• Journal of Distribution Science
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• v.20 no.11
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• pp.121-129
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• 2022

Purpose: Artificial intelligence is widely used, particularly in the popular neural network theory called Deep learning. The improvement of computing speed and capability expedited the progress of Deep learning applications. The application of Deep learning in education has various effects and possibilities in creating and managing educational content and services that can replace human cognitive activity. Among Deep learning, Deep fake technology is used to combine and synchronize human faces with voices. This paper will show how to develop e-Learning content videos using those technologies and open-source tools. Research design, data, and methodology: This paper proposes 4 step development process, which is presented step by step on the Google Collab environment with source codes. This technology can produce various video styles. The advantage of this technology is that the characters of the video can be extended to any historical figures, celebrities, or even movie heroes producing immersive videos. Results: Prototypes for each case are also designed, developed, presented, and shared on YouTube for each specific case development. Conclusions: The method and process of creating e-learning video contents from the image, video, and audio files using Deep fake open-source technology was successfully implemented.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.115-119
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• 2020

Recently, CCTV installations are rapidly increasing in the public and private sectors to prevent various crimes. In accordance with the increasing number of CCTVs, video-based abnormal behavior detection in control systems is one of the key technologies for safety. This is because it is difficult for the surveillance personnel who control multiple CCTVs to manually monitor all abnormal behaviors in the video. In order to solve this problem, research to recognize abnormal behavior using deep learning is being actively conducted. In this paper, we propose a model for detecting abnormal behavior based on the deep learning model that is currently widely used. Based on the abnormal behavior video data provided by AI Hub, we performed a comparative experiment to detect anomalous behavior through violence learning and fainting in videos using 2D CNN-LSTM, 3D CNN, and I3D models. We hope that the experimental results of this abnormal behavior learning model will be helpful in developing intelligent CCTV.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.3 no.2
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• pp.273-283
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• 1996

Effectiveness of the video-record learning method in teaching bowel elimination nursing skill was investigated using an experimental research methodology. Data was collected from 63 female students attending Fundamental Nursing class from a nursing college in Seoul. The subjects were randomly assigned to two groups, one is the experimental group of 29 and the other the control group of 34. The independent variable was video-record learning method and the dependent variable were the degree of knowledge achivement, nursing skill achivement, competence on practicing elimination skill, and satisfaction about the learning method. The hypotheses of the study were as following. 1) There will be significant difference between the experimental group and the control group in dependent variables. 2) There will be significant positive correlations between nursing skill achievement and other three dependent variables-interest in nursing, adaptation in nursing, and preference of nursing job. Data was analyzed using descriptive statistics, chi-square test, t-test, and Pearson's correlation coefficient with SPSS $PC^+$ program. Findings of the study are : 1) There was no significant difference between the experimental group and the control group in knowledge achievement using P<.05. 2) There was significant difference between the experimental group and the control group in nursing skill achievement using P<.05. 3) There was no significant difference between the experimental group and the control group in competence on practicing elimination skill using P<.05. 4) There was no significant difference between the experimental group and the control group in satisfaction about learning method using P<.05. 5) There was positive correlation between nursing skill achievement and the other variables but no significant difference was shown. 6) This study suggests that video-record learning method is an effective learning method for achiving basic nursing skills but is not effective in other areas such as knowledge achivement, competence in performing nursing practice, and satis-faction about the learning method. Further study with more developed research design and statistical analysis should be done to investigate the effectivenes of video-record learning method in learning basic nursing skill more accurately.

• Journal of Technologic Dentistry
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• v.42 no.4
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• pp.402-408
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• 2020

Purpose: To understand the students' level of recognition of online classes in the Department of Dental Technology and to provide the basic data for designing online classes based on the dental technology course. Methods: A survey was conducted among the students of the dental technology department. The collected data was analyzed with the SPSS ver. 25.0 program. To ensure a reliable verification, the α=0.05 significance level was used. The t-test and analysis of variance were also performed. Results: The students' level of recognition of online classes in the Department of Dental Technology is shown in the rate of recognition for video-based classes for both the theory and experiments. Students displayed high positivity with the video-based learning as it is repeated learning that is not affected by the limitations of time. In addition, video-based learning is highly beneficial in terms of convenience, satisfaction, and achievement for learning. Conclusion: Based on the results, video-based learning is a highly positive learning type for students. It was also recommended that the Department of Dental Technology should offer a post-COVID-19 online class to include the blended methods of a face-to-face class and video-based learning.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.87-96
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• 2021

