• Journal of Physiology & Pathology in Korean Medicine
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• v.30 no.6
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• pp.412-418
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• 2016

The emergence of systems biology in the 21st century is changing the paradigm of biomedical research. Whereas the reductionist approaches focus on components rather than time or contexts, systems biology focus more on interrelationships, dynamics, and contexts. The key ideas of the systems biology shares much with the philosophy of Korean Medicine(KM) and therefore, the paradigm shift is shedding light on understanding the mechanism of action of KM at system level. In this article, I provide a guide to learning systems biology for KM researchers using online learning resources. Thanks to the recent development of MOOC(massive open online courses) and other online learning platforms, learners can access to plenty of high-quality resources from top-tier universities in the world. I expect this guide help researchers to employ systems biology methods into their KM researches, and will lead to the development of future curricula for training "bi-lingual" experts, KM and computational approaches.

• Journal of The Korean Association For Science Education
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• v.11 no.1
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• pp.15-24
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• 1991

The purpose of this study was to investigate the effectiveness of Learning Cycle approach to change the concept of density. The results of the study were as follows : 1) Students already had various types of preconception related to density before formal learning. These preconceptions mostly differ from scientific concepts. 2) Male students were much better than female ones in the development of scientific concepts before formal learning. These differences were found statistically significant(P<0.01). 3) The higher the cognitive level of the students, the better the development of scientific concepts. 4) In the change of preconceptions to scientific concepts by treatment, there was significant difference between control group and experimental group at the 0.05 level. It was found that Learning Cycle approaches were more effective than traditional approaches in acquiring the concept of density. 5) It was found that there was no significant difference On the retention level of the concept of density between control group and experimental group.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.2
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• pp.19-24
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• 2018

This study investigated the academic stress and the immersion in learning in relation to AR and VR assisted instructions compared to traditional approaches. To that end, 78 $8^{th}$ graders in T and S city in Gangwondo were assigned to experimental and control groups. The experimental group received the VR and AR lessons. The academic stress was measured with the pre- and post-test scores, while the immersion in learning was measured with the post-test scores. In brief, AR and VR assisted lessons made statistically significant differences in the academic stress and immersion in learning in comparison to the traditional approaches.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.129-134
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• 2024

Object Detection is an emerging technology in the field of Computer Vision and Image Processing that deals with detecting objects of a particular class in digital images. It has considered being one of the complicated and challenging tasks in computer vision. Earlier several machine learning-based approaches like SIFT (Scale-invariant feature transform) and HOG (Histogram of oriented gradients) are widely used to classify objects in an image. These approaches use the Support vector machine for classification. The biggest challenges with these approaches are that they are computationally intensive for use in real-time applications, and these methods do not work well with massive datasets. To overcome these challenges, we implemented a Deep Learning based approach Convolutional Neural Network (CNN) in this paper. The Proposed approach provides accurate results in detecting objects in an image by the area of object highlighted in a Bounding Box along with its accuracy.

• Asia pacific journal of information systems
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• v.6 no.2
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• pp.77-105
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• 1996

The accuracy of knowledge is a major concern for expert system developers and users. Machine learning approaches have recently been found to be useful in knowledge acquisition for expert systems. However, the accuracy of concept acquired from machine learning could not be analyzed in most cases. In this paper we develop a comprehensive knowledge error analysis methodology for practical use of expert systems. Decision tree induction is an important type of machine learning method for business expert systems. Here we start to analyze with knowledge acquired from decision tree induction method, and extend the results to develop error analysis methodology for general machine learning methods. We give several examples and illustrations for these results. We also discuss the applicability of these results to multistrategy learning approaches.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.1014-1028
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• 2017

Social networking services (SNSs) such as Twitter, MySpace, and Facebook have become progressively significant with its billions of users. Still, alongside this increase is an increase in security threats such as cross-site scripting (XSS) threat. Recently, a few approaches have been proposed to detect an XSS attack on SNSs. Due to the certain recent features of SNSs webpages such as JavaScript and AJAX, however, the existing approaches are not efficient in combating XSS attack on SNSs. In this paper, we propose a machine learning-based approach to detecting XSS attack on SNSs. In our approach, the detection of XSS attack is performed based on three features: URLs, webpage, and SNSs. A dataset is prepared by collecting 1,000 SNSs webpages and extracting the features from these webpages. Ten different machine learning classifiers are used on a prepared dataset to classify webpages into two categories: XSS or non-XSS. To validate the efficiency of the proposed approach, we evaluated and compared it with other existing approaches. The evaluation results show that our approach attains better performance in the SNS environment, recording the highest accuracy of 0.972 and lowest false positive rate of 0.87.

