• 동의생리병리학회지
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• 제30권6호
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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.

• 한국과학교육학회지
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• 제11권1호
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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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• 제10권2호
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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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• 제24권5호
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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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• 제6권2호
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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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• 제13권4호
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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.

• 한국과학교육학회지
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• 제30권5호
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• pp.668-680
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• 2010

이 연구에서는 알갱이의 크기에 따른 혼합물의 분리 원리에 대해 초등학생들이 만든 비유의 특성, 대응관계 이해도, 대응 오류, 비유 만들기에 대한 인식을 학습접근양식에 따라 조사했다. 초등학교 4학년 92명을 선정하여 학습접근양식 검사, 비유 만들기 검사, 비유 만들기에 대한 인식 검사를 실시했다. 연구 결과, 기계적 학습접근양식을 지닌 학생들보다 유의미학습접근양식을 지닌 학생들이 더 많은 수의 비유를 만드는 것으로 나타났다. 만든 비유 유형의 경우, 기계적 학습접근양식보다 유의미 학습접근양식을 지닌 학생들이 구조적/기능적 비유, 부연 비유, 고체계성 비유를 더 많이 만드는 경향이 있었으나 표현 방식(글, 그림, 글/그림)과 상황의 작위성(작위적, 일상적), 추상도(추상적, 구체적) 항목에서는 학습접근양식에따른 차이가 거의 없었다. 기계적 학습접근양식보다 유의미 학습접근양식을 지닌 학생들이 비유에 대한 이해도가 더 높았고, 비유물에 포함된 공유 속성의 수와 대응 관계 이해도 점수도 유의미하게 높았으며, 대응 오류를 범하는 경우도 더 적었다. 학습접근양식에 관계없이 많은 학생들이 비유 만들기 활동에 대해 다양한 인지적 동기적 측면에서 긍정적으로 인식하는 것으로 나타났다. 반면, 비유 만들기 활동에서의 다양한 어려움 등과 같은 단점을 지적하기도 했다. 이에 대한 교육적 함의를 논했다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권2호
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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.

• 한국콘텐츠학회논문지
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• 제16권12호
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• pp.35-42
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• 2016

본 연구는 액션러닝을 적용한 간호과정 학습프로그램이 간호학과 학생들의 문제해결능력과 자기주도적 학습능력에 미치는 효과를 규명하기 위한 비동등성 대조군 전후설계 실험연구이다. 연구대상은 G시와 J도에 소재한 4년제 간호학과 2학년 중 실험군 53명, 대조군 52명으로 총 105명이었다. 주 2회, 4주간 프로그램을 실시하였으며 실시 전과 후에 문제해결능력과 자기주도적 학습능력를 측정하였다. 측정된 결과는 ${\chi}^2$-test와 Chi-Square test, t-test, paired t-test로 분석하였다. 연구결과 액션러닝 학습프로그램은 문제명료화, 원인분석, 대안개발, 계획/실행, 수행평가로 구성된 학습자의 문제해결능력을 증진시켰고 학습계획, 학습실행, 학습평가로 구성된 자기주도적 학습능력을 증진시켰다. 그러므로 본 연구는 학습방법으로의 액션러닝이 간호학과 학생들의 간호과정 수업을 하는데 효과적임을 파악한 점에서 연구의 의의를 지니며, 향후 간호관련 전공학문 분야의 효과적인 교수법으로 확대적용 할 수 있을 것으로 기대한다.

• International Journal of Advanced Culture Technology
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• 제4권3호
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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.