• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.189-200
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• 2022

The gig economy offers many advantages, such as flexibility, variety, independence, and lower cost. However, there are also safety concerns, lack of regulations, uncertainty, and unsatisfactory services, causing people to voice their opinion on social media. This paper aims to explore the sentiments of consumers concerning gig economy services (Grab, Foodpanda and Airbnb) through the analysis of social media. First, Vader Lexicon was used to classify the comments into positive, negative, and neutral sentiments. Then, the comments were further classified into three machine learning algorithms: Support Vector Machine, Light Gradient Boosted Machine, and Logistic Regression. Results suggested that gig economy services in Malaysia received more positive sentiments (52%) than negative sentiments (19%) and neutral sentiments (29%). Based on the three algorithms used in this research, LGBM has been the best model with the highest accuracy of 85%, while SVM has 84% and LR 82%. The results of this study proved the power of text mining and sentiment analysis in extracting business value and providing insight to businesses. Additionally, it aids gig managers and service providers in understanding clients' sentiments about their goods and services and making necessary adjustments to optimize satisfaction.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.98-102
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• 2023

This paper presents a study on how augmenting semi-synthetic image data improves the performance of human detection algorithms. In the field of object detection, securing a high-quality data set plays the most important role in training deep learning algorithms. Recently, the acquisition of real image data has become time consuming and expensive; therefore, research using synthesized data has been conducted. Synthetic data haves the advantage of being able to generate a vast amount of data and accurately label it. However, the utility of synthetic data in human detection has not yet been demonstrated. Therefore, we use You Only Look Once (YOLO), the object detection algorithm most commonly used, to experimentally analyze the effect of synthetic data augmentation on human detection performance. As a result of training YOLO using the Penn-Fudan dataset, it was shown that the YOLO network model trained on a dataset augmented with synthetic data provided high-performance results in terms of the Precision-Recall Curve and F1-Confidence Curve.

• Journal of Web Engineering
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• v.21 no.2
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• pp.425-442
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• 2021

Media technology has been developed to give users a sense of immersion. Recent media using 3D spatial data, such as augmented reality and virtual reality, has attracted attention. A point cloud is a data format that consists of a number of points, and thus can express 3D media using coordinates and color information for each point. Since a point cloud has a larger capacity than 2D images, a technology to compress the point cloud is required, i.e., standardized in the international standard organization MPEG as a video-based point cloud compression (V-PCC). V-PCC decomposes 3D point cloud data into 2D patches along orthogonal directions, and those patches are placed into a 2D image sequence, and then compressed using existing 2D video codecs. However, data loss may occur while converting a 3D point cloud into a 2D image sequence and encoding this sequence using a legacy video codec. This data loss can cause deterioration in the quality of a reconstructed point cloud. This paper proposed a method of enhancing a reconstructed point cloud by applying a super resolution network to the 2D patch image sequence of a 3D point cloud.

• Journal of Broadcast Engineering
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• v.9 no.4 s.25
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• pp.305-321
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• 2004

With the advent of the digital broadcasting, the audiences can access a large number of TV programs and their information through the multiple channels on various media devices. The access to a large number of TV programs can support a user for many chances with which he/she can sort and select the best one of them. However, the information overload on the user inevitably requires much effort with a lot of patience for finding his/her favorite programs. Therefore, it is useful to provide the persona1ized broadcasting service which assists the user to automatically find his/her favorite programs. As the growing requirements of the TV personalization, we introduce our automatic user preference learning algorithm which 1) analyzes a user's usage history on TV program contents: 2) extracts the user's watching pattern depending on a specific time and day and shows our automatic TV program recommendation system using MPEG-7 MDS (Multimedia Description Scheme: ISO/IEC 15938-5) and 3) automatically calculates the user's preference. For our experimental results, we have used TV audiences' watching history with the ages, genders and viewing times obtained from AC Nielson Korea. From our experimental results, we observed that our proposed algorithm of the automatic user preference learning algorithm based on the Bayesian network can effectively learn the user's preferences accordingly during the course of TV watching periods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.470-477
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• 2016

This study analyzed the research trends concerning the use of smart devices by young children, such as smart phones, tablet PCs, interactive whiteboards and teacher assistant robots, which has begun to be mentioned relatively recently, and attempted to analyze the characteristics of the research trends and provide guidelines for the direction of future research. A search of articles related to the use of electronic media by young children using an Online Search DB revealed a total of 192 research papers, which were analyzed according to the subject of research, teaching-learning method, area of development and area of activity. It was found that the teaching-learning method, teacher education and professionalism were highly prevalent in the subject of research; the education method integrating play activity with literature activity were highly prevalent in the teaching-learning method; language development and social development were highly prevalent in the area of development; and language activity and social activity were highly prevalent in the area of activity.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.39-60
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• 2021

