• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.49-62
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• 2023

There has been a rapid increase in the creation and alteration of new malware samples which is a huge financial risk for many organizations. There is a huge demand for improvement in classification and detection mechanisms available today, as some of the old strategies like classification using mac learning algorithms were proved to be useful but cannot perform well in the scalable auto feature extraction scenario. To overcome this there must be a mechanism to automatically analyze malware based on the automatic feature extraction process. For this purpose, the dynamic analysis of real malware executable files has been done to extract useful features like API call sequence and opcode sequence. The use of different hashing techniques has been analyzed to further generate images and convert them into image representable form which will allow us to use more advanced classification approaches to classify huge amounts of images using deep learning approaches. The use of deep learning algorithms like convolutional neural networks enables the classification of malware by converting it into images. These images when fed into the CNN after being converted into the grayscale image will perform comparatively well in case of dynamic changes in malware code as image samples will be changed by few pixels when classified based on a greyscale image. In this work, we used VGG-16 architecture of CNN for experimentation.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.4
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• pp.111-121
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• 2020

This study analyzed the effects on system quality, learning commitment, and satisfaction in a mobile environment. A survey was conducted on 192 students enrolled in K University, and the research results are as follows. first, it was found that usefulness had an effect on learning commitment, but connectivity and reliability did not affect learning commitment. second, it was found that the usefulness and connectivity of the system quality had a significant effect on the satisfaction of use. third, the intermediary effect of learning immersion was verified as the connectivity, reliability, and usefulness of the system quality influence the satisfaction of use. connectivity and reliability had no mediating effect of learning commitment, and usefulness was found to play a partial mediating role in affecting user satisfaction. This study is meaningful in that it can provide a plan for improving the quality management of mobile learning and improving the learning effect by analyzing the effects on mobile learning from multiple perspectives.

• Science of Emotion and Sensibility
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• v.27 no.2
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• pp.69-80
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• 2024

The COVID-19 pandemic highlighted the importance of remote education, yet the adoption rate of AI in the educational sector remains relatively low, and studies into learners' familiarity with using AI learning robots are scarce. In response, this study analyzes the factors influencing users' familiarity with AI learning robots in a smart learning environment tailored to the untact era. To this end, social big data analysis was used to examine changes in public perception and the frequency of mentions of smart learning and AI learning robots. The results showed that positive perceptions of smart learning significantly outweigh negative ones, reflecting the convenience and improved accessibility that technology brings to education. However, there is also a considerable negative perception attached to smartphone use, which is interpreted as reflecting concerns that smartphones may disrupt learning and bring other negative aspects of technology dependence. These results indicate mixed social concerns and expectations regarding the educational use of smart learning and AI technologies. The effective introduction and use of AI learning robots, especially in smart learning environments, necessitate considering these social perceptions. This study provides foundational data for the effective implementation and use of AI learning robots in smart learning environments and suggests the need for approaches that primarily consider users' familiarity and social perceptions in the development of educational technologies.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.1
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• pp.79-91
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• 2022

In this study, the effects of self-efficacy, perceived usefulness, perceived ease of use, and depression on college students' academic persistence in the COVID-19 epidemic and the resulting non-face-to-face education situation were identified as mediating effects on learning satisfaction. In the second semester of 2020, a survey was conducted on students enrolled in a four-year university in Daegu and the data were statistically analyzed. The path coefficient was estimated by the Smart PLS bootstrap method and the significance of the path coefficient was verified. The Sobel Test was conducted to verify the mediating effect of academic continuity intention as a parameter. The research results can be summarized as follows. First, it was found that self-efficacy and perceived usefulness had a significant influence in the relationship with learning satisfaction. Second, the relationship between learning satisfaction and academic continuity intention was found to have a significant influence. Third, depression and ease of use did not show any significant influence in the relationship between learning satisfaction. Finally, a Sobel Test was conducted to verify the mediating effect of academic continuity intention with self-efficacy, usefulness, ease of use, and depression as independent variables and learning satisfaction as parameters. As a result of both regression analyses, it was found that β values decreased, and learning satisfaction had a mediating effect. As a result of this study, it is suggested that research to increase learner satisfaction and develop various contents to increase the effectiveness of education that can increase self-efficacy and perceived usefulness should be conducted in parallel. I think this study can be used as basic data in establishing measures to continue studying for college students in natural disaster situations or psychological crisis situations called COVID-19.

