• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.1-11
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• 2023

Breast cancer is the disease that affects women the most worldwide. Due to the development of computer technology, the efficiency of machine learning has increased, and thus plays an important role in cancer detection and diagnosis. Deep learning is a field of machine learning technology based on an artificial neural network, and its performance has been rapidly improved in recent years, and its application range is expanding. In this paper, we propose a DNN-SVM hybrid model that combines the structure of a deep neural network (DNN) based on transfer learning and a support vector machine (SVM) for breast cancer classification. The transfer learning-based proposed model is effective for small training data, has a fast learning speed, and can improve model performance by combining all the advantages of a single model, that is, DNN and SVM. To evaluate the performance of the proposed DNN-SVM Hybrid model, the performance test results with WOBC and WDBC breast cancer data provided by the UCI machine learning repository showed that the proposed model is superior to single models such as logistic regression, DNN, and SVM, and ensemble models such as random forest in various performance measures.

• The Journal of Information Systems
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• v.17 no.4
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• pp.99-127
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• 2008

E-learning becomes an important part of education these days in Korea. Students had no choice but to accept the e-learning when the e-learning was used as a supplementary learning tool within a traditional class or a stand-alone distance education. This study focuses on the students' intention of e-learning acceptance for voluntary studies. In voluntary study, students have the rights to adopt the e-learning or not for their personal study. We used individual characteristics as the external variables of TAM to explain user acceptance of e-learning for voluntary studies and examined the effect of individual characteristics on user's beliefs. Research model and nine hypotheses were set up to identify the relationships among these variables based on investigations of previous researches. The theoretical model is tested with questionnaires from 420 users who accept e-learning for voluntary studies. We tested the measurement and research models by applying a structural equation modeling(SEM) approach, using the AMOS 5.0. Overall, the results provided support for the model as explaining acceptance of an e-learning system. Most path coefficients in the research model were found statistically significant. The results showed usefulness and enjoyment and subject norm were the factors affecting attitude of students using e-learning. In addition, usefulness and subject norm were the factors affecting intention of students using e-learning. The results show innovation and self-efficacy have a significant impact on user's perception of ease of use. Self-efficacy also have significant effects on user's perception of usefulness.

• Journal of the Korea Society of Computer and Information
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• v.16 no.6
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• pp.99-109
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• 2011

Ubiquitous computing technology makes an impact on the appearance of u-learning and presents an advanced direction of futuristic school education. In ubiquitous learning environments, various embedded computational devices will be pervasive and interoperable across the network for supporting the learning, so users may utilize these devices anytime anywhere. An important next step for ubiquitous learning is the introduction of context-aware learning service that employing knowledge and reasoning to understand the local context and share this information in support of intelligent learning services. However, the existing studies on design and application of ontology context model to support context-aware service in actual school environments are incomplete state. This paper, therefore, suggests a scheme of constructing ubiquitous learning space for existing school network by introducing USN to support context-aware ubiquitous learning services. This paper, also, designs an ontology based context model for ubiquitous school environments which describes context information through OWL. To determine the suitability of proposed ubiquitous learning space and ontology context model, we implement some of context-aware learning services in the ubiquitous learning environments.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• Journal of Distribution Science
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• v.21 no.4
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• pp.11-20
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• 2023

Purpose: 1) develop a learning model involving design thinking to develop creative economy innovation and the characteristics of digital entrepreneurs. 2) evaluate the impact of design thinking on creative economy innovation 3) evaluate the impact of design thinking on digital entrepreneurial ability. Research design, data and methodology: 1) develop a learning model involving design thinking in order to develop creative economy innovation and the characteristics of digital entrepreneurs. 2) Evaluating creative economy innovation involving design thinking. 3) Assessing the characteristics of digital entrepreneurs based on design concepts. Results: 1) the development of a learning model involving design thinking to develop creative economy innovation and digital entrepreneurial competency 2) The students who studied using the learning model involving a design thinking process had the highest overall scores in terms of creative economy innovation 3) The scores for the assessment of digital entrepreneurial activity for the students who studied by using the design thinking learning model were at a high level. Conclusions: The development of the design thinking learning model can encourage students to be able to develop creative economy innovations and to empower digital entrepreneurs' ability for distribution strategy. Educational institutions that would like to succeed in developing creative economy innovative and digital entrepreneurship characteristics with the support of design thinking.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.3
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• pp.872-880
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• 2016

The purpose of this study is to find the research trends relating to flipped learning through keyword network analysis. For investigating this topic, final 100 papers (removed due to overlap in all 205 papers) were selected as subjects from the result of research databases such as RISS, DBPIA, and KISS. After keyword extraction, coding, and data cleaning, we made a 2-mode network with final 202 keywords. In order to find out the research trends, frequency analysis, social network structural property analysis based on co-keyword network modeling, and social network centrality analysis were used. Followings were the results of the research: (a) Achievement, writing, blended learning, teaching and learning model, learner centered education, cooperative leaning, and learning motivation, and self-regulated learning were found to be the most common keywords except flipped learning. (b) Density was .088, and geodesic distance was 3.150 based on keyword network type 2. (c) Teaching and learning model, blended learning, and satisfaction were centrally located and closed related to other keywords. Satisfaction, teaching and learning model blended learning, motivation, writing, communication, and achievement were playing an intermediary role among other keywords.