In the field of deep learning, there are many algorithms mainly after GAN in research related to generation, but in terms of generation, there are similarities and differences with art. If the generation in the engineering aspect is mainly to judge the presence or absence of a quantitative indicator or the correct answer and the incorrect answer, the creation in the artistic aspect creates a creation that interprets the world and human life by cross-validating and doubting the correct answer and incorrect answer from various perspectives. In this paper, the video generation ability of deep learning was interpreted from the perspective of collage and compared with the results made by the artist. The characteristic of the experiment is to compare and analyze how much GAN reproduces the result of the creator made with the collage technique and the difference between the creative part, and investigate the satisfaction level by making performance evaluation items for the reproducibility of GAN. In order to experiment on how much the creator's statement and purpose of expression were reproduced, a deep learning algorithm corresponding to the statement keyword was found and its similarity was compared. As a result of the experiment, GAN did not meet much expectations to express the collage technique. Nevertheless, the image association showed higher satisfaction than human ability, which is a positive discovery that GAN can show comparable ability to humans in terms of abstract creation.

• Journal of Broadcast Engineering
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• v.27 no.2
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• pp.185-197
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• 2022

Recently, deep learning networks with high performance for object recognition are emerging. In the case of object recognition using deep learning, it is important to build a training data set to improve performance. To build a data set, we need to collect and label the images. This process requires a lot of time and manpower. For this reason, open data sets are used. However, there are objects that do not have large open data sets. One of them is data required for license plate detection and recognition. Therefore, in this paper, we propose an artificial license plate generator system that can create large data sets by minimizing images. In addition, the detection rate according to the artificial license plate arrangement structure was analyzed. As a result of the analysis, the best layout structure was FVC_III and B, and the most suitable network was D2Det. Although the artificial data set performance was 2-3% lower than that of the actual data set, the time to build the artificial data was about 11 times faster than the time to build the actual data set, proving that it is a time-efficient data set building system.

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.1-10
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• 2022

Learning C programming language in electronics education is an important basic education course for understanding computer programming and acquiring the ability to use microprocessors in embedded systems. In order to focus on understanding basic grammar and algorithms, it is a common teaching method to write programs based on C standard library functions in the console window and learn theory and practice in parallel. However, if a student wants to start a project activity or go to a deeper stage after acquiring some basic knowledge of the C language, using only the C standard library function in the console window limits what a student can express or control with the C program. For the purpose of making it easier for a student to use graphics or multimedia resources and increase educational value, this paper studies a case of applying Simple DirectMedia Layer (SDL), an open source software, into the C programming language learning process. The SDL-based programming course applied after completing the basic programming curriculum performed in the console window is introduced, and the educational value is evaluated through a survey. As a result, more than 56% of the respondents expressed positive opinions in terms of improved application ability, stimulating interest, and overall usefulness, and less than 4% of them had negative opinions.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.91-117
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• 2022

The number of depressed patients in Korea and around the world is rapidly increasing every year. However, most of the mentally ill patients are not aware that they are suffering from the disease, so adequate treatment is not being performed. If depressive symptoms are neglected, it can lead to suicide, anxiety, and other psychological problems. Therefore, early detection and treatment of depression are very important in improving mental health. To improve this problem, this study presented a deep learning-based depression tendency model using Korean social media text. After collecting data from Naver KonwledgeiN, Naver Blog, Hidoc, and Twitter, DSM-5 major depressive disorder diagnosis criteria were used to classify and annotate classes according to the number of depressive symptoms. Afterwards, TF-IDF analysis and simultaneous word analysis were performed to examine the characteristics of each class of the corpus constructed. In addition, word embedding, dictionary-based sentiment analysis, and LDA topic modeling were performed to generate a depression tendency classification model using various text features. Through this, the embedded text, sentiment score, and topic number for each document were calculated and used as text features. As a result, it was confirmed that the highest accuracy rate of 83.28% was achieved when the depression tendency was classified based on the KorBERT algorithm by combining both the emotional score and the topic of the document with the embedded text. This study establishes a classification model for Korean depression trends with improved performance using various text features, and detects potential depressive patients early among Korean online community users, enabling rapid treatment and prevention, thereby enabling the mental health of Korean society. It is significant in that it can help in promotion.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.26-37
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• 2021

This study conducted a big data analysis on news to identify the agenda of media literacy, which has been socially discussed, and on which relevant policy directions will be proposed. To this end 1,336 articles from January 1, 2019 to September 30, 2020 were collected and a topic modeling analysis was conducted according to four periods. Five topics for each period were derived through the analysis, and implications based on the results are as follows. First, the government should implement a nation-level systematic approach to media literacy education according to life cycle stages to generate economic and cultural value. Second, local communities and schools should provide systematic support and education guidance activities to ensure a sustainable ecosystem for media literacy and prevent an educational gap and loss in learning. Third, efforts should be made in various aspects to minimize the side effects resulting from constantly providing media literacy education; furthermore a culture of desirable media application should be established. Finally, a research environment for scientific research on media literacy, active exchange of experience and value obtained in the field, and long-term accumulation of research results should be encouraged to develop a robust knowledge exchange culture.