• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.307-312
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• 2021

Artificial Intelligence has a long history and is used in various fields including image recognition and automatic translation. Therefore, when we first encounter artificial intelligence, many terms, concepts and technologies often have difficulty in setting or implementing research direction. This study summarized important concepts related to artificial intelligence and summarized the progress of the past 60 years to help researcher suffering from these difficulties. Through this, it is possible to establish the basis for the use of vast artificial intelligence technologies and establish the right direction for research.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.251-259
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• 1999

The purpose of this study is to develope a program for statistics education. This program deals with simulation which is helpful in understanding some elementary statistical concepts. This program under multimedia environment which includes sound video animation etc. doesn't show only the result but make it possible for students to execute the program by stages. This type of dynamic learning is efficient to overcome the limits of teaching materials or classroom work. Also it can interest students greatly. By executing it the students can understand the method and meaning of simulation and acquire concepts of probability and statistical inference naturally.

• The Mathematical Education
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• v.47 no.2
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• pp.221-224
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• 2008

The article shows that the concept 'weight' and the concept 'heaviness' give rise to different abstractions in the sense of Piaget and that these two concepts are differentiated by set-theoretic devices. The failure of differentiation of these two concepts 'weight' and the 'heaviness' can cause the failure of learning of the difference between reflective abstraction and empirical reflective abstraction. To explain the Piagetian abstrcation in a classroom, the author suggests to use the concept 'color' instead of the concept 'weigtht'.

• The Mathematical Education
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• v.42 no.1
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• pp.11-18
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• 2003

One of the terms that are most often used in mathematics classrooms by either teachers or students might be about 'understanding' of mathematical concepts. Although 'understanding' in mathematics teaching and learning has been highly emphasized by many people, there is no exact and undebatable definition of 'understanding' as of yet. This paper tries to contribute to unfolding the meaning and the structure of understanding in mathematics education along with various literature and finally enhance our understanding of 'understanding' in mathematics education.

• The Mathematical Education
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• v.48 no.3
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• pp.353-358
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• 2009

The article discusses the independence concept occurring in the learning of probability. The author does not distinguishes the independence in the events from the independence in the trials. Instead, the author suggests the physico-empirical independence and the logico-mathematical independence to distinguish between the two concepts.

• Journal of the Korean Regional Science Association
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• v.15 no.1
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• pp.93-116
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• 1999

Recently the concept of learning has become very fashionable among academics from different economic disciplines. Economic geographers and spatial planners joined this fashion by increasingly speaking about the 'learning region'. This paper makes clear that this learning region'. This paper makes clear that this learning region concept has been launched from three angles; as spatial outcome of grand societal changes, as spatial concentration of entrepreneurial learning for innovation and as regional development concept. Despite the deficits and flaws such a young concept is faced with, such as vague definitions, the lack of empirical research and an insufficiently clear separation from existing concepts, the learning region concept might provide economic geography with more insight in agglomeration effects, stronger links with policy-making and more knowledge on path dependency and thus on unravelling the distinction between 'good' and 'bad' industrial agglomerations.

• Electronics and Telecommunications Trends
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• v.34 no.1
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• pp.98-110
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• 2019

Machine learning has been applied to medical imaging and has shown an excellent recognition rate. Recently, there has been much interest in preventive medicine. If data are accessible, machine learning packages can be used easily in digital healthcare fields. However, it is necessary to prepare the data in advance, and model evaluation and tuning are required to construct a reliable model. On average, these processes take more than 80% of the total effort required. In this study, we describe the basic concepts of machine learning, pre-processing and visualization of datasets, feature engineering for reliable models, model evaluation and tuning, and the latest trends in popular machine learning frameworks. Finally, we survey a explainable machine learning analysis tool and will discuss the future direction of machine learning.

• Korean Journal of Radiology
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• v.21 no.1
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• pp.33-41
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• 2020

Artificial intelligence has been applied to many industries, including medicine. Among the various techniques in artificial intelligence, deep learning has attained the highest popularity in medical imaging in recent years. Many articles on deep learning have been published in radiologic journals. However, radiologists may have difficulty in understanding and interpreting these studies because the study methods of deep learning differ from those of traditional radiology. This review article aims to explain the concepts and terms that are frequently used in deep learning radiology articles, facilitating general radiologists' understanding.

• The Journal of Korean Association of Computer Education
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• v.12 no.6
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• pp.53-67
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• 2009

In this study we surveyed the characteristics of the Semantic Web and ontology technology, analyzing the studies which applied ontology to e-Learning. In addition, we investigated the models which should be considered in the adaptive learning, analyzing the existing adaptive learning systems. On the basis of the analysis of them, we sought the ways to apply ontology for supporting the adaptive learning in the e-learning system, designing an ontology-based adaptive learning system. The system made up for the weak points of the existing ontology-based learning systems. That is, it appropriately diagnoses learners' knowledge level of learning concepts, classifying the learning styles in detail, and providing their corresponding learning methods and content. By adapting the learning content to the learners' individual learning style and knowledge level, this system would support their learning more efficiently and more effectively.

• Proceedings of the Zoological Society Korea Conference
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• 1998.10b
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• pp.377.2-377
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• 1998

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