• International Journal of Internet, Broadcasting and Communication
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• 제15권3호
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• pp.166-175
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• 2023

Reinforcement learning can be applied to a wide variety of problems. However, the fundamental limitation of reinforcement learning is that it is difficult to derive an answer within a given time because the problems in the real world are too complex. Then, with the development of neural network technology, research on deep reinforcement learning that combines deep learning with reinforcement learning is receiving lots of attention. In this paper, two types of neural networks are combined with reinforcement learning and their characteristics were compared and analyzed with existing value-based reinforcement learning algorithms. Two types of neural networks are FNN and CNN, and existing reinforcement learning algorithms are SARSA and Q-learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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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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• 제15권2호
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• pp.133-143
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• 2023

In this study, we propose a big data set of strawberry pests collected directly for diagnosis model learning and an automatic pest diagnosis model architecture based on deep learning. First, a big data set related to strawberry pests, which did not exist anywhere before, was directly collected from the web. A total of more than 12,000 image data was directly collected and classified, and this data was used to train a deep learning model. Second, the deep-learning-based automatic pest diagnosis module is a module that classifies what kind of pest or disease corresponds to when a user inputs a desired picture. In particular, we propose a model architecture that can optimally classify pests based on a convolutional neural network among deep learning models. Through this, farmers can easily identify diseases and pests without professional knowledge, and can respond quickly accordingly.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2006년도 춘계 종합학술대회 논문집
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• pp.35-38
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• 2006

본 연구는 사이버가정학습체제의 도입으로 인터넷 학습에 대한 관심이 고조되고 있는 초등학교 학생들을 위한 수학교과에서의 게임형 콘텐츠 설계 및 개발에 대한 전략을 연구하였다. 게임형 콘텐츠는 학습내용 및 활동 설계시 다양한 게임적 요소를 활용하여 학습자들의 흥미를 지속적으로 유발하고 즐겁게 학습할 수 있도록 유도하는 콘텐츠 유형으로서, 재미를 추구하는 면에서는 일반 게임과 동일하지만, 상대를 이기는 것이 목적인 일반 게임과 달리 교육용 게임에서는 학습 내용을 효과적으로 습득하는 것을 근본 목적으로 한다. 본 논문에서는 초등수학에서 게임형 콘텐츠의 설계시 필요한 고려해야 할 설계전략과 그에 따른 개발사례를 통해 향후 게임형 콘텐츠의 활용시 필요한 시사점들을 분석해보기로 한다.

• 한국산업정보학회논문지
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• 제12권5호
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• pp.71-85
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• 2007

최근 인터넷상에서 청소년들의 잘못된 행동이 증가하면서 인터넷 윤리 교육이 강조되고 있다. 그에 따라 인터넷 윤리 교육 방안에 대한 많은 연구가 이루어지고 있다. 하지만 교육 현장에서는 텍스트 위주의 자료로 주입식 수업을 하여, 청소년들의 윤리 의식을 고취시키기에는 역부족이다. 본 연구에서는 윤리교육 학습자의 능동적인 참여를 이끌어 내기 위해, 다양한 멀티미디어 매체를 이용한 웹 기반 학습 모형 및 학습시스템을 개발하고, 학교 현장에서 실험 적용하여 그 효용성을 검증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.16-30
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• 2023

The elderly population is increasing owing to a low fertility rate and an aging population. In addition, life expectancy is increasing, and the advancement of medicine has increased the importance of health to most people. Therefore, government and companies are developing and supporting smart healthcare, which is a health-related product or industry, and providing related services. Moreover, with the development of the Internet, many people are managing their health through online searches. The most convenient way to achieve such management is by consuming nutritional supplements or seasonal foods to prevent a nutrient deficiency. However, before implementing such methods, knowing the nutrient status of the individual is difficult, and even if a test method is developed, the cost of the test will be a burden. To solve this problem, we developed a questionnaire related to nutrient classification twice, based upon which an adaptive algorithm was designed. This algorithm was designed as a machine learning based algorithm for nutrient classification and its accuracy was much better than the other machine learning algorithm.

• 대한임베디드공학회논문지
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• 제11권3호
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• pp.153-162
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• 2016

This paper proposes an optimal dynamic resource allocation method in IoT (Internet of Things) parking guidance system using Q-learning resource allocation model. In the proposed method, a resource allocation using a forecasting model based on Q-learning is employed for optimal utilization of parking guidance system. To demonstrate efficiency and availability of the proposed method, it is verified by computer simulation and practical testbed. Through simulation results, this paper proves that the proposed method can enhance total throughput, decrease penalty fee issued by SLA (Service Level Agreement) and reduce response time with the dynamic number of users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5427-5445
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• 2019

While deep neural networks have achieved remarkable performance in representation learning, a huge amount of labeled training data are usually required by supervised deep models such as convolutional neural networks. In this paper, we propose a new representation learning method, namely generative adversarial networks (GAN) based bagging deep convolutional autoencoders (GAN-BDCAE), which can map data to diverse hierarchical representations in an unsupervised fashion. To boost the size of training data, to train deep model and to aggregate diverse learning machines are the three principal avenues towards increasing the capabilities of representation learning of neural networks. We focus on combining those three techniques. To this aim, we adopt GAN for realistic unlabeled sample generation and bagging deep convolutional autoencoders (BDCAE) for robust feature learning. The proposed method improves the discriminative ability of learned feature embedding for solving subsequent pattern recognition problems. We evaluate our approach on three standard benchmarks and demonstrate the superiority of the proposed method compared to traditional unsupervised learning methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2188-2203
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• 2021

With the rapid development of mobile Internet, smart phones have been widely popularized, among which Android platform dominates. Due to it is open source, malware on the Android platform is rampant. In order to improve the efficiency of malware detection, this paper proposes deep learning Android malicious detection system based on behavior features. First of all, the detection system adopts the static analysis method to extract different types of behavior features from Android applications, and extract sensitive behavior features through Term frequency-inverse Document Frequency algorithm for each extracted behavior feature to construct detection features through unified abstract expression. Secondly, Long Short-Term Memory neural network model is established to select and learn from the extracted attributes and the learned attributes are used to detect Android malicious applications, Analysis and further optimization of the application behavior parameters, so as to build a deep learning Android malicious detection method based on feature analysis. We use different types of features to evaluate our method and compare it with various machine learning-based methods. Study shows that it outperforms most existing machine learning based approaches and detects 95.31% of the malware.

• Journal of Information Technology Applications and Management
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• 제12권3호
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• pp.57-80
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• 2005

This study defined the notion of general idea on m-learning as based upon e-Learning and mobile internet related literature review and identified the m-Learning distinctive features. Also, this study has searched for factors that are expected to influence the use intended for m-Learning from self-regulated learning, which is acknowledged to be a useful method for learning accomplishment in education field, in order to measure the relationship between learners' motivation and use intention. Then it has empirically validated the conceptual model based on Davis' TAM (Technology Acceptance Model) As a result, self-efficacy, self-determination, interest, contents quality, time management, help seeking, and Peer study are factors affecting Perceived usefulness. Also self-efficacy, self-determination, interest, contents qualify, time management, and peer study are factors affecting perceived ease of use. Finally both perceived usefulness and perceived ease of use are significant factors affecting use intention.