• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1231-1238
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• 2021

Recently, large online shopping malls are providing image search services as well as text or category searches. However, in the case of an image search service, there is a problem in that the search service cannot be used in the absence of an image. This paper describes the development of a system that allows users to find the clothes they want through hand-drawn images of the style of clothes when they search for clothes in an online clothing shopping mall. The hand-drawing data drawn by the user increases the accuracy of matching through neural network learning, and enables matching of clothes using various object detection algorithms. This is expected to increase customer satisfaction with online shopping by allowing users to quickly search for clothing they are looking for.

• Journal of the Korea Convergence Society
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• v.9 no.2
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• pp.85-90
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• 2018

The purpose of this study is to analyze the effect of flipped learning strategy, which is an online offline convergence learning strategy becoming a hot issue, on students' learning styles..Ultimately, the goal is to analyze the academic achievements and learning attitudes by applying the flipped learning strategy considering the preferred learning styles of Korean students. By assuming that Korean students are accustomed to traditional lecture class, it is assumed that the students would have difficulty in applying the flipped learning strategy which involves information gathering and problem solving through discussion. In order to analyze whether the application of flipped learning strategy is effective, it is necessary to identify students' preferred learning style and to develop appropriate teaching strategies accordingly.

• Journal of Platform Technology
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• v.10 no.3
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• pp.51-59
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• 2022

Machine learning, which has recently innovatively developed, has become an important technology that can solve various optimization problems. In this paper, we introduce the latest research papers that solve the problem of channel sharing in heterogeneous networks using machine learning, analyze the characteristics of mainstream approaches, and present a guide to future research directions. Existing studies have generally adopted Q-learning since it supports fast learning both on online and offline environment. On the contrary, conventional studies have either not considered various coexistence scenarios or lacked consideration for the location of machine learning controllers that can have a significant impact on network performance. One of the powerful ways to overcome these disadvantages is to selectively use a machine learning algorithm according to changes in network environment based on the logical network architecture for machine learning proposed by ITU.

• The Journal of Korean Academic Society of Nursing Education
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• v.15 no.2
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• pp.194-204
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• 2009

Purpose: Level of reflective thinking of students are important factors in the area of nursing studies on web-based PBL. These factors were used as a component of learning strategies. One was to understand general recognition to web-based PBL and reaction to the use of reflective journal. The other was to investigate the level of reflective thinking was related to learning activity. Method: PBL was adopted for an online course titled 'Nursing Assessment and Intervention'. Twenty graduate students were evaluated from questionnaire, reflective journals, and individual assignments. Result: Web-based PBL was strong for self-directed learning, team activity, creative thinking, diversity of thinking, and diverse process of learning while hard time, lack of cooperation, uncertainty of outcome, and lack of time were considered as weakness. Reflective journal gave moderate help to learning activities. The learners' learning activities was the lowest in the middle level of reflective thinking. Conclusion: Generally, graduate students in college of nursing showed slightly positive attitude to PBL experience and slightly positive reaction to the learning effect of reflective journal. PBL was estimated to be valuable and meaningful. There was no relationship between the level of reflective thinking and learning activities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3868-3888
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• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.

• Journal of Information Technology Applications and Management
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• v.16 no.1
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• pp.21-36
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• 2009

As the World Wide Web (WWW) has become a major channel for information delivery, the data accumulated in the Internet increases at an incredible speed, and it derives the advances of information search technologies. It is the search engine that solves the problem of information overloading and helps people to identify relevant information. However, as search engines become a powerful tool for finding information, the opportunities of plagiarizing have increased significantly in e-Learning. In this paper, we developed an online plagiarism detection system for detecting plagiarized documents that incorporates the functions of search engines and acts in exactly the same way of plagiarizing. The plagiarism detection system uses morpheme analysis to improve the performance and sentence-based comparison to investigate document comes from multiple sources. As a result of applying this system in e-Learning, the performance of plagiarism detection was improved.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.890-903
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• 2017

