• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.3
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• pp.227-234
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• 2017

In this paper, we propose a system whereby one can automatically classifies categories based on image data of the products for a shopping mall platform. Many products sold within internet shopping malls are classified their category defined by the same use of product names and products. However, it is difficult to search by category classification when the classification of the product is uncertain and the product classified by the shopping mall seller judgment is different from the purchasing user judgment. We proposes classification and retrieval method by Deep Learning technique solely using product image. The system can categorize products by using their images and its speed and accuracy are quantified using test data. The performance is evaluated with the test data. In addition, its usability is tested with the participants.

• Journal of Digital Convergence
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• v.17 no.3
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• pp.221-226
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• 2019

The characteristic of the beta wave among the EEG waves corresponds to the stress area of human perception. The over-bandwidth of the stress is extracted by analyzing the beta-wave correlation between the low-bandwidth and high-bandwidth. We present a KMeans clustering analysis model for unsupervised machine learning to construct an analytical model for analyzing and extracting the beta-wave correlation. The proposed model classifies the beta wave region into clusters of similar regions and identifies anomalous waveforms in the corresponding clustering category. The abnormal group of waveform clusters and the normal category leaving region are discriminated from the stress risk group. Using this model, it is possible to discriminate the degree of stress of the cognitive state through the EEG waveform, and it is possible to manage and apply the cognitive state of the individual.

• Journal of Software Assessment and Valuation
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• v.14 no.2
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• pp.1-9
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• 2018

The proportion of users and companies using open source continues to grow. The size of open source software market is growing rapidly not only in foreign countries but also in Korea. However, compared to the continuous development of open source software, there is little research on open source software subject classification, and the classification system of software is not specified either. At present, the user uses a method of directly inputting or tagging the subject, and there is a misclassification and hassle as a result. Research on open source software classification can also be used as a basis for open source software evaluation, recommendation, and filtering. Therefore, in this study, we propose a method to classify open source software by using machine learning model and propose performance comparison by machine learning model.

• Journal of Science Education
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• v.34 no.1
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• pp.33-46
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• 2010

The purpose of this study was to find out the image as science teachers recognized by pre-service science teachers. The data was collected from 312 pre-service science teachers from Kyungpook National University in Daegu and participants were asked to write about the image of science teachers they liked most and least in their secondary school years freely. The result of this research was as follows. The image as science teachers categorized 2 factors: science instructional situation, image of science teacher. Each factor was subdivided into more detailed ones. First of all, 'science instructional situation' category subdivided into lesson style, teaching-learning materials, teaching methods, and class atmosphere. In lesson style, 'experiment' and 'observation' gained the most favorable comments, and questioning-answering gained the least. In teaching-learning materials, print materials such as handouts, worksheets, reports were the most liked, and 'writing on the blackboard' was the least liked. In teaching methods, the 'detailed and systematic explanation of the theory and concepts' was preferred to rote learning and memorization lacking explanation. In class atmosphere, friendly and free atmosphere was the most preferred, and uncomfortable, boring one was the least preferred. Secondly, in 'image of the science teachers' category and 'quality as the teachers' sub-category, thoughtful and considerate teachers who respect students' personality was the most preferred. On the contrary, they didn't prefer teachers who were indifferent and humiliated students. Finally in 'characteristics of the teachers' sub-category, the participants liked clear, energetic voice, and mild expression, and they didn't like formal style, overly fancy clothes, etc. Based on the result of this study, more empirical study on the teachers' image is needed, and the thoughts of educational administrators, students, parents, and teachers should be reflected because an undesirable teacher can be advised and get opportunity to be a better teacher.

• Journal of The Korean Association For Science Education
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• v.24 no.1
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• pp.90-108
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• 2004

This study is to understand preservice science teachers' previous experience, beliefs about teaching and learning, and visions of themselves as future teachers. The data were collected from two individual interviews with 7 voluntary students and analyzed qualitatively for category construction. As the results of this study, we presented two cases, which showed that their different views of teaching science are strongly related to their previous experiences as learners and observers in schools, and that there is the apparent consistency between each participant's beliefs about science teaching and learning and their own visions of teaching in a science classroom. Implications for preservice science teacher education related to the results were discussed.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.385-386
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• 2019

This paper proposes a fusion model based on Long-Short Term Memory networks (LSTM) and CNN deep learning methods, and applied to multi-category news datasets, and achieved good results. Experiments show that the fusion model based on deep learning has greatly improved the precision and accuracy of text sentiment classification.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.140-146
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• 2023

A detailed survey, applications and challenges of video encoding-decoding systems is discussed in this paper. A novel architecture has also been set aside for future work in the same direction. The literature reviews span the years 1960 to the present, highlighting the benchmark methods proposed by notable academics in the field of video compression. The timeline used to illustrate the review is divided into three sections. Classical methods, conventional heuristic methods, and current deep learning algorithms are all used for video compression in these categories. The milestone contributions are discussed for each category. The methods are summarized in various tables, along with their benefits and drawbacks. The summary also includes some comments regarding specific approaches. Existing studies' shortcomings are thoroughly described, allowing potential researchers to plot a course for future research. Finally, a closing note is made, as well as future work in the same direction.

• Asia pacific journal of information systems
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• v.32 no.2
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• pp.226-248
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• 2022

The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.111-128
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• 2018

About 3.9 million apps and 24 primary categories can be approved on Apple iTunes Store. Making accurate categorization can potentially receive many benefits for developers, app stores, and users, such as improving discoverability and receiving long-term revenue. However, current categorization problems may cause usage inefficiency and confusion, especially for cross-attribution, etc. This study focused on evaluating the reliability of app categorization on Apple iTunes Store by using several rounds of inter-rater reliability statistics, locating categorization problems based on Machine Learning, and making more accurate suggestions about representative functionality stems for each primary category. A mixed methods research was performed and total 4905 popular apps were observed. The original categorization was proved to be substantial reliable but need further improvement. The representative functionality stems for each category were identified. This paper may provide some fusion research experience and methodological suggestions in categorization research field and improve app store's categorization in discoverability.

• Education of Primary School Mathematics
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• v.10 no.1 s.19
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• pp.15-28
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• 2007

From a social constructivists' perspective, knowledge is not transmitted by language but it is constructed by social interactions with others. That is, it is viewed in social constructivism that learning is a process in which knowledge is constructed by communicative interactions with more capable others. In this vein, a class might be analyzed and characterized in terms of interactional patterns of teacher-student and student-student in class. For this, a primary math class was selected and observed and it was analyzed by the Flanders category system to investigate the effects of the math teaching based on verbal interactions on the learning of math. The class was taught in a teacher-centered and direct way but in the class math knowledge was taught through univocal communications in the form of question-answer. The results of this study appeared to suggest that verbal interactional patterns should take place frequently in math teaching in the sequence of a teacher's questions$\to$students' extensive responses $\to$ positive feedback for the students' responses by the teacher $\to$ the acceptance of the students' responses $\to$ the teacher's explanation or students' questions. In other words, math might be taught more effectively through the verbal discourse patterns proposed in this study.