• Journal of The Korean Association of Information Education
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• v.7 no.2
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• pp.175-185
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• 2003

The Ministry of Education gives increasing weight to ICT-in education to produce creative and positive people in response to the demands of the 21st century's Information Age. The use of ICT in teaching-learning process aims largely to facilitate the creative thinking and diverse learning activities of students to help achieve learning objectives. The purpose of this study was to sort out what's available for teaching- learning activities from among Edunet contents for teacher and to assist teachers to have easier access to them, as ICT-in education gains in importance. And it's found that this attempt would serve to make Edunet more accessible even to teachers who are not acquainted with ICT, and learning effectiveness would be greater by integrating well- organized and well-selected ICT-in education into academic education. Furthermore, this would enable teachers to have better computer skills and teach students with more confidence.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.65-72
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• 2020

In this paper, we developed a web-based DApp system based on a private blockchain by applying machine learning techniques to automatically identify Android malicious apps that are continuously increasing rapidly. The optimal machine learning model that provides 96.2587% accuracy for Android malicious app identification was selected to the authorized experimental data, and automatic identification results for Android malicious apps were recorded/managed in the Hyperledger Fabric blockchain system. In addition, a web-based DApp system was developed so that users who have been granted the proper authority can use the blockchain system. Therefore, it is possible to further improve the security in the Android mobile app usage environment through the development of the machine learning-based Android malicious app identification block chain DApp system presented. In the future, it is expected to be able to develop enhanced security services that combine machine learning and blockchain for general-purpose data.

• Smart Media Journal
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• v.11 no.5
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• pp.48-55
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• 2022

Tomato crops are easy to expose to disease and spread in a short period of time, so late measures against disease are directly related to production and sales, which can cause damage. Therefore, there is a need for a service that enables early prevention by simply and accurately diagnosing tomato diseases in the field. In this paper, we construct a system that applies a deep learning-based model in which ImageNet transition is learned in advance to classify and serve nine classes of tomatoes for disease and normal cases. We use the input of MobileNet, ResNet, with a deep learning-based CNN structure that builds a lighter neural network using a composite product for the image set of leaves classifying tomato disease and normal from the Plant Village dataset. Through the learning of two proposed models, it is possible to provide fast and convenient services using MobileNet with high accuracy and learning speed.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.

• Convergence Security Journal
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• v.23 no.4
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• pp.61-70
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• 2023

Clustering is an unsupervised learning method that involves grouping data based on features such as distance metrics, using data without known labels or ground truth values. This method has the advantage of being applicable to various types of data, including images, text, and audio, without the need for labeling. Traditional clustering techniques involve applying dimensionality reduction methods or extracting specific features to perform clustering. However, with the advancement of deep learning models, research on deep clustering techniques using techniques such as autoencoders and generative adversarial networks, which represent input data as latent vectors, has emerged. In this study, we propose a deep clustering technique based on deep learning. In this approach, we use an autoencoder to transform the input data into latent vectors, and then construct a vector space according to the cluster structure and perform k-means clustering. We conducted experiments using the MNIST and Fashion-MNIST datasets in the PyTorch machine learning library as the experimental environment. The model used is a convolutional neural network-based autoencoder model. The experimental results show an accuracy of 89.42% for MNIST and 56.64% for Fashion-MNIST when k is set to 10.

• The Journal of the Convergence on Culture Technology
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• v.4 no.2
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• pp.197-204
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• 2018

One of the hottest issues in our 21st century is AI. Just as the automation of manual labor has been achieved through the Industrial Revolution in the agricultural society, the intelligence information society has come through the SW Revolution in the information society. With the advent of Google 'Alpha Go', the computer has learned and predicted its own machine learning, and now the time has come for the computer to surpass the human, even to the world of Baduk, in other words, the computer. Machine learning ML (machine learning) is a field of artificial intelligence. Machine learning ML (machine learning) is a field of artificial intelligence, which means that AI technology is developed to allow the computer to learn by itself. The time has come when computers are beyond human beings. Many companies use machine learning, for example, to keep learning images on Facebook, and then telling them who they are. We also used a neural network to build an efficient energy usage model for Google's data center optimization. As another example, Microsoft's real-time interpretation model is a more sophisticated translation model as the language-related input data increases through translation learning. As machine learning has been increasingly used in many fields, we have to jump into the AI industry to move forward in our 21st century society.

• Communications of Mathematical Education
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• v.20 no.3 s.27
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• pp.425-441
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• 2006

Entering upon 21th, the internet which bring the digital era, are changing the paradigm of education and cultivating creative and challenging person, which is the core of competitive power in knowledge-information society, is more emphasized than ever. To meet the needs of the present times, it has been concentrating its effort to improve learning-environment using e-Learning in the field of teaching. E-schoolbooks that were introduced recently by way of showing an example are representative case of this intention. Though many e-learning contents are being developed, the more speedily a society grow, the shorter the life of contents are. Moreover, the contents developed are impossible to use directly for tele-education system, so standard types adjusted for various kind of system are showing up. Among them the leading standard type is SCORM(Sharable Content Object Reference Medel) made in ADL(Advanced Distributed Learning) Corporation. The KERIS' Cyber Home Education System adopted this and is using it. So, in this study, we set the goal at designing and developing an e-learning contents and an experiment-focused mathematics that suit the KERIS' Cyber Home Education System on the basis of SCORM.

• Journal of the Korean School Mathematics Society
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• v.23 no.4
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• pp.491-508
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• 2020

In this study, by measuring and analyzing the motivation of prospective mathematics teachers in learning mathematics, we tried to understand the features of prospective teachers' learning motivation and find the implications of developing expertise in terms of learning motivation. Prior research related to learning motivation identifies the three elements that consist of learning motivation as values, self-efficacy, and interest. Based on these elements, a survey tool was developed to investigate the learning motivation of prospective mathematics teachers. This survey was then carried out for 120 students in the mathematics education department of a local college. In addition, the survey asked what methods prospective teachers would choose for motivating their future students. According to the results of this study, the overall motivation of prospective mathematics teachers differed by grade (academic year) and there were significant differences between grades in self-efficacy and interest factors. In addition, the prospective teachers preferred to use interesting materials rather than inform the value of learning mathematics to induce learning motivation. Therefore, it is necessary to enhance this self-efficacy and interest in learning and to provide various material to strengthen this motivation for learning.

• Informatization Policy
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• v.29 no.4
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• pp.26-42
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• 2022

In order to evaluate the usefulness of metaverse learning from the learner's point of view, this study 1) evaluated whether the expectancy-value of the class was satisfied before and after the learner used the metaverse learning platform and 2) verified factors affecting metaverse learning satisfaction with regard to the self-efficacy and expectancy-value of learners. Expectancy-value was evaluated by the learning effect, communication, class involvement, and learning attitude, whereas self-efficacy was evaluated by preference for task difficulty, self-regulation efficacy, and self-confidence. As a result of a study targeting 70 college students who applied for a few courses using the metaverse platform at a university in the northeastern part of Seoul, learners were found to have high expectations and values for learning before using the metaverse platform, but both were not statistically satisfied after use. In addition, the higher the self-efficacy of the learner, the higher the satisfaction with the metaverse learning, and statistically significant results were found in the task-difficulty preference and self-regulatory efficacy among the sub-factors of self-efficacy. There is a negative causal relationship between expectancy-value factors and satisfaction with metaverse learning. This study implies that it is a learner-centered evaluation of metaverse learning, revealing the expectancy-value effect and factors influencing the satisfaction with metaverse learning.