• Investigative Magnetic Resonance Imaging
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• v.23 no.2
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• pp.81-99
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• 2019

Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion, and image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications.

• Journal of Information Technology Services
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• v.9 no.4
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• pp.109-121
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• 2010

Most organizations adopt their own performance measurement systems. Those organizations select performance measures to meet their goals. Organizations can give only limited description of what performance measures are. Kaplan and Norton suggest that the Balanced Scorecard (BSC) to complement the conventional performance measures. The BSC can provide management system with a comprehensive strategic vision and integrates non-financial measures with financial measures. The BSC is widely used for measuring corporate performance. This paper investigates how the BSC-based performance measures can be applied to Learning City. The Learning City's performance measures and strategy map on the basis of the BSC are suggested in this research. This paper adopt the AR(assurance region)-DEA model which could limit the range of weight on performance measures to prevent each viewpoint of BSC from having unlimited elasticity. The proposed model is based on CCR model including a property of unit invariance to use the data without normalization process.

• The Journal of Information Systems
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• v.31 no.4
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• pp.01-23
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• 2022

Purpose This study aims to propose a deep learning-based fake review detection model by combining authors' linguistic features and semantic information of reviews. Design/methodology/approach This study used 358,071 review data of Yelp to develop fake review detection model. We employed linguistic inquiry and word count (LIWC) to extract 24 linguistic features of authors. Then we used deep learning architectures such as multilayer perceptron(MLP), long short-term memory(LSTM) and transformer to learn linguistic features and semantic features for fake review detection. Findings The results of our study show that detection models using both linguistic and semantic features outperformed other models using single type of features. In addition, this study confirmed that differences in linguistic features between fake reviewer and authentic reviewer are significant. That is, we found that linguistic features complement semantic information of reviews and further enhance predictive power of fake detection model.

• Journal of Engineering Education Research
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• v.14 no.3
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• pp.9-14
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• 2011

This study was conducted to compare the learning styles and self-leadership between engineering college students with high and low team performance records. About 70% of students in high team performance group showed learning styles of converger and accommodator, whereas about 67% of students in low team performance group showed learning styles of accommodator and diverger. In regard to self-leadership, high team performance group showed higher level of self-leadership, especially self-observation, self-punishment, natural reward strategies, visualizing successful performance, self-talk, and evaluating beliefs and assumptions. It is recommended to provide the engineering students with the specialized training program to complement their learning styles and self-leadership strategies.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.651-661
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• 2020

This research aims to examine the relationships among entrepreneurial orientation, organizational learning capability, firm innovation, and firm performance. To achieve a data collection, a mail survey procedure via questionnaire was implemented by using executives or managers of gems & jewelry industries, textile and clothing industries, leather and accessories, fashion apparel industries in Thailand as the key informants. Of the surveys completed and returned, 388 were usable. Hence, a model with a structural equation was used to evaluate the data survey of 388 respondents. The results reveal that, in terms of the mediating effect, organizational learning capacity and firm innovation can complement each other in order to improve entrepreneurial orientation. Findings show that entrepreneurial orientation improves firm innovation, which in turn improves firm efficiency. Firm innovation acts as a variable mediating between enterprise orientation and firm performance. Our findings contribute to the current emergence of organizational learning capacity that mediated the relationship between entrepreneurial orientation and firm performance. Entrepreneurial orientation is normally a firm performance that enterprises develop which can have use the information available and make an impact. It can be considered through the mediation of organizational learning capability, and firm innovation variable and as stated in previous literature, it can influence firm performance.

• Journal of Korea Multimedia Society
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• v.21 no.4
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• pp.441-449
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• 2018

Deep learning has been used for various processing centered on image recognition. One core algorithms of the deep learning, convolutional neural network is an deep neural network that specialized in image recognition. In this paper, we use a convolutional neural network to classify forest insects and propose an optimization method. Experiments were carried out by combining two weight initialization and six parameter update methods. As a result, the Xavier-SGD method showed the highest performance with an accuracy of 82.53% in the 12 different combinations of experiments. Through this, the latest learning algorithms, which complement the disadvantages of the previous parameter update method, we conclude that it can not lead to higher performance than existing methods in all application environments.

• Research in Mathematical Education
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• v.6 no.2
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• pp.159-170
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• 2002

The undergraduate curriculum in differential equations has undergone important changes in favor of the visual and numerical aspects of the course primarily because of recent technological advances. Yet, research findings that have analyzed students' thinking and understanding in a reformed setting are still lacking. This paper discusses an ongoing developmental research effort to adapt the instructional design perspective of Realistic Mathematics Education (RME) to the teaching and learning of differential equations at Ewha Womans University. The RME theory based on the design heuristic using context problems and modeling was developed for primary school mathematics. However, the analysis of this study indicates that a RME design for a differential equations course can be successfully adapted to the university level.

• Journal of The Korean Association of Information Education
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• v.12 no.4
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• pp.395-404
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• 2008

The shape learning of elementary mathematics should required a variety of activities to help student based on intuitive understanding. Learning three-dimensional shapes, it is effectively take advantage of actual object, but difficult to check a development figure or various forms of actual object, it is effectively utilizing computers semi-actual object. In addition, the computer to take advantage of even after school resources, the limits of learning something concrete to take advantage complement. This research developed the three-dimensional shapes application of elementary students shape learning to use of Flex and Flash. This application to take advantage of the free observation and causing an interesting and effective learning.

• The Journal of the Korea Contents Association
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• v.7 no.11
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• pp.248-257
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• 2007

With the advance of information technology, e-learning is widely used. However, due to the lack of human computer interaction, e-mentoring or blended learning are adopted to complement the drawbacks of e-learning these days. One of the common purposes for adopting these tools is to enhance the interaction level by using bbs or blogs based on e-communities. If the cooperative learning contents management tools that share learners' knowledge in e-learning are provided, interactivity and educational effects can be enhanced. In this paper, a tree-based learning contents construction tool and a community-based cooperative learning contents construction tools that can share the learners' knowledge are proposed. Also, we analyze the influencing factors to the learners by using the proposed tools.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.499-509
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• 2020

This study explores how to build object classification learning data based on artificial intelligence. The data has been investigated recently in image classification fields and, in turn, has a great potential to use. In order to recognize and extract relatively accurate objects using artificial intelligence, a large amount of learning data is required to be used in artificial intelligence algorithms. However, currently, there are not enough datasets for object recognition learning to share and utilize. In addition, generating data requires long hours of work, high expenses and labor. Therefore, in the present study, a small amount of initial aerial image learning data was used in the GAN (Generative Adversarial Network)-based generator network in order to establish image learning data. Moreover, the experiment also evaluated its quality in order to utilize additional learning datasets. The method of oversampling learning data using GAN can complement the amount of learning data, which have a crucial influence on deep learning data. As a result, this method is expected to be effective particularly with insufficient initial datasets.