• Journal of Internet Computing and Services
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• v.9 no.3
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• pp.159-167
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• 2008

The purpose of this study is finding factors affecting the students' use of the Cyber Home Learning Service System and exploring the direction of this system. It is based on the TAM(Technology Acceptance Model) and the result of the previous studies, six external and three internal factors influencing the sue of Cyber Home Learning Service System were extracted. The participants were 201 elementary school students in Pusan. The response of the questionnaire was gathered by online survey system. To analyze the data and the hypothesis, multiple regression and factor analysis were explored. The result indicated that (a) "usefulness" and "future-intention" affected statically to the use, (b) "usefulness" to the future-intention, (c) "subjective judgement", "fun", and "ease of use" to the usefulness, (d) "self-efficiency" and "contents quality" to the ease of use.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.29-34
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• 2023

The flow from developing a machine learning model to deploying it in a production environment suffers challenges. Efficient and reliable deployment is critical for realizing the true value of machine learning models. Bridging this gap between development and publication has become a pivotal concern in the machine learning community. FastAPI, a modern and fast web framework for building APIs with Python, has gained substantial popularity for its speed, ease of use, and asynchronous capabilities. This paper focused on leveraging FastAPI for deploying machine learning models, addressing the potentials associated with integration, scalability, and performance in a production setting. In this work, we explored the seamless integration of machine learning models into FastAPI applications, enabling real-time predictions and showing a possibility of scaling up for a more diverse range of use cases. We discussed the intricacies of integrating popular machine learning frameworks with FastAPI, ensuring smooth interactions between data processing, model inference, and API responses. This study focused on elucidating the integration of machine learning models into production environments using FastAPI, exploring its capabilities, features, and best practices. We delved into the potential of FastAPI in providing a robust and efficient solution for deploying machine learning systems, handling real-time predictions, managing input/output data, and ensuring optimal performance and reliability.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.31-36
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• 2020

This research study designed a location image collecting technology. It provides the exact location information of an image which is not given in the photo to the user. Deep learning technology analysis and collects the images. The purpose of this service system is to provide the exact place name, location and the various information of the place such as nearby recommended attractions when the user upload the image photo to the service system. Suggested system has a deep learning model that has a size of 25.3MB, and the model repeats the learning process 50 times with a total of 15,266 data, performing 93.75% of the final accuracy. This system can also be linked with various services potentially for further development.

• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.31-42
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• 2020

During the 4th Industrial Revolution, service platforms utilizing diverse contents are emerging, and research on recommended systems that can be customized to users to provide quality service is being conducted. hybrid recommendation systems that provide high accuracy recommendations are being researched in various domains, and various filtering techniques, machine learning, and deep learning are being applied to recommended systems. However, in a recommended service environment where data must be analyzed and processed real time, the accuracy of the recommendation is important, but the computational speed is also very important. Due to high level of model complexity, a hybrid recommendation system or a Deep Learning-based recommendation system takes a long time to calculate. In this paper, a Cascade-hybrid recommended algorithm is proposed that can reduce the computational time while maintaining the accuracy of the recommendation. The proposed algorithm was designed to reduce the complexity of the model and minimize the computational speed while processing sequentially, rather than using existing weights or using a hybrid recommendation technique handled in parallel. Therefore, through the algorithms in this paper, contents can be analyzed and recommended effectively and real time through services such as SNS environments or shared economy platforms.

• Educational Technology International
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• v.11 no.2
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• pp.77-103
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• 2010

The purpose of this study was to develop an instructional model using new technologies aiming to secure students' learnability and to enhance the public school values in the rural districts. The present study attempts to suggest a practical e-learning instructional and learning model named Group Dynamics- based Debate Instructional Model', which utilizes unique technology environment conditions in most. To develop the model, concepts of group dynamics and debate-based instructional models were reviewed. And in-service teachers in two public schools in a certain rural district were interviewed in order to collect and analyze their needs for a teaching and learning model with which they utilizes unique technology conditions as environment in most. Based on literature review and the need analysis, a group dynamics-based debate instructional model has been suggested in terms of conceptual model. And then expert assessment composing of five in-service teachers from the model schools was implemented twice in order to acquire the suggested model validation, followed by the model validation by a group of experts. Then a revised group dynamics-based debate instructional model has been finally suggested. The group dynamics-based debate instructional model is expected to build up members' affective connection in the process, to generate group value, or collective intelligence, and to establish positive discussion culture. Furthermore, beyond of just utilizing the existing materials, learners are encouraged to develop and collect their own materials and data such as expert's interview, or public news for their argument or refutation. In doing so, learners enhance their learnability as well as accountability, prompting self-directed learning, and establishing appropriate discussion culture resulting in positive learning outcomes.

