• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.107-118
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• 2024

With the seamless growth of the technology, network usage requirements are expanding day by day. The majority of electronic devices are capable of communication, which strongly requires a secure and reliable network. Network-based intrusion detection systems (NIDS) is a new method for preventing and alerting computers and networks from attacks. Machine Learning is an emerging field that provides a variety of ways to implement effective network intrusion detection systems (NIDS). Bagging and Boosting are two ensemble ML techniques, renowned for better performance in the learning and classification process. In this paper, the study provides a detailed literature review of the past work done and proposed a novel ensemble approach to develop a NIDS system based on the voting method using bagging and boosting ensemble techniques. The test results demonstrate that the ensemble of bagging and boosting through voting exhibits the highest classification accuracy of 99.98% and a minimum false positive rate (FPR) on both datasets. Although the model building time is average which can be a tradeoff by processor speed.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.25-32
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• 2022

In this paper, we propose a pet monitoring system based on deep learning using an activity recognition device. The system consists of a pet's activity recognition device, a pet owner's smart device, and a server. Accelerometer and gyroscope data were collected from an Arduino-based activity recognition device, and the number of steps was calculated. The collected data is pre-processed and the amount of activity is measured by recognizing the activity in five types (sitting, standing, lying, walking, running) through a deep learning model that hybridizes CNN and LSTM. Finally, monitoring of changes in the activity, such as daily and weekly briefing charts, is provided on the pet owner's smart device. As a result of the performance evaluation, it was confirmed that specific activity recognition and activity measurement of pets were possible. Abnormal behavior detection of pets and expansion of health care services can be expected through data accumulation in the future.

• Journal of Information Technology Applications and Management
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• v.17 no.2
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• pp.71-90
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• 2010

Electronic learning(or e-learning) has created hype for companies, universities, and other educational institutions. It has led to the phenomenal growth in the use of web-based learning and experimentation with multimedia, video conferencing, and internet-based technologies. Many researchers are interested in the factors that affect to the performance of e-learning or e-learning services. In this sense, this study is aimed at proposing e-learning system reuse prediction models in which e-learner intention to reuse influence factors(i.e., system accessibility, system stability, information clarity, information validity, self-regulated efficacy, computer self-efficacy, perceived usefulness, perceived ease of use, flow, and parental expectation) affect e-learner intention to reuse positively. A web survey was conducted for the full members of the e-learning education institute A in Seoul, Republic of Korea, an exclusive e-learning company that provides real time video lectures via the desktop conferencing system. The web survey was conducted for 20 days from November 5, 2009, through the e-learning web site of the company A. In this study, three data mining techniques were used : the multivariate discriminant analysis, CART, and C5.0 algorithm. This study was conducted to provide the e-learning service providers, e-learning operators, and contents developers with marketing and management strategies for improving the e-learning service companies, based on the data mining analysis results.

• Asia pacific journal of information systems
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• v.16 no.1
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• pp.1-21
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• 2006

e-Learning can be seen as not only one of Internet-based information technologies which can provide education services but also one of teaching-learning methods which can implement self-directed learning. Thus, for evaluation of e-Learning effectiveness, both information-technology-based learning environment and learners' abilities in self-learning and computer-using should be considered simultaneously. This study suggests a research model for evaluating the effectiveness of e-Learning, which is theoretically based on information systems success model, constructivism and self-efficacy. The model is composed of three parts: effectiveness, learning environment, and learners' self-efficacy. Effectiveness is a part of dependent variables: satisfaction and academic performance. Learning environment and learners' self-efficacy can be considered as two sets of explanation variables for effectiveness. The former consists of learning management system, learning contents, and interactions that are provided bye-Learning and the latter means learners' self-regulated efficacy and computer self-efficacy. We show validity of the model empirically by surveying the college students who have experienced e-Learning. In result, most of all hypotheses suggested in this model are accepted in low significant level.

• The Journal of Korean Association of Computer Education
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• v.9 no.1
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• pp.11-18
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• 2006

Many web-based learning systems have been developed and used widely. But it is known that web-based courses have higher drop out rate. Prior studies in classroom-based courses have shown there is a high correlation between student satisfaction and retention. This paper examines the developed web based self-regulated learning system and analyze self-regulated learning factors. We have derived key factors and their relationship that affect student satisfaction in web-based learning. The key factors are Self-evaluating, Goal setting & Planning, Seeking information, Seeking social assistance and Reviewing records. We found the key factors will help retention in web-based learning.

