• Journal of Fisheries and Marine Sciences Education
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• v.29 no.1
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• pp.40-50
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• 2017

This study aimed to establish and validate the path models among faculty-student interaction, self-directed learning, and institutional commitment which impacted on the academic achievement of the university students. To achieve these goals, the survey results from 488 university students in North Gyungsang Province were analyzed. Descriptive analysis, correlation analysis, t-test, and path model analysis were performed to understand the relationship among variables. First, all the variables showed positive correlations except academic achievement and institutional commitment upon the study results. Second with respect to the differences by groups, faculty-student interaction and institutional commitment demonstrated the significant differences by sex while self-directed learning and academic achievement did not. Third on the path analyses, self-directed learning influenced to academic achievement directly, while faculty-student interaction did to it by mediating with self-directed learning and institutional commitment. The results of this study suggest that faculty-student interaction, self-directed learning, and institutional commitment perceived by the university students were significant elements on their academic achievements.

• Journal of Engineering Education Research
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• v.10 no.3
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• pp.109-124
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• 2007

With the advance of the Internet and computing technologies, e-learning is now a hot issue worldwide for providing the effective learning on the cyber-space. However, most of existing e-learning contents have been developed mainly based on text, including simple multimedia elements such as images, animations, and voices. This paper suggests a method effective for the computer programming e-learning. The method is based on program visualization using flowcharts. It features the stepwise hierarchical program visualization on the level of statements, the flowchart based visualization for control constructs of languages, visualization over whole programs, visualization compared with source codes, and interaction with users. Finally, we implement a system to realize the suggested method, and execute it for an example program.

• The Journal of Korean Association of Computer Education
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• v.8 no.2
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• pp.33-40
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• 2005

It has been found that the learning method based on conventional CAI(Computer Assisted Instruction) to be inadequate and inefficient as it is designed without considering the individual learning characteristics of the learners. In order to rectify and remedy the problem, the development of an ITS(Intelligent Tutoring System) that is adequately equipped with an artificial intelligence that successfully interprets the individual learning ability characteristics through accumulated individual data is in order. This study attempts to verify the individual acquisition ability and the possible error committed by learners in the process of learning in order to present the elements to be considered for designing a successful student module that enables the effective learning through the 'learner ability grouping' for learning Electronic Calculator Architecture.

• Journal of Practical Engineering Education
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• v.8 no.1
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• pp.31-37
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• 2016

Five and above engineering courses were selected from each of exemplary international MOOC platforms, and common e-learning design strategies were drawn out through observing the courses and analyzing the course elements. By finding out both macro(platform) and micro(content) levels of designing strategies, this study suggests the direction for designing engineering courses incorporating e-learning nationally. The major trend of current e-learning design is to provide bite-sized contents rapidly created and to deploy instructional strategies for promoting student participation in learning and diverse and contextualized learning experiences.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.125-131
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• 2021

Detecting fraudulent insurance claims is difficult due to small and unbalanced data. Some research has been carried out to better cope with various types of fraudulent claims. Nowadays, technology for detecting fraudulent insurance claims has been increasingly utilized in insurance and technology fields, thanks to the use of artificial intelligence (AI) methods in addition to traditional statistical detection and rule-based methods. This study obtained meaningful results for a fraudulent insurance claim detection model based on machine learning (ML) and deep learning (DL) technologies, using fraudulent insurance claim data from previous research. In our search for a method to enhance the detection of fraudulent insurance claims, we investigated the reinforcement learning (RL) method. We examined how we could apply the RL method to the detection of fraudulent insurance claims. There are limited previous cases of applying the RL method. Thus, we first had to define the RL essential elements based on previous research on detecting anomalies. We applied the deep Q-network (DQN) and double deep Q-network (DDQN) in the learning fraudulent insurance claim detection model. By doing so, we confirmed that our model demonstrated better performance than previous machine learning models.