• Journal of The Korean Association of Information Education
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• v.3 no.2
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• pp.113-119
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• 2000

I studied a distance education model for English learning on the Internet. Basic WWW files, that contain courseware, are constructed with HTML, and functions, which are required in learning, are implemented with Java. Students and educators can access the preferred unit composed of the appropriate text, voice and image data by using a WWW browser at any time. The education system supports the automatic generation facility of English problems to practice reading and writing by making good use of the courseware data or various English text resources located on the Internet. Our system has functions to manage and control the flow of distance learning and to offer interaction between students and the system in a distributed environment. Educators can manage students' learning and can immediately be aware of who is attending and who is quitting the lesson in virtual space. Also, students and educators in different places can communicate and discuss a topic through the server. I implemented these functions, which are required in a client/server environment of distance education, with the use of Java. The URL for this system is "http://park.taegu-e.ac.kr" in the name of GAIA.

• International Journal of Contents
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• v.17 no.2
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• pp.65-76
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• 2021

The aim of this study was to explore the use of Google Classroom as a learning management system for College English. The study targeted 34 university students. They took part in various activities, such as writing reactions to video lectures, peer-editing essays, and recording video presentations, et cetera. For the study, a t-test was conducted to evaluate the English development of the students. The two essays that each student wrote were used as the data sources. The result (t=-5.854, p=.000) indicated an improvement in their English writing proficiency. In addition, a survey was conducted to gather students' feedback regarding their perceptions towards the course. The study covered five aspects of their experience: Google Classroom, language development, Quizlet, classroom experience, and essay-writing experience. From the results, students indicated a positive response to the program. The use of Google Classroom in an online learning setting accomplishes two things; it helped the students in the development of their English proficiency, and provided activities that students find interesting, which in turn stimulates their self-learning spirit.

• Journal of Educational Research in Mathematics
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• v.8 no.2
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• pp.439-452
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• 1998

In this Study, I concerned the learning-teaching of the use of letters in algebra. Our Study can be summarized as follows; First, I tried to establish the theoretical Foundation necessary for the learning-teaching of the use of letters in literal expressions. Second, I made a course of study that leads to the understanding of the meaning and the use f literals in algebraic expressions. Third, Based on this course of study, I held classes on First-grade students in middle school and I carried on an investigation their understanding of the meaning and the use of literals in algebraic expressions. Finally, I made an analysis of findings in this investigation and identified student's a better understanding of the meaning and the use of literals in algebraic expressions.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.325-333
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• 2016

The development of smart technology has brought the conversion of closed traditional e-learning contents into open flexible smart learning contents consisting of learner-centered modules, without the constraints of time and space by use of smart devices from the uniformed and passive classroom between teachers and learners. It has been demanded an open, personalized and customized teaching and learning contents of smart education and training systems according to wide supply of various smart devices. In this paper, we discuss about the status of the smart teaching and learning systems and analyze the characteristics and structure of the web contents for smart education and training systems by use of smart devices. And we propose a method how to block web contents, to extract them, and adapt personalized segments of web contents by adaptive algorithm into smart learning devices. We extract blocks from the web contents based on the smart device information and the preference information of the learners from existing web contents without the hassle of learners environment. After specifying a block priority from the extracted web contents by the adaptive segment algorithm, it can be displayed directly to the screen to fit the individual learning progress of the learners.

• Proceedings of the Korea Association of Information Systems Conference
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• 1996.11a
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• pp.217-227
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• 1996

The new field of learning control develops controllers that learn to improve their performance at executing a given task, based on experience performin this specific task. In a previous work［6］, the authors presented a theory of indirect learning control based on use of indirect adaptive control concepts employing simultaneous identification ad control. This paper develops improved indirect learning control algorithms, and studies the use of such controllers in decentralized systems. The original motivation of the learning control field was learning in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. The basic result of the paper is to show that stability of the indirect learning controllers for all subsystems when the coupling between subsystems is turned off, assures convergence to zero tracking error of the decentralized indirect learning control of the coupled system, provided that the sample time in the digital learning controller is sufficiently short.

• Asia pacific journal of information systems
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• v.31 no.1
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• pp.43-75
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• 2021

Clearly, the smartphone is increasingly playing a greater role in everyday life, thus providing opportunities to evaluate how well the use of the smartphone meets the requirements of undergraduate students in independent learning outside of a classroom setting. This study used the task-technology fit (TTF) model to explore the effectiveness of smartphone usage to interact with learning materials in independent learning outside of classrooms, the need for smartphone support, and the fit of devices to tasks as well as performance. First, the study used interviews, observation, and survey data to identify what are the most important constructs of smartphones that stimulate students to interact with learning materials in independent learning outside of classrooms. Based on the findings from the exploratory study and Task Technology Fit theory, we postulated the Navigation design, Ergonomic design, Content support, and Capacity as the essential dimension of the smartphone construct. Then, we proposed a research model and empirically tested hypotheses with the structural model analysis. The results reveal a significant positive impact of task and technology on TTF for smartphone usage to interact with learning materials in independent learning outside of classrooms; it also confirmed the TTF and performance have a direct effect on actual use.