• Proceedings of the KAIS Fall Conference
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• 2010.05a
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• pp.463-466
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• 2010

본 논문에서는 클라우드 컴퓨팅 기반의 u-Learning 시스템 모델을 제시하고자 한다. 최근 다양한 공간에서 생성된 방대한 양의 교육 자료를 사용자들에게 보다 질 높은 교육 시스템으로 사용자들에게 제공하기 위해 클라우드 컴퓨팅 기술의 적용이 필요하다. 기존의 u-Learning 시스템은 One-Way 방식으로 자료 제공자와 사용자 사이에 신뢰성의 문제가 대두되고 있다. 따라서, 본 논문에서는 One-Way 방식의 일방적인 자료 전송이 아닌 One-Way-Reply 방식을 이용하여 사용자와 공급자간의 커뮤니케이션 과정을 보다 신뢰도 높은 정보를 제공할 수 있는 u-Learning시스템을 제안하였다.

• Journal of The Korean Association of Information Education
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• v.5 no.1
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• pp.69-78
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• 2001

Recently, the current trend for computer based learning is moving from CAI environment to WBI environment. Most web documents for WBI learning are collected by aid of search engine. Instructors use those documents as learning materials after they evaluate availability of retrieved web documents. But, this method has the following problems. First, we search repeatedly the web documents selected by instructor. Second, there is a need for another course of instruction design in order to suggest the web documents for learner. Third, it is very difficult to analyze for relevance between the web documents and test results. In this work, we suggest WAILS(Web Agent Instruction Learning System) that retrieves web documents for WBI learning and guides learning course for learners. WAILS collects web documents for WBI learning by aid of web agent. Then, instructors can evaluate them and suggest to learners by using instruction-learning generating machine. Instructors retrieve web documents and the instruction-learning design at the same time. This can facilitate WBI learning.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04a
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• pp.607-610
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• 2002

최근 5년간 e-Loaming에 대한 중요성과 웹 기반 학습의 활용성은 대부분의 기업에서 LMS(Learning Management System)의 형태로 도입을 하고 있다. 또한, 현재는 학습관리와 컨텐츠의 관리영역을 통합하고 학습 컨텐츠의 객체화를 통한 재사용성과 관리 측면을 극대화하는 노력이 이루어지고 있다. e-Learning을 활용하는 80%이상의 기업이 표준적인 메타데이터와 리파지토리를 기반으로하는 LCMS(Leaning Content Management System)형태로 전환하는 시점에서 LCMS 관린 연구가 요구된다. 본 연구에서는 학습객체를 통한 코스의 개발과 관리 배포를 위한 LCMS를 재사용 가능한 실행 모듈인 컴포넌트 기반으로 구성하고자 한다. 학습 컨텐츠 관리시스템에서의 주요 기능을 계층적으로 체계화하며, LCMS를 위한 컴포넌트 참조 아키텍처를 정의함으로써 개발의 용이성과 시간, 비용의 효율성을 보장한다. 또한, 재사용 및 공유가능한 학습객체를 통한 코스 개발로 학습 컨텐츠의 중복을 피하고 학습과정 개발의 시간 효율성을 기대한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.95-101
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• 2005

In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

• The Journal of Korean Association of Computer Education
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• v.9 no.3
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• pp.121-128
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• 2006

This study suggested the new e-learning systems based on agent to provide an adaptable learning. In Semantic Web environment, to develop an ontology and an intelligent agent is essential for an adaptable e-learning systems. Especially, to develop a reasoning engine using analysis of learning content and learners' information can offer an effective e-learning system. Therefore, we developed an applying model to an adaptable e-learning systems and the various ontologies for Semantic Web environment. Moreover, we analyzed and developed ontologies within the framework of learning domain, a learner and interface. Further, we implemented an intelligent e-learning for applying an agent's reasoning. Through this system proposed, we suggested the new e-learning systems model for Semantic Web environment.