The demand for e-learning through video lectures is rapidly increasing due to its diverse advantages over the traditional learning methods. This led to massive volumes of web-based lecture videos. Indexing and retrieval of a lecture video or a lecture video topic has thus proved to be an exceptionally challenging problem. Many techniques listed by literature were either visual or audio based, but not both. Since the effects of both the visual and audio components are equally important for the content-based indexing and retrieval, the current work is focused on both these components. A framework for automatic topic-based indexing and search depending on the innate content of the lecture videos is presented. The text from the slides is extracted using the proposed Merged Bounding Box (MBB) text detector. The audio component text extraction is done using Google Speech Recognition (GSR) technology. This hybrid approach generates the indexing keywords from the merged transcripts of both the video and audio component extractors. The search within the indexed documents is optimized based on the Naïve Bayes (NB) Classification and K-Means Clustering models. This optimized search retrieves results by searching only the relevant document cluster in the predefined categories and not the whole lecture video corpus. The work is carried out on the dataset generated by assigning categories to the lecture video transcripts gathered from e-learning portals. The performance of search is assessed based on the accuracy and time taken. Further the improved accuracy of the proposed indexing technique is compared with the accepted chain indexing technique.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.247-256
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• 2018

The purpose of this study is to examine effects of pre-learning and role learning using video on self-regulated learning of nursing students in fundamental nursing practice education. A nonequivalent control group was designed to conduct a pre-post test for this study. The participants were assigned to the experimental(n=84) or control group(n=76). Data was collected from March to June, 2016. The experimental group received education based on pre-learning and role learning using video for 13 weeks. On the other hand, the control group only received explanation-based education. Data was analyzed using ${\chi}^2-test$, independent t-test, and ANCOVA. There was a significant increase in rehearsal, metacognition, self-efficacy, and help seeking in the experimental group compared to those in the control group. Results of this study indicate that pre-learning and role learning using video were effective in enhancing students' ability in rehearsal, metacognition, self-efficacy, and help seeking sections.

• 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.

• Journal of Engineering Education Research
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• v.26 no.6
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• pp.79-86
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• 2023

In our research, we delved into the impact of video length assigned for pre-class assignments on students' level of engagement. What we discovered is that as the length of the video increases, student engagement tends to decrease and the time allocated for homework preparation does not significantly influence engagement, as many students tend to complete their assignments just before the due date. Interestingly, the well-known "6-minute rule" often advocated for online educational videos does not align with the dynamics of real university settings. Whether in traditional lecture-based classes or flipped learning environments, students exhibit a high degree of self-responsibility when it comes to video consumption. Our findings strongly suggest that, in the context of flipped learning, it is advisable to create videos that are shorter than 15 minutes in length.

• Educational Technology International
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• v.8 no.2
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• pp.53-71
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• 2007

This study identified the effectiveness of e-learning by comparing learning outcome in conventional face-to-face lecture with the selected e-learning methods. Two e-learning contents (animation based and video based) were developed based on the rapid prototyping model and loaded onto the learning management system (LMS), which is http://www.enaged.co.kr. Fifty-four Korean agricultural high school students were randomly assigned into three groups (face-to-face lecture, animation based e-learning, and video based e-learning group). The students of the e-learning group logged on the LMS in school computer lab and completed each e-learning. All students were required to take a pretest and posttest before and after learning under the direction of the subject teacher. A one-way analysis of covariance was administered to verify whether there was any difference between face-to-face lecture and e-learning in terms of students' learning outcomes after controlling the covariate variable, pretest score. According to the results, no differences between animation based and video based e-learning as well as between face-to-face learning and e-learning were identified. Findings suggest that the use of well designed e-learning could be worthy even in agricultural education, which stresses hands-on experience and lab activities if e-learning was used appropriately in combination with conventional learning. Further research is also suggested, focusing on a preference of e-learning content type and its relationship with learning outcome.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.115-127
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• 2023

Video stabilization is one of the camera technologies that the importance is gradually increasing as the personal media market has recently become huge. For deep learning-based video stabilization, existing methods collect pairs of video datas before and after stabilization, but it takes a lot of time and effort to create synchronized datas. Recently, to solve this problem, unsupervised learning method using only unstable video data has been proposed. In this paper, we propose a network structure that learns the stabilized trajectory only with the unstable video image without the pair of unstable and stable video pair using the Convolutional Auto Encoder structure, one of the unsupervised learning methods. Optical flow data is used as network input and output, and optical flow data was mapped into grid units to simplify the network and minimize noise. In addition, to generate a stabilized trajectory with an unsupervised learning method, we define the loss function that smoothing the input optical flow data. And through comparison of the results, we confirmed that the network is learned as intended by the loss function.