• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.111-117
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• 2022

Many studies have been conducted on software fault prediction models for decades, and the models using machine learning techniques showed the best performance. Deep learning techniques have become the most popular in the field of machine learning, but few studies have used them as classifiers for fault prediction models. Some studies have used deep learning to obtain semantic information from the model input source code or syntactic data. In this paper, we produced several models by changing the model structure and hyperparameters using MLP with three or more hidden layers. As a result of the model evaluation experiment, the MLP-based deep learning models showed similar performance to the existing models in terms of Accuracy, but significantly better in AUC. It also outperformed another deep learning model, the CNN model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.205-211
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• 2024

The purpose of this study is to implement an agent that intelligently performs tracking and movement through reinforcement learning using the Unity and ML-Agents. In this study, we conducted an experiment to compare the learning performance between training one agent in a single learning simulation environment and parallel training of several agents simultaneously in a multi-learning simulation environment. From the experimental results, we could be confirmed that the parallel training method is about 4.9 times faster than the single training method in terms of learning speed, and more stable and effective learning occurs in terms of learning stability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3730-3744
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• 2020

This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.108-115
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• 2020

Blended learning is a teaching method utilizing all the advantages in 'on and off-line' learning circumstances in order to enhance the learning effect and efficiency, more than the simple use of online factors in the classroom education. In this paper, we present the realization and simulation of algorithm for the realtime evaluation of low-grade and high-grade subjects in order to implement smart e-learning system, considering a lecture intelligibility. In order to grasp the levels of student's intelligibility, we simulated a function that automatically summarizes the study contents of class given by a lecturer. Especially, in administrator mode of smart e-learning system, we suggested and simulated a system in order to help the lecturer to easily manage the student's grades, and we have provided software to tell the student's intelligibility of lecture, analyzed the rate of incorrect answers, automatic judgment of lecture intelligibility and judge the weakest subject.

• 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.

• Journal of Korean Elementary Science Education
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• v.36 no.4
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• pp.367-378
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• 2017

Because the lessons of 'our body' are based on indirect experiences and simple experiments, various methods are needed to improve the learning effect. In this study, seventeen AR contents were created to be used in five subjects in the 5th grade elementary school. The learning contents implementation were made using QCAR (QualComm's Augmented Reality) and Unity 3D (Unity 3D) program, which are augmented reality software development kits (SDKs). In order to find out the applicability, we applied the developed contents to one grade 5 classroom equipped with internet environment. Participants were asked about their perception of the program and interviewed. As a result, the developed AR learning contents appeared to be available. It was expected to help improve learning and was pointed out that improvement of internet condition and development, also, was needed expansion of various contents should be complemented.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.255-260
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• 2013

Nowadays, the intelligent education system has been studied using the self-directed learning ability. It can connect to the online virtual university and it is based on web technology that can be accessed anywhere anyplace. In order to implement the intelligent tutoring system, the student's weak and strong subjects must be first determined in real time, it proposed level learning capabilities and security algorithms in this paper. Moreover, in this paper, to implement the intelligent education tutoring system it proposed qr code and student level learning simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1228-1248
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• 2020

Steganalysis & steganography have witnessed immense progress over the past few years by the advancement of deep convolutional neural networks (DCNN). In this paper, we analyzed current research states from the latest image steganography and steganalysis frameworks based on deep learning. Our objective is to provide for future researchers the work being done on deep learning-based image steganography & steganalysis and highlights the strengths and weakness of existing up-to-date techniques. The result of this study opens new approaches for upcoming research and may serve as source of hypothesis for further significant research on deep learning-based image steganography and steganalysis. Finally, technical challenges of current methods and several promising directions on deep learning steganography and steganalysis are suggested to illustrate how these challenges can be transferred into prolific future research avenues.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1799-1813
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• 2011

Many researches indicate that programming learning could help improve problem solving skills through algorithmic thinking. But in general, programming learning has been focused on programming language features and it also gave a heavy cognitive load to learners. Therefore, this paper proposes a programming activity process to improve novice programming learners' algorithmic thinking efficiently. An experiment was performed to measure the effectiveness of the proposed programming activity process. After the experiment, the learners' perception on programming was shown to be changed, to effective activity in improving problem solving.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.2
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• pp.19-24
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• 2018

This study investigated the academic stress and the immersion in learning in relation to AR and VR assisted instructions compared to traditional approaches. To that end, 78 $8^{th}$ graders in T and S city in Gangwondo were assigned to experimental and control groups. The experimental group received the VR and AR lessons. The academic stress was measured with the pre- and post-test scores, while the immersion in learning was measured with the post-test scores. In brief, AR and VR assisted lessons made statistically significant differences in the academic stress and immersion in learning in comparison to the traditional approaches.