• 한국인터넷방송통신학회논문지
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• 제12권3호
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• pp.75-81
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• 2012

인터넷이 발달하고 접할 수 있는 데이터가 폭증하면서 데이터들에서 사용자는 자신의 기호에 맞는 정보를 찾기가 점점 힘들어 진다. 추천 시스템은 사용자의 기호에 맞는 정보들을 추출하는데 큰 도움을 줄 수 있다. 본 연구는 강화 학습 알고리즘을 기반으로 한 하이브리드 추천 시스템을 사용하여 사용자의 선호도 예측에 대한 정확도를 향상 시켰다. 본 연구는 2000장의 이미지로 테스트를 진행하였다. 테스트 할 때 평균 절대 오차를 구하여 분석한 결과 제안하는 시스템이 협업적 필터링, 내용 기반 필터링, 단순 하이브리드 필터링의 성능보다 더 우수한 것으로 나타났다.

• 한국인터넷방송통신학회논문지
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• 제12권3호
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• pp.201-207
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• 2012

RAM 기반 신경망은 2진 신경망(Binary Neural Network, BNN)에 복수개의 정보 저장 비트를 두어 교육의 반복 횟수를 누적하도록 구성된 가중치를 가지지 않는(weightless) 신경회로망으로서 한 번의 교육만으로 학습이 이루어지는 효율성이 뛰어난 신경회로망이다. 지도 학습에 기반을 둔 RAM 기반 신경망은 패턴 인식 분야에는 우수한 성능을 보이는 반면, 비지도 학습에 의해 패턴을 구분해야 하는 범주화 연구에는 적합하지 않은 모델로 분류된다. 본 논문에서는 비지도 학습 알고리즘을 제안하여 RAM 기반 신경망으로 패턴 범주화를 수행한다. 제안된 비지도 학습 알고리즘에 의해 RAM 기반 신경망은 입력 패턴에 따라 자율 학습하여 스스로 범주를 생성할 수 있으며, 이를 통해 RAM 기반 신경망이 지도 학습과 비지도 학습이 모두 가능한 복합 모델임을 증명한다. 실험에 사용한 학습 패턴으로는 0에서 9까지의 오프라인 필기체 숫자로 구성된 MNIST 데이터베이스를 사용하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권1호
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• pp.109-134
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• 2018

Collaborative Filtering (CF) is widely used in recommendation field, which can be divided into rating-based CF and learning-to-rank based CF. Although many methods have been proposed based on these two kinds of CF, there still be room for improvement. Firstly, the data sparsity problem still remains a big challenge for CF algorithms. Secondly, the malicious rating given by some illegal users may affect the recommendation accuracy. Existing CF algorithms seldom took both of the two observations into consideration. In this paper, we propose a recommendation method based on listwise learning-to-rank by incorporating users' social information. By taking both ratings and order of items into consideration, the Plackett-Luce model is presented to find more accurate similar users. In order to alleviate the data sparsity problem, the improved matrix factorization model by integrating the influence of similar users is proposed to predict the rating. On the basis of exploring the trust relationship between users according to their social information, a listwise learning-to-rank algorithm is proposed to learn an optimal ranking model, which can output the recommendation list more consistent with the user preference. Comprehensive experiments conducted on two public real-world datasets show that our approach not only achieves high recommendation accuracy in relatively short runtime, but also is able to reduce the impact of malicious ratings.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권2호
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• pp.608-627
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• 2016

Wireless mesh networks (WMNs) based on IEEE 802.11s have emerged as one of the prominent technologies in multi-hop communications. However, the deployment of WMNs suffers from serious interference problem which severely limits the system capacity. Using multiple radios for each mesh router over multiple channels, the interference can be reduced and improve system capacity. Nevertheless, interference cannot be completely eliminated due to the limited number of available channels. An effective approach to mitigate interference is to apply dynamic channel switching (DCS) in WMNs. Conventional DCS schemes trigger channel switching if interference is detected or exceeds a predefined threshold which might cause unnecessary channel switching and long protocol overheads. In this paper, a P-learning based dynamic switching algorithm known as learning automaton (LA)-based DCS algorithm is proposed. Initially, an optimal channel for communicating node pairs is determined through the learning process. Then, a novel switching metric is introduced in our LA-based DCS algorithm to avoid unnecessary initialization of channel switching. Hence, the proposed LA-based DCS algorithm enables each pair of communicating mesh nodes to communicate over the least loaded channels and consequently improve network performance.

