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• 2018

There have been many topics in gamer research on gamers' game addiction, education, and psychological interest. This paper investigates how to perceive the virtual environment of gamers based on James Gibson 's theories of cognitive science. Gibson's theory is not a stimulus input through individual sensory receptors, but rather a learning process such as establishing a cognitive relationship between perceptual systems, external invariant property separation, behavioral learning, invariant property separation of events, selectiveism development. Based on this analysis tool, I collected and verified gamers' perception of game environment of by FGI survey method. The results of the analysis showed that Gibson 's perceptual learning process was perceived as a virtual environment as in reality, and there was also perceptual difference found only in games. Patterned perception develops in the direction of classifying invariant properties appearing in the game based on the purpose of the game. In this study, it can be seen as a result of the research that FGI interview can be summarized as patterning (typification) perception process based on the goal consciousness of gamers. But,The results of the study suggest that the psychological analysis of the gamer can not be presented by the FGI results alone. In the future, we need a model study to confirm the causality and the verification through statistical analysis.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1229-1244
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• 2017

In recent years, as demand for data-based analytical methodologies increases in various fields, optimization methods have been developed to handle them. In particular, various constraints required for problems in statistics and machine learning can be solved by convex optimization. Alternating direction method of multipliers (ADMM) can effectively deal with linear constraints, and it can be effectively used as a parallel optimization algorithm. ADMM is an approximation algorithm that solves complex original problems by dividing and combining the partial problems that are easier to optimize than original problems. It is useful for optimizing non-smooth or composite objective functions. It is widely used in statistical and machine learning because it can systematically construct algorithms based on dual theory and proximal operator. In this paper, we will examine applications of ADMM algorithm in various fields related to statistics, and focus on two major points: (1) splitting strategy of objective function, and (2) role of the proximal operator in explaining the Lagrangian method and its dual problem. In this case, we introduce methodologies that utilize regularization. Simulation results are presented to demonstrate effectiveness of the lasso.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.42-49
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• 2018

The purpose of this study is to investigate the effects of team - based simulation training in nursing students on critical thinking and communication clarity. This study was conducted from October 2017 to November 2017 for 69 students (33 experimental group and 36 control group) who took a course of "Basic Nursing Theory and Practice" students in a major nursing student in P city, Gyeongbuk province. Collection and analysis. In this study, we conducted a questionnaire survey using a tool of critical accidents measurement by Yoon Jin(2004) and a communication clarity tool by Hye - jin Jo(2013). Statistical analysis was performed using SPSS 23.0, Chi-square, Fisher's exact test, t-test and ANCOVA. Data analysis showed that the groups participating in the SBAR team based simulation training were significantly more effective than the control group in critical thinking (F = 11.91, p <.001) and communication clarity (F = 4.40, p = .040). Based on these results, it is shown that using SBAR team - based simulation learning for nursing students is effective in teaching 'fundamental nursing and practice' and can be recommended as teaching method for nursing students.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.4 s.38
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• pp.71-77
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• 2006

Support Vector Machine (SVM) which has roots in a statistical learning theory is a training algorithm based on structural risk minimization. Generally, SVM algorithm uses the kernel for determining a linearly non-separable boundary and classifying the data. But, classical kernels can not apply to effectively the hyperspectral image classification because it measures similarity using vector's dot-product or euclidian distance. So, This paper proposes the spectral similarity kernel to solve this problem. The spectral similariy kernel that calculate both vector's euclidian and angle distance is a local kernel, it can effectively consider a reflectance property of hyperspectral image. For validating our algorithm, SVM which used polynomial kernel, RBF kernel and proposed kernel was applied to land cover classification in Hyperion image. It appears that SVM classifier using spectral similarity kernel has the most outstanding result in qualitative and spatial estimation.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1260-1270
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• 2005

Support Vector Machines (SVM) is well known for a representative learner as one of the kernel methods. SVM which is based on the statistical learning theory shows good generalization performance and has been applied to various pattern recognition problems. However, SVM is basically to deal with a two-class classification problem, so we cannot solve directly a multi-class problem with a binary SVM. One-Per-Class (OPC) and All-Pairs have been applied to solve the face recognition problem, which is one of the multi-class problems, with SVM. The two methods above are ones of the output coding methods, a general approach for solving multi-class problem with multiple binary classifiers, which decomposes a complex multi-class problem into a set of binary problems and then reconstructs the outputs of binary classifiers for each binary problem. In this paper, we introduce the output coding methods as an approach for extending binary SVM to multi-class SVM and propose new output coding schemes based on the Error-Correcting Output Codes (ECOC) which is a dominant theoretical foundation of the output coding methods. From the experiment on the face recognition, we give empirical results on the properties of output coding methods including our proposed ones.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.22-28
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• 2010

Adaboost is well known for a representative learner as one of the kernel methods. Adaboost which is based on the statistical learning theory shows good generalization performance and has been applied to various pattern recognition problems. However, Adaboost is basically to deal with a two-class classification problem, so we cannot solve directly a multi-class problem with Adaboost. One-Vs-All and Pair-Wise have been applied to solve the multi-class classification problem, which is one of the multi-class problems. The two methods above are ones of the output coding methods, a general approach for solving multi-class problem with multiple binary classifiers, which decomposes a complex multi-class problem into a set of binary problems and then reconstructs the outputs of binary classifiers for each binary problem. However, two methods cannot show good performance. In this paper, we propose the method to solve a multi-target classification problem by using radial basis function of Adaboost weak classifier.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.157-164
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• 2020

In 2020, due to COVID-19, all universities in Korea were conducting non-face-to-face classes. The purpose of this study is to study what factors affect academic achievement under such non-face-to-face instruction, especially for engineering students where practical training is important. Validity of the statistical hypothesis defined in this study by applying a structural equation model using questionnaires about academic achievement for engineering students at University D for this study. In addition, I would like to suggest what factors should be considered in non-face-to-face classes, especially in engineering colleges. As a result of the study, it was found that students' Q&A, feedback and e-learning system had a direct influence on academic achievement. In addition, it was confirmed that they had an indirect influence on academic achievement through the parameters of theory class and practical class.

• Korean Medical Education Review
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• v.23 no.3
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• pp.185-193
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• 2021

Students must be familiar with clinical skills before starting clinical practice to ensure patients' safety and enable efficient learning. However, performance is mainly tested in the third or fourth years of medical school, and studies using the validity framework have not been reported in Korea. We analyzed the validity of a performance test conducted among second-year students classified into content, response process, internal structure, relationships with other variables, and consequences according to Messick's framework. As results of the analysis, content validity was secured by developing cases according to a pre-determined blueprint. The quality of the response process was controlled by training and calibrating raters. The internal structure showed that (1) reliability by generalizability theory was acceptable (coefficients of 0.724 and 0.786, respectively, for day 1 and day 2), and (2) the relevant domains had proper correlations, while the clinical performance examination (CPX) and objective structured clinical examination (OSCE) showed weaker relationships. OSCE/CPX scores were correlated with other variables, especially grade point average and oral structured exam scores. The consequences of this assessment were (1) making students learn clinical skills and study themselves, while causing too much stress for students due to lack of motivation; (2) reminding educators of the need to apply practical teaching methods and to give feedback on the test results; and (3) providing an opportunity for faculty to consider developing support programs. It is necessary to develop the blueprint more precisely according to students' level and to verify the validity of the response process with statistical methods.