• Design & Manufacturing
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• v.15 no.3
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• pp.1-6
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• 2021

An artificial neural network model based on a deep learning algorithm is known to be more accurate than humans in image classification, but there is still a limit in the sense that there needs to be a lot of training data that can be called big data. Therefore, various techniques are being studied to build an artificial neural network model with high precision, even with small data. The transfer learning technique is assessed as an excellent alternative. As a result, the purpose of this study is to develop an artificial neural network system that can classify burr images of light guide plate products with 99% accuracy using transfer learning technique. Specifically, for the light guide plate product, 150 images of the normal product and the burr were taken at various angles, heights, positions, etc., respectively. Then, after the preprocessing of images such as thresholding and image augmentation, for a total of 3,300 images were generated. 2,970 images were separated for training, while the remaining 330 images were separated for model accuracy testing. For the transfer learning, a base model was developed using the NASNet-Large model that pre-trained 14 million ImageNet data. According to the final model accuracy test, the 99% accuracy in the image classification for training and test images was confirmed. Consequently, based on the results of this study, it is expected to help develop an integrated AI production management system by training not only the burr but also various defective images.

• Journal of Internet Computing and Services
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• v.10 no.1
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• pp.135-143
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• 2009

The purpose of this study was to explore factors that affected the satisfaction of students' experiences in an education course, with the educational method and educational technology designed with a blended learning strategy. Blended learning is currently recognized as a good solution for the problems posed by both online and face-to-face learning, because it has features like flexibility and accessibility by using tools supporting both individualization and socialization. This study is one case that illustrates how blended learning can be applied at the university level. Subjects were 56 students who had participated in the class and responded to the survey questions. The gathered data were analyzed by using Factor Analysis and the Structural Equation Model. Based on the results of Factor Analysis, data revealed 5 factors: learning motivation, previous experience, ability to use information & technology, capability of self-regulated learning, and learning satisfaction. The results of the Structural Equation Model revealed causal relationships among the aforementioned factors as follows: (a) there was a statistically meaningful causal relationship between "learning motivation" and "capability of self-regulated learning", (b) there was a statistically meaningful casual relationship between "previous experience" and "capability of self-regulated learning", and (c) "capability of self-regulated learning" directly affected "learning satisfaction".

• Journal of Information Technology Services
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• v.10 no.3
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• pp.61-76
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• 2011

The purpose of this paper was to design and implement effective learning model for internet ethics education, following the learning principle and procedure of PBL(Problem-Based Learning) which is one of the constructivism teaching-learning theories(, and to apply it). In this learning model, online learning and face-to-face classes were systematically combined for achieving the teaching-learning goals and the main module for online learning run on Moodle, an open source LMS(Learning Management System). It is possible for learner to participate actively in creation of micro-contents and reorganize contents using various SNS(Social Network Service). The learner can achieve the learner-oriented learning and select micro-contents in order to reorganize the personalized learning contents to take advantage of SNS among learners. To examine educational effectiveness of the proposed learning model, an experimental study was conducted through the education content and method to the subjects of two classes in the second-grade of university located in OO city. 60 students(treatment group=30, control group=30) participated in the experiment. The result statistically verified that the proposed learning method is more effective in cultivating consciousness of internet ethics than the face-to-face PBL learning method. The results of this paper also showed that a lecture using blended learning is efficient in achieving learning performance and that learners responded positively(, which are indicating that the higher effectiveness of learning would be expected) by forming connectedness among learners using SNS. The results of this paper showed that a lecture using blended learning is effectiveness in achieving learning performance and that learners responded positively, which are indicating that the higher effectiveness of learning would be expected by forming connectedness among learners using SNS.

• The Journal of Korean Association of Computer Education
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• v.14 no.2
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• pp.13-22
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• 2011

The importance of algorithm education has been emphasized for creative problem-solving capability. Especially, algorithm teaching materials related with mathematics and science are under development to enhance logical thinking. However, there are not enough teaching-learning models applicable in the field of education. Therefore, this paper proposed a teaching-learning model for effective algorithm education. The teaching-learning model reflects two characteristics : an algorithm learning process is spiral, and algorithm education is based on logical thinking. Furthermore, a survey was conducted for students' satisfaction, and the result was a mixed teaching-learning model with PBL, SDL, and peer tutoring. Based on the proposed model, examples of classes for mathematics and science are suggested to show the feasibility of effective algorithm education.