• Journal of Service Research and Studies
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• v.11 no.4
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• pp.67-76
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• 2021

The importance of smart learning is increasing as the speed of development of non-face-to-face services increases due to the influence of COVID-19. This study is the user satisfaction evaluation model that utilizes the causal relationship between variables used for evaluation, focusing on the usability evaluation results of the learning disability intervention service (www.basic-edu.net) according to the need to evaluate the use satisfaction of the smart learning system. To this end, theoretical studies were conducted on smart learning and learning disability intervention services, www.basic-edu.net, usability evaluation of learning disability intervention systems, and use satisfaction evaluation models. And based on the results, a hypothesis was presented on the user satisfaction evaluation model of the smart learning system. The experimental method allowed 40 students and parents across the country to use the www.basic-edu.net service and was evaluated for its usability. In addition, using this data, the hypothesis was verified using regression analysis based on four variables: ease of use, interest, self-learning, and satisfaction with use. As a result of the hypothesis verification, it was found that the causal relationship of all hypotheses from H1 to H4 was significant.

• Information Systems Review
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• v.22 no.1
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• pp.59-72
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• 2020

Artificial Intelligence is establishing itself as a familiar tool from an intractable concept. In this trend, financial sector is also looking to improve the problem of existing system which includes Fraud Detection System (FDS). It is being difficult to detect sophisticated cyber financial fraud using original rule-based FDS. This is because diversification of payment environment and increasing number of electronic financial transactions has been emerged. In order to overcome present FDS, this paper suggests 3 types of artificial intelligence models, Generative Adversarial Network (GAN), Deep Neural Network (DNN), and Convolutional Neural Network (CNN). GAN proves how data imbalance problem can be developed while DNN and CNN show how abnormal financial trading patterns can be precisely detected. In conclusion, among the experiments on this paper, WGAN has the highest improvement effects on data imbalance problem. DNN model reflects more effects on fraud classification comparatively.

• 한국정보교육학회:학술대회논문집
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• 2008.01a
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• pp.263-268
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• 2008

학교의 학습 과정에 있어서 학습자 간의 학습 능력의 차이는 존재하며 이를 해결하기 위해 교육과정에서는 개별 학습과 수준별 학습을 권장한다. 유무선 인터넷을 통한 수준별 학습은 최근에 많은 연구가 되고 있는 u-러닝(Ubiquitous Learning) 환경에도 부합하며 학습자 개개인이 자신의 속도와 수준에 맞게 자기주도적으로 학습을 하기에 알맞은 방법이라 할 수 있다. 이에 본 연구에서는 학습자가 시간과 공간에 구애받지 않고 자율적으로 수준에 맞게 학습할 수 있는 수준별 학습 시스템을 설계하였다. 특히 시스템에 체계성을 더하기 위해 개별화 학습 체제 중에서 과거 많은 연구를 통해 그 효과성이 입증된 Keller의 PSI(Personal System of Instruction) 이론을 활용하여 시스템의 각 과정을 설계하였다. 본 시스템의 장점은 다음과 같다. 첫째, 학습자가 원하는 시간과 공간에서 자신의 속도에 맞게 학습할 수 있으므로 자기주도적인 학습 능력을 기를 수 있다. 둘째, 시스템 구성상 평가를 통해 일정한 기준에 미달하면 목표에 도달할 때까지 계속 학습하고 도전해야 하므로 궁극적으로 완전학습에 도달할 수 있다. 셋째, 제한된 교실 상황에서 벗어나 온라인에서의 학습 지원이 가능하므로 학습자의 개인차에 따른 수준별 학습을 관리하고 책임져야 하는 교사의 부담을 덜어준다.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.75-81
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• 2024

In this paper, we proposes a method for real-time processing of inter-floor noise problems by embedding TinyML, which includes a deep learning model, into ultra-low-power systems. The reason this method is feasible is because of lightweight deep learning model technology, which allows even systems with small computing resources to perform inference autonomously. The conventional method proposed to solve inter-floor noise problems was to send data collected from sensors to a server for analysis and processing. However, this centralized processing method has issues with high costs, complexity, and difficulty in real-time processing. In this paper, we address these limitations by employing On-Sensor AI using TinyML. The method presented in this paper is simple to install, cost-effective, and capable of processing problems in real-time.

