• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.13-19
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• 2006

The instance-based learning is a machine learning technique that has proven to be successful over a wide range of classification problems. Despite its high classification accuracy, however, it has a relatively high storage requirement and because it must search through all instances to classify unseen cases, it is slow to perform classification. In this paper, we have presented a new data reduction method for instance-based learning that integrates the strength of instance partitioning and attribute selection. Experimental results show that reducing the amount of data for instance-based learning reduces data storage requirements, lowers computational costs, minimizes noise, and can facilitates a more rapid search.

• Journal of Industrial Technology
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• v.24 no.A
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• pp.99-107
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• 2004

This paper considers reinforcement learning control with the self-organizing map. Reinforcement learning uses the observable states of objective system and signals from interaction of the system and the environments as input data. For fast learning in neural network training, it is necessary to reduce learning data. In this paper, we use the self-organizing map to parition the observable states. Partitioning states reduces the number of learning data which is used for training neural networks. And neural dynamic programming design method is used for the controller. For evaluating the designed reinforcement learning controller, an inverted pendulum of the cart system is simulated. The designed controller is composed of serial connection of self-organizing map and two Multi-layer Feed-Forward Neural Networks.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.1083-1090
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• 2003

Web-based learning is currently an active area of research and a considerable number of studies have been conducted on its application in the learning environment. However, in spite of many advances in the research and development of the educational contents, questions about how the environment affects learning remains largely unanswered. In this article, we propose a Web-based learning environment to improve the educational effect. The goal of this article is not to provide a complete system to support Web-based learning but rather to describe some meaningful strategies and fundamental design concepts that utilize information technologies to support teaching and learning.

• Progress in Medical Physics
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• v.30 no.2
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• pp.39-48
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• 2019

Deep learning has been applied to various medical data. In particular, current deep learning models exhibit remarkable performance at specific tasks, sometimes offering higher accuracy than that of experts for discriminating specific diseases from medical images. The current status of deep learning applications to molecular imaging can be divided into a few subtypes in terms of their purposes: differential diagnostic classification, enhancement of image acquisition, and image-based quantification. As functional and pathophysiologic information is key to molecular imaging, this review will emphasize the need for accurate biomarker acquisition by deep learning in molecular imaging. Furthermore, this review addresses practical issues that include clinical validation, data distribution, labeling issues, and harmonization to achieve clinically feasible deep learning models. Eventually, deep learning will enhance the role of theranostics, which aims at precision targeting of pathophysiology by maximizing molecular imaging functional information.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.353-360
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• 2004

This paper considers reinforcement learning control with the self-organizing map. Reinforcement learning uses the observable states of objective system and signals from interaction of the system and environment as input data. For fast learning in neural network training, it is necessary to reduce learning data. In this paper, we use the self-organizing map to partition the observable states. Partitioning states reduces the number of learning data which is used for training neural networks. And neural dynamic programming design method is used for the controller. For evaluating the designed reinforcement learning controller, an inverted pendulum on the cart system is simulated. The designed controller is composed of serial connection of self-organizing map and two Multi-layer Feed-Forward Neural Networks.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.364-373
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• 2022

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

• Journal of Ocean Engineering and Technology
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• v.36 no.3
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• pp.194-210
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• 2022

Recently, an analysis method using machine learning for solving problems in coastal and ocean engineering has been highlighted. Machine learning models are effective modeling tools for predicting specific parameters by learning complex relationships based on a specified dataset. In coastal and ocean engineering, various studies have been conducted to predict dependent variables such as wave parameters, tides, storm surges, design parameters, and shoreline fluctuations. Herein, we introduce and describe the application trend of machine learning models in coastal and ocean engineering. Based on the results of various studies, machine learning models are an effective alternative to approaches involving data requirements, time-consuming fluid dynamics, and numerical models. In addition, machine learning can be successfully applied for solving various problems in coastal and ocean engineering. However, to achieve accurate predictions, model development should be conducted in addition to data preprocessing and cost calculation. Furthermore, applicability to various systems and quantifiable evaluations of uncertainty should be considered.

• The Journal of Korean Association of Computer Education
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• v.6 no.2
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• pp.107-120
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• 2003

In Web Based Instruction, as evaluation of learning process means individual student's learning activity, it demands data on learning time, pattern, participation, environment in a specific learning contents. The purpose of this paper is to reflect analysis results of individual student's learning status in achievement evaluation using the most suitable web log mining to settle evaluation problem of learning process, an issue in web based instruction. The contents and results of this study are as following. First, conformity item for learning status analysis is determined and web log data preprocessing is executed. Second, on the basis of web log data, I construct student's database and analyze learning status using data mining techniques.

• Journal of Industrial Technology
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• v.33 no.A
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• pp.31-39
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• 2013

There are two methods to make a distinction of deterioration of high-speed railway track. One is that an administrator checks for each attribute value of track induction data represented in graph and determines whether maintenance is needed or not. The other is that an administrator checks for monthly trend of attribute value of the corresponding section and determines whether maintenance is needed or not. But these methods have a weak point that it takes longer times to make decisions as the amount of track induction data increases. As a field of artificial intelligence, the method that a computer makes a distinction of deterioration of high-speed railway track automatically is based on machine learning. Types of machine learning algorism are classified into four type: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. This research uses supervised learning that analogizes a separating function form