The problem of determining a smooth and collision-free path with maximum possible speed for a Mobile Robot (MR) which is chasing a moving target in a dynamic environment is addressed in this paper. Genetic Network Programming with Reinforcement Learning (GNP-RL) has several important features over other evolutionary algorithms such as it combines offline and online learning on the one hand, and it combines diversified and intensified search on the other hand, but it was used in solving the problem of MR navigation in static environment only. This paper presents GNP-RL based on predicting collision positions as a first attempt to apply it for MR navigation in dynamic environment. The combination between features of the proposed collision prediction and that of GNP-RL provides safe navigation (effective obstacle avoidance) in dynamic environment, smooth movement, and reducing the obstacle avoidance latency time. Simulation in dynamic environment is used to evaluate the performance of collision prediction based GNP-RL compared with that of two state-of-the art navigation approaches, namely, Q-Learning (QL) and Artificial Potential Field (APF). The simulation results show that the proposed GNP-RL outperforms both QL and APF in terms of smooth movement and safer navigation. In addition, it outperforms APF in terms of preserving maximum possible speed during obstacle avoidance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2590-2606
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• 2017

Generally, research of classical image classification algorithms assume that training data and testing data are derived from the same domain with the same distribution. Unfortunately, in practical applications, this assumption is rarely met. Aiming at the problem, a domain adaption image classification approach based on multi-sparse representation is proposed in this paper. The existences of intermediate domains are hypothesized between the source and target domains. And each intermediate subspace is modeled through online dictionary learning with target data updating. On the one hand, the reconstruction error of the target data is guaranteed, on the other, the transition from the source domain to the target domain is as smooth as possible. An augmented feature representation produced by invariant sparse codes across the source, intermediate and target domain dictionaries is employed for across domain recognition. Experimental results verify the effectiveness of the proposed algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.2
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• pp.141-146
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• 2009

Support vector machine is a classification algorithm based on statistical learning theory. It has shown many results with good performances in the data mining fields. But there are some problems in the algorithm. One of the problems is its heavy computing cost. So we have been difficult to use the support vector machine in the dynamic and online systems. To overcome this problem we propose to use stratified sampling of statistical sampling theory. The usage of stratified sampling supports to reduce the size of training data. In our paper, though the size of data is small, the performance accuracy is maintained. We verify our improved performance by experimental results using data sets from UCI machine learning repository.

• The Journal of Korean Society for School & Community Health Education
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• v.23 no.4
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• pp.17-28
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• 2022

Objectives: This study aimed to provide the measures for improving the self-directed learning ability and the reference data for substantializing the educational programs by verifying the main factors affecting the self-directed learning ability of dental hygiene students in reality when the learners' autonomy is emphasized than ever. Methods: From June 20 to July 4, 2022, an online survey was conducted targeting total 218 dental hygiene students. The collected data was analyzed by using the SPSS Program Version 22.0. Results: First, in the results of analyzing differences in detailed items of self-directed learning ability according to the general characteristics, the 'students who entered the department of dental hygiene by considering their aptitude and interest' showed higher results than the 'students who entered the department by considering their high school record'. Second, when the academic efficacy, study immersion, and problem-solving ability of dental hygiene students were higher, their self-directed learning ability was also high. Third, the factor that had the greatest effect on self-directed learning ability of dental hygiene students was problem-solving ability, which was followed by academic efficacy and study immersion. Conclusion: Putting together the results above, in order to cultivate the problem-solving ability of dental hygiene students, it would be necessary to operate the problem-solving-centered simulation course that could foster critical thinking, interactions with others, and creative approach and solution to problems in dental medical site. It would be also possible to improve their academic efficacy by applying the learning mentoring & one-to-one learning counseling program, and also strengthening proper feedbacks for learners. Moreover, the study immersion could be strengthened by developing and operating the emotion-based learning motivation program & learning coaching program through the process of verifying the potential and growth needs of learners, exploring one's own resources through learning diagnosis/introspection, and exploring the career-related vision for strengthening the learning motivation, which could have positive effects on the improvement of self-directed learning ability.