• Journal of The Korean Association For Science Education
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• v.30 no.5
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• pp.668-680
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• 2010

In this study, we investigated the characteristics, the mapping understanding, the mapping errors, and the perceptions of student-generated analogies on the separation of mixtures using the sizes of particles by elementary school students' approaches to learning. Fourth graders (N=92) were selected and administered with the tests on the approaches to learning, self-generating analogies, and perception of self-generating analogies. The results revealed that the meaningful learners made more analogies, especially structural/functional, enriched, and higher systematic ones than the rote learners. However, there were little difference in students' approaches to learning in the subcategories of representation (verbal, pictorial, and verbal/pictorial), artificiality (artificial and everyday), and abstraction (abstract and concrete). The meaningful learners had deeper understanding of the analogy and fewer mapping errors than the rote learners. In addition, the numbers of the shared attributes included in student-generated analogies and the scores of the mapping understanding of the meaningful learners were significantly higher than those of the rote learners. Many students, regardless of students' approaches to learning, had positive perceptions of the self-generating analogies in various cognitive and motivational aspects. However, they also point out the various difficulties in the self-generating analogies as their disadvantages. Educational implications of these findings are discussed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.111-118
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• 2016

Artificial intelligence (AI) is making computer systems intelligent to do right thing. The AI is used today in a variety of fields, such as journalism, medical, industry as well as entertainment. The impact of AI is becoming larger day after day. In general, the AI system has to lead the optimal decision under uncertainty. But it is difficult for the AI system can derive the best conclusion. In addition, we have a trouble to represent the intelligent capacity of AI in numeric values. Statistics has the ability to quantify the uncertainty by two approaches of frequentist and Bayesian. So in this paper, we propose a methodology of the connection between statistics and AI efficiently. We compute a fixed value for estimating the population parameter using the frequentist learning. Also we find a probability distribution to estimate the parameter of conceptual population using Bayesian learning. To show how our proposed research could be applied to practical domain, we collect the patent big data related to Apple company, and we make the AI more intelligent to understand Apple's technology.

• The Journal of the Korea Contents Association
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• v.16 no.12
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• pp.35-42
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• 2016

The purpose of this study is to examine the effect of action learning approaches on problem-solving skills and learning agency of nursing undergraduate students. This experimental study is designed for a nonequivalent control group. The program was put into practice 2 times a week for 4 weeks. The number of subjects in this research consists of 105, where 53 of the experimental group participated in action learning program and 52 of the control group didn't do. The data was analyzed by ${\chi}^2$-test, Chi-Square test, t-test and paired t-test. The effects of action learning approaches on learning outcomes in nursing process courses are as follows: The problem solving ability of the experimental group has been more elevated than that of the control group. The experimental group has made increase in self directed learning skills. The action learning approaches on learning outcomes in nursing process courses are convenient in nursing process courses. This study has significance in that it identified the availability of the action learning program and that it would be useful teaching and learning method to achieve learning outcomes.

• International Journal of Advanced Culture Technology
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• v.4 no.3
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• pp.6-12
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• 2016

The concept of intergenerational and intercultural approaches in education and learning are changing nowadays. Intergenerational approach in the third age education and learning programs can be defined as planned activities that link various generations with the goal of exchanging knowledge, experiences and receiving mutual benefits. The goal is to connect people by using mutually beneficial activities that encourage understanding, cooperation and respect between generations, as well as contribute to the society. Intercultural approach in the third age education is connected with activities that link people of various cultures aimed at receiving mutual benefits. This paper discusses the development of third age education in Saint Petersburg, Russia and shows how the intercultural and intergenerational approaches are used in this type of education. The third age universities in Saint Petersburg do not have a lot of experience in this. In the article examples of the using intercultural and intergenerational approaches in the third age education are showed.