• Analytical Science and Technology
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• v.34 no.5
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• pp.231-239
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• 2021

Detection of fire accelerants from fire residues is critical to determine whether the case was arson or accidental fire. However, to develop a standardized model for determining the presence or absence of fire accelerants was not easy because of high temperature which cause disappearance or combustion of components of fire accelerants. In this study, logistic regression, random forest, and support vector machine models were trained and evaluated from a total of 728 GC-MS analysis data obtained from actual fire residues. Mean classification accuracies of the three models were 63 %, 81 %, and 84 %, respectively, and in particular, mean AU-PR values of the three models were evaluated as 0.68, 0.86, and 0.86, respectively, showing fine performances of random forest and support vector machine models.

• Convergence Security Journal
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• v.8 no.4
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• pp.81-89
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• 2008

This paper introduces u-learning service model based on context awareness. Also, it concentrates on agent-based WPAN technology, OSGi based middleware design, and the application mechanism such as context manager/profile manager provided by agents/server. Especially, we'll introduce the meta structure and its management algorithm, which can be updated with learning experience dynamically. So, we can provide learner with personalized profile and dynamic context for seamless learning service. The OSGi middleware is applied to our meta structure as a conceptual infrastructure.

• Journal of Korean Society for Quality Management
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• v.44 no.1
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• pp.121-138
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• 2016

Purpose: Financial institute employees have various education programs for enhancing customer satisfaction. The purpose of this study is to explore critical factors that affect the service commitment in the financial institution and to propose the implication for employee's service involvement. Methods: This study is intended to identify how service quality of education, servicescape, and learning motivation affect the service commitment. The research model proposed in this study is tested via a survey of 322 employees for financial institution employees. Results: This study shows that tangibles, reliability, assurance and ambient condition, physical structure, symbolic artifacts and internal motivation, extrinsic motivation significantly influence education satisfaction. Tangibles, reliability and ambient condition, physical structure, symbolic artifacts and internal motivation significantly influence affective service orientation and that tangibles, reliability, assurance and extrinsic motivation significantly influence altruistic service orientation. It also verifies that education satisfaction affective service orientation, and altruistic service orientation positively affect service commitment. Conclusion: This study suggests critical factors to promote service commitment in the financial institute. It has focused on not only the service quality of education program, but also servicescape and learning motivation as the meaningful factors for increasing the employee's service involvement.

• Journal of Korean Society for Quality Management
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• v.45 no.1
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• pp.11-24
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• 2017

Purpose: This study is on the priority for improving students satisfaction in university educational service. It explores the dimension of service quality and analyzes the relationship among quality elements, service satisfaction, and loyalty. Methods: This paper performs empirical studies by questionnaire survey. The Timko model is used for finding the degree of possible improvement of quality elements, and structural equation and regression models are used to analyze the effect of them on service satisfaction and loyalty. Also, explanatory factor analysis is used to investigate the quality determinants. Results: The quality dimension is composed of curriculum, employment support, interaction with outsiders, start-up support, learning support, counselling, and administration service. Curriculum, learning support, and administration service are positively correlated with service satisfaction, and service satisfaction has a positive effect on loyalty. Counselling service is an attractive element, and curriculum, start-up support, and learning support are indifferent elements. Conclusion: Comprehensive analysis shows that curriculum, academic advisor, and administration service have high priorities for improving educational service satisfaction.

• Journal of the Korean Society of Systems Engineering
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• v.17 no.2
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• pp.98-105
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• 2021

As the average life expectancy is rising, the population is aging and the number of chronic diseases is increasing. This has increased the importance of healthy life and health management, and interest in mobile health services is on the rise thanks to the development of ICT(Information and communication technologies) and the smartphone use expansion. In order to meet these interests, many mobile services related to daily health are being launched in the market. Therefore, in this study, the characteristics of users who actually use mobile health services were analyzed and a predictive model applied with machine learning modeling was developed. As a result of the study, we developed a prediction model to which the decision tree and ensemble methods were applied. And it was found that the mobile health service users' continued use can be induced by providing features that require frequent visit, suggesting achievable activity missions, and guiding the sensor connection for user's activity measurement.