• International Journal of Contents
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• v.15 no.3
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• pp.32-38
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• 2019

We usually suffer from difficulties in treating or managing Big Data generated from various digital media and/or sensors using traditional mining techniques. Additionally, there are many problems relative to the lack of memory and the burden of the learning curve, etc. in an increasing capacity of large volumes of text when new data are continuously accumulated because we ineffectively analyze total data including data previously analyzed and collected. In this paper, we propose a general-purpose classifier and its structure to solve these problems. We depart from the current feature-reduction methods and introduce a new scheme that only adopts changed elements when new features are partially accumulated in this free-style learning environment. The incremental learning module built from a gradually progressive formation learns only changed parts of data without any re-processing of current accumulations while traditional methods re-learn total data for every adding or changing of data. Additionally, users can freely merge new data with previous data throughout the resource management procedure whenever re-learning is needed. At the end of this paper, we confirm a good performance of this method in data processing based on the Big Data environment throughout an analysis because of its learning efficiency. Also, comparing this algorithm with those of NB and SVM, we can achieve an accuracy of approximately 95% in all three models. We expect that our method will be a viable substitute for high performance and accuracy relative to large computing systems for Big Data analysis using a PC cluster environment.

• Korean Journal of Artificial Intelligence
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• v.11 no.1
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• pp.17-24
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• 2023

With the continuous growth in the amount of data collected and analyzed, deep learning has become increasingly popular for extracting meaningful insights from various fields. However, hardware limitations pose a challenge for achieving meaningful results with limited data. To address this challenge, this paper proposes an algorithm that leverages the characteristics of convolutional neural networks (CNNs) to reduce the size of image datasets by 20% through smoothing and shrinking the size of images using color elements. The proposed algorithm reduces the learning time and, as a result, the computational load on hardware. The experiments conducted in this study show that the proposed method achieves effective learning with similar or slightly higher accuracy than the original dataset while reducing computational and time costs. This color-centric dataset construction method using image smoothing techniques can lead to more efficient learning on CNNs. This method can be applied in various applications, such as image classification and recognition, and can contribute to more efficient and cost-effective deep learning. This paper presents a promising approach to reducing the computational load and time costs associated with deep learning and provides meaningful results with limited data, enabling them to apply deep learning to a broader range of applications.

• Journal of Haehwa Medicine
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• v.23 no.2
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• pp.1-4
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• 2015

Learning can be defined as its objects, main question for the objects, and its unique way to organize all the knowledge acquired as the results of the question. From the point of view like this, Traditional Korean Medicine(TKM) can be defined as learning for human body and its functions, health and diseases based on the theory of the Yin and Yang and of the five elements. Nowaday Many papers based on laboratory work publish for the name of scientization of TKM, but from the viewpoint of definition of learning, they have a problem that there is no basic theory. If TKM could be communicated with western natural science, it has to be solved. And oriental physiology has a same object and same questions with western physiology, so oriental physiology can be useful to make a bridge between TKM and western natural science.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.4
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• pp.352-359
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• 1988

A learning control system which should have memory elements can be designed by utilizing the concept of parameter adaptation for unknown control object system parameters and regard it as a hybrid adaptive control system. A parameter adaptive learning control system applicable to a continuous time system has been already reported. Since there have been rapid developments in digital technology, it is possible to extend a continuous time parameter adaptive learning control system concept to a discrete time case. This problem is treated in this paper. Its justfication is proved and a simulation shows that this algorithms is effective.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.9
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• pp.985-996
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• 1990

An inverse kinematic solution of a robot manipulator using multilayer perceptrons is proposed. Neural networks allow the solution of some complex nonlinear equations such as the inverse kinematics of a robot manipulator without the need for its model. However, the back-propagation (BP) learning rule for multilayer perceptrons has the major limitation of being too slow in learning to be practical. In this paper, a new algorithm named Dynamically Reconfiguring BP is proposed to improve its learning speed. It uses a modified version of Kohonen's Self-Organizing Feature Map (SOFM) to partition the input space and for each input point, select a subset of the hidden processing elements or neurons. A subset of the original network results from these selected neuron which learns the desired mapping for this small input region. It is this selective property that accelerates convergence as well as enhances resolution. This network was used to learn the parity function and further, to solve the inverse kinematic problem of a robot manipulator. The results demonstrate faster learning than the BP network.