• The Journal of Korean Association of Computer Education
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• v.7 no.6
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• pp.69-76
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• 2004

E-learning is the application of e-business technology and services to teaching and learning. It use of new multimedia technologies and Internet to improved the quality of learning by facilitating access to remote resources and services. In this paper, we show a web-based test system, which is carefully designed and implemented based on the real TOEFL CBT. The system consists of a contents delivery mechanism, computer-adaptive test algorithm, and review engine. In this papepr, we describe design and implementing issues of web-based test systems.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.495-496
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• 2023

최근 보급되고 있는 스크린 야구장을 많은 이용자가 단순한 타격만을 하고 피드백이 없이 일회성으로 이용하고 있고 이용자의 타격 자세를 평가해주는 기능을 제공하지 않고 있다. 부정확한 자세로 타격을 하게 되면 부상의 위험도 있고, 타격 실력도 향상될 수 없다. 따라서 이용자가 올바른 타격자세를 취할 수 있도록 자세를 평가 해주는 시스템이 필요하다. 본 논문에서는 구글의 미디어 파이프와 딥러닝 기술을 활용하여 타격 자세 영상을 인식하여 타격 자세를 평가해주는 시스템을 개발하였다. 제안한 시스템은 사전에 다양한 영상을 LSTM 알고리즘으로 학습하여 이용자의 타격자세를 4개 등급으로 평가해준다. 이를 활용하여 스크린 야구장에서 카메라만 설치하여 간단하게 사용 가능하며 이용자들이 타격 자세를 자체 평가할 수 있다.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.131-141
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• 2017

This paper analyzed class satisfaction and perceived learning achievement to the interaction type on e-learning in university. To achieve the study's objective, one course with and another course without learner-instructor interactions were selected. A total of184 student-respondents completed the questionnaire. Accordingly, more learner-content and learning-system interactions were noted in the course with learner-instructor interactions. Moreover, a correlation was observed between interaction, class satisfaction, and learning achievement. Learner-instructor interactions indicated the highest effect on both educational satisfaction and perceived learning achievement, followed by learner-system interactions on class satisfaction, and by learner-instructor interactions on learning achievement. Recommendations were then formulated based on the foregoing findings. First, workshops or training focusing on content development and on how to present the course should be provided to the instructors. Second, learner-instructor interactions should be activated in the course through various means. In this study, although learner-learner interactions was not given focus, future studied should delve into how learner-learner interaction should be activated and considered.

• Journal of The Korean Association of Information Education
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• v.13 no.3
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• pp.303-312
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• 2009

In order to establish an air force e-learning system, we analysed previous researches on air force education systems and conducted a survey on 3,597 air force trainees. Most of trainees have little experience on e-learning and do not understand it. But if they were given an opportunity, they would like to take a e-learning course. They had a lot of requests about function improvement for learning management systems, preferred individual education rather than collective education, and did not wanted to take an e-learning class at a computer laboratory. In this study, we found four elements that need to establish e-learning system for air force. They are content, trainer, trainee and service. Also, we suggested improvement plans for each elements.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.87-92
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• 2019

This paper describes the pattern analysis for data of the factory energy consumption by using machine learning method. While usual statistical methods or approaches require specific equations to represent the physical characteristics of the plant, machine learning based approach uses historical data and calculate the result effectively. Although rule-based approach calculates energy usage with the physical equations, it is hard to identify the exact equations that represent the factory's characteristics and hidden variables affecting the results. Whereas the machine learning approach is relatively useful to find the relations quickly between the data. The factory has several components directly affecting to the electricity consumption which are machines, light, computers and indoor systems like HVAC (heating, ventilation and air conditioning). The energy loads from those components are generated in real-time and these data can be shown in time-series. The various sensors were installed in the factory to construct the database by collecting the energy usage data from the components. After preliminary statistical analysis for data mining, time-series clustering techniques are applied to extract the energy load pattern. This research can attributes to develop Factory Energy Management System (FEMS).