• 인터넷정보학회논문지
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• 제11권2호
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• pp.165-172
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• 2010

웹 기반 학습시스템에서 학습자의 학습효율을 높이기 위하여 자기조절학습, 자기주도학습 등의 방법이 사용되고 있다. 이러한 방법들은 학습자의 학습수준을 고려한 방법이지만 학습자의 정확한 학습능력을 고려하기 위해서는 수준별 학습의 적용이 필요하다. 본 연구에서는 학습자의 학습능력을 고려한 학습 시스템을 제시하였다. 학습자의 학습능력을 설정하기 위하여 문항반응이론을 사용하였으며, 학습 콘텐츠와 학습문항은 각 난이도에 따라 개발 및 적용되었다.

• International Journal of Internet, Broadcasting and Communication
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• 제14권2호
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• pp.192-198
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• 2022

Companies are building and utilizing their own data analysis systems according to business characteristics in the distributed cloud. However, as businesses and data types become more complex and diverse, the demand for more efficient analytics has increased. In response to these demands, in this paper, we propose an unsupervised learning-based data analysis agent to which reinforcement learning is applied for effective data analysis. The proposal agent consists of reinforcement learning processing manager and unsupervised learning manager modules. These two modules configure an agent with k-means clustering on multiple nodes and then perform distributed training on multiple data sets. This enables data analysis in a relatively short time compared to conventional systems that perform analysis of large-scale data in one batch.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.327-347
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• 2024

Edge computing is frequently employed in the Internet of Vehicles, although the computation and communication capabilities of roadside units with edge servers are limited. As a result, to perform distributed machine learning on resource-limited MEC systems, resources have to be allocated sensibly. This paper presents an Improved MADDPG algorithm to overcome the current IoV concerns of high delay and limited offloading utility. Firstly, we employ the MADDPG algorithm for task offloading. Secondly, the edge server aggregates the updated model and modifies the aggregation model parameters to achieve optimal policy learning. Finally, the new approach is contrasted with current reinforcement learning techniques. The simulation results show that compared with MADDPG and MAA2C algorithms, our algorithm improves offloading utility by 2% and 9%, and reduces delay by 29.6%.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2004년도 춘계학술발표대회논문집
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• pp.709-713
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• 2004

This Paper Presents an Internet-based Java self-learning educational system which consists of a management system named Java Web Player (JWP) and creative multimedia contents fer Java language. The JWP Is a Java application program free from security problems by the Java Web Start technologies that supports an Integrated learning environment including three Important learning Procedures: Java concept learning Process, Programming practice process and assessment process. This JWP enables the learners to achieve efficient and Interesting self-learning since the learning process is designed to enhance the multimedia capabilities on the basis of various educational technologies. On-line voice presentation and its related texts together with moving images are synchronized for efficiently conveying creative contents to learners. Furthermore, a simple and useful compiler is included in the JWP fur providing user-friendly language practice environment enabling such as coding, editing, executing and debugging Java source files on the Web. The assessment process with various items helps the learners not only to increase their academic capability but also to appreciate their current degree of understanding. Finally, simple multiple choices are given suddenly to the learners while they are studying through the JWP and the test results are displayed on the message box. The proposed system can be used for an efficient tool for learning system on the Web.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권5호
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• pp.2277-2298
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• 2019

Adaptive learning in e-learning has garnered researchers' interest. In it, learning resources could be recommended automatically to achieve a personalized learning experience. There are various ways to realize it. One of the realistic ways is adaptive learning path recommendation, in which learning resources are provided according to learners' requirements. This paper summarizes existing works and proposes an innovative approach. Firstly, a learner-centred concept map is created using graph theory based on the features of the learners and concepts. Then, the approach generates a linear concept sequence from the concept map using the proposed traversal algorithm. Finally, Learning Objects (LOs), which are the smallest concrete units that make up a learning path, are organized based on the concept sequences. In order to realize this step, we model it as a multi-objective combinatorial optimization problem, and an improved immune algorithm (IIA) is proposed to solve it. In the experimental stage, a series of simulated experiments are conducted on nine datasets with different levels of complexity. The results show that the proposed algorithm increases the computational efficiency and effectiveness. Moreover, an empirical study is carried out to validate the proposed approach from a pedagogical view. Compared with a self-selection based approach and the other evolutionary algorithm based approaches, the proposed approach produces better outcomes in terms of learners' homework, final exam grades and satisfaction.