• Knowledge Management Research
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• v.20 no.3
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• pp.119-137
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• 2019

With the recent spread of digital content, more people have been watching the digital content of TV programs on their PCs or mobile devices, rather than on TVs. With the change in such media use pattern, genres(types) of broadcast programs change in the flow of the times and viewers' trends. The programs that were broadcast on TVs have been released in digital content, and thereby people watching such content change their perception. For this reason, it is necessary to newly and differently classify genres(types) of broadcast programs on the basis of digital content, from the conventional classification of program genres(types) in broadcasting companies or relevant industries. Therefore, this study suggests a plan for newly classifying broadcast programs through using machine learning with the log data of people watching the programs in online media and for applying the new classification. This study is academically meaningful in the point that it analyzes and classifies program types on the basis of digital content. In addition, it is meaningful in the point that it makes use of the program classification algorithm developed in relevant industries, and especially suggests the strategy and plan for applying it.

• Smart Media Journal
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• v.12 no.2
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• pp.36-41
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• 2023

With the advent of applicant tracking systems, online recruitment has become more popular, and recruitment fraud has become a serious problem. This research aims to develop a reliable model to detect recruitment fraud in online recruitment environments to reduce cost losses and enhance privacy. The main contribution of this paper is to provide an automated methodology that leverages insights gained from exploratory analysis of data to distinguish which job postings are fraudulent and which are legitimate. Using EMSCAD, a recruitment fraud dataset provided by Kaggle, we trained and evaluated various single-classifier and ensemble-classifier-based machine learning models, and found that the ensemble classifier, the random forest classifier, performed best with an accuracy of 98.67% and an F1 score of 0.81.

• Smart Media Journal
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• v.9 no.1
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• pp.45-50
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• 2020

With the development of deep learning, studies using artificial neural networks based on deep learning in recommendation systems are being actively conducted. Especially, the recommendation system based on RNN (Recurrent Neural Network) shows good performance because it considers the sequential characteristics of data. This study proposes a travel route recommendation system using GRU(Gated Recurrent Unit) and Session-based Parallel Mini-batch which are RNN-based algorithm. This study improved the recommendation performance through an ensemble of top1 and bpr(Bayesian personalized ranking) error functions. In addition, it was confirmed that the RNN-based recommendation system considering the sequential characteristics in the data makes a recommendation reflecting the meaning of the travel destination inherent in the travel route.

• Smart Media Journal
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• v.13 no.3
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• pp.27-35
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• 2024

This study deals with a method of combining image generation using Semi Supervised Learning based on GAN (Generative Adversarial Network) and image classification using ResNet50. Through this, a new approach was proposed to obtain more accurate and diverse results by integrating image generation and classification. The generator and discriminator are trained to distinguish generated images from actual images, and image classification is performed using ResNet50. In the experimental results, it was confirmed that the quality of the generated images changes depending on the epoch, and through this, we aim to improve the accuracy of industrial accident prediction. In addition, we would like to present an efficient method to improve the quality of image generation and increase the accuracy of image classification through the combination of GAN and ResNet50.

• Journal of Broadcast Engineering
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• v.21 no.5
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• pp.739-748
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• 2016

This study quantitatively measured the impact of blended learning with smart devices for learning satisfaction. It is targeted in specialized domestic company with IT Service which build smart learning systems and utilize for employee training. Specifically, it empirically analyzed that learning attitude(Self-efficacy, Self-innovativeness, Perceived usefulness, Perceived ease of use) with smart devices affect acceptance of smart learning and offline face-to-face learning satisfaction. As a result, the learning attitude of the smart learning gave a positive effect on the acceptance of the smart learning and then acceptance of the smart learning gave a positive effect on offline face-to-face learning satisfaction. Additionally learning the attitude of the smart learning even gave a positive impact, as well as the acceptance of smart learning experience in offline training. It imply that this variables of smart-learning attitude affect the self-directed learning and positive learning experience.

• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.137-143
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• 2012

The learners using learning content through the smart devices can access to the Internet from anytime and anywhere. However, with the rapid increase of learning content on the Web, it will be time-consuming for learners to find contents they really want to and need to study. Therefore, e-learning systems should not only provide flexible content delivery, but support adaptive harmonizing fusion content. The harmonizing fusion content it is a very important in fusion e-learning service. The representative method to provide synchronization between fusion content is a provide absolute time value between of the contents. However, this method is occurs a problem transferring time delay. Also, to enter an absolute time value for the duration of the each content is several problems arise. In this paper introduces a new smart e-smart service support the harmonizing media based technology to create synchronized learning presentation.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.9
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• pp.291-296
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• 2020

With the recent development of IT technology, various digital media to which new information technology is applied are being developed. In classroom lessons, the learning environment centered on lectures has changed to a learning environment in which learners participate. In particular, Interactive WhiteBoard (IWB) are used as an effective digital medium for teacher-learners to collaborate for interaction. However, existing IWB are expensive to purchase and maintain, and provide various and complicated functions to operate the IWB, which imposes a heavy burden on users. In this paper, we develop a low-cost IWB that supports collaborative learning to facilitate interaction in the space where projectors are installed to improve the teaching-learning environment. In addition, it is implemented so that learning activities using the IWB are interlocked and stored in the learning management system.

• Smart Media Journal
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• v.12 no.9
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• pp.142-151
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• 2023

This study is a case study on investigating students' learning behavior in the online English reading class of an on-off course. For this purpose, a survey was conducted on a total of 77 students from an English listening and reading course and phone interview with 5 students was implemented. The findings revealed that regularity of learning interval and learning sequence need to be improved through student management in order to increase the effect of online learning. In case of lecture watching, the students are good at utilizing the strengths of taped lecture, such as using pause and repeating watching. However, more research need to be done on how to develop online lecture to enhance students' understanding of the lecture. Regarding the offline review quiz that is supposed to stimulate students into their more positive watching of the lecture, it is suggested that a few of its related elements such as the online learning period and the number of quiz questions be corrected for its better effect.

• Journal of Engineering Education Research
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• v.17 no.3
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• pp.34-41
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• 2014

In distance education, use of proper software tools can greatly enhance student's attention and learning efficiency. In such software tools, offering diverse multimedia information is one of the most critical factors. However, integration and synchronization of the various media types has been relatively difficult parts of implementation. This paper proposes a prototype system that uses a metafile and event handling mechanism for the uniform treatment of various media types. This event-level integration and synchronization of multimedia makes the implementation relatively simple. With this approach, instructor's behaviors are automatically recorded, and the instructors can freely choose and show any type of multimedia contents while lecturing. Current commercial or non-commercial lecture management systems could incorporate this approach, so that the distance education market could be expanded with richer multimedia contents.

• Smart Media Journal
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• v.11 no.11
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• pp.63-74
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• 2022

Nowadays, lithium-ion battery has become more popular around the world. Knowing when batteries reach their end of life (EOL) is crucial. Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is needed for battery health management systems and to avoid unexpected accidents. It gives information about the battery status and when we should replace the battery. With the rapid growth of machine learning and deep learning, data-driven approaches are proposed to address this problem. Extracting aging information from battery charge/discharge records, including voltage, current, and temperature, can determine the battery state and predict battery RUL. In this work, we first outlined the charging and discharging processes of lithium-ion batteries. We then summarize the proposed techniques and achievements in all published data-driven RUL prediction studies. From that, we give a discussion about the accomplishments and remaining works with the corresponding challenges in order to provide a direction for further research in this area.