• Journal of Korean society of Dental Hygiene
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• v.16 no.6
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• pp.1023-1031
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• 2016

Objectives: The purpose of the study was to investigate the related factors of learning ethics of dental hygiene students. Methods: A self-reported questionnaire was completed by 278 dental hygiene students in G metropolitan city from June 9 to July 29, 2016. The data were analyzed by frequency analysis, percentage and stepwise multiple regression analysis using SPSS 12.0 program. The questionnaire comprised learning ethics (10 items), condition of learning ethics (10 items), reason of plagiarism (8 items), intellectual property right consciousness (8 items), internet ethics consciousness (20 items), individual ethics consciousness (2 items). Results: Condition of learning ethics was higher in mosaic plagiarism (33.9%). The main reason of plagiarism was higher in lack of time (52.7%). Related factors with the intellectual property right consciousness was use of reference (${\beta}=0.424$), internet expectancy (${\beta}=0.228$) and parental rearing attitude (${\beta}=0.229$) (Adjusted $R^2=0.336$). Related factors with the internet ethics consciousness were parental rearing attitude (${\beta}=-0.241$), academic achievements (${\beta}=0.420$), internet expectancy (${\beta}=-0.368$) and grade (${\beta}=-0.154$)(Adjusted $R^2=0.390$). Related factor with the individual ethics consciousness was academic achievements (${\beta}=0.445$) (Adjusted $R^2=0.192$). Conclusions: To increase the learning ethics and preventing plagiarism, it is necessary to have essential understanding and practice to make the liberal arts education and extracurricular program of institutions.

• Journal of Internet Computing and Services
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• v.11 no.3
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• pp.25-32
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• 2010

It takes quite amount of time to study a game because there are many game characters and different stages are exist for games. This paper used reinforcement learning algorithm for characters to learn, and so they can move intelligently. On learning early, the learning speed becomes slow. Improved sequential prediction method was used to improve the speed of learning. To compare a normal learning to an improved one, a game was created. As a result, improved character‘s ability was improved 30% on learning speed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.4
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• pp.157-162
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• 2010

The cart-pole balancing problem is a pseudo-standard benchmark problem from the field of control methods including genetic algorithms, artificial neural networks, and reinforcement learning. In this paper, we propose a novel approach by using online reinforcement learning(OREL) to solve this cart-pole balancing problem. The objective is to analyze the learning method of the OREL learning system in the cart-pole balancing problem. Through experiment, we can see that approximate faster the optimal value-function than Q-learning.

• International Journal of Internet, Broadcasting and Communication
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• v.4 no.2
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• pp.12-16
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• 2012

This paper presents a learning framework that fits the digital age - an e-Friendly PLE. The learning framework is based on the theory of connectivism which asserts that knowledge and the learning of knowledge is distributive and is not located in any given place but rather consists of the network of connections formed from experiences and interactions with a knowing community, thus, the newly empowered learner is thinking and interacting in new ways. The framework's approach to learning is based on conversation and interaction, on sharing, creation and participation, on learning not as a separate activity, but rather as embedded in meaningful activities such as games or workflows. It sees learning as an active, personal inquiry, interpretation, and construction of meaning from prior knowledge and experience with one's actual environment.

• Journal of Internet Computing and Services
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• v.11 no.2
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• pp.165-172
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• 2010

In Web-based learning systems, the techniques, as self-regulated learning, self-directed learning, are used to improve the effect of learner's study. These techniques are methods considering learner's study level but to consider the learner's study ability properly, the tailored course for learner should be applied. In this research, the learning system considering learner's study ability was proposed. To decide a learner's study ability, IRT(Item Response Theory) was applied and learning contents and question items were developed and applied by the degree of difficulty.

• Journal of Internet Computing and Services
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• v.11 no.1
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• pp.71-76
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• 2010

This research proposed the method for application of SCORM in U-learning system. That is, I proposed the API broker to connect between U-learning and API Instance of RTE that is existing SCORM based learning object interface environment. The API broker operated handling process using request port and response port between SCORM and U-learning server. For efficient operation in each service, this system has learning contents service buffer in API broker.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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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.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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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.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.394-401
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• 2024

The difference of this paper is that it analyzes the latest machine learning and deep learning tools for various tasks of program such as program search, understanding, completion, and review. In addition, the purpose of this study is to increase the understanding of various tasks of program by examining specific cases of applying various tasks of program based on tools. Recently, machine learning (ML) and deep learning (DL) technologies have contributed to automation and improvement of efficiency in various software development tasks such as program search, understanding, completion, and review. This study examines the characteristics of the latest ML and DL tools implemented for various tasks of program. Although these tools have many strengths, they still have weaknesses in generalization in various programming languages and program structures, and efficiency of computational resources. In this study, we evaluated the characteristics of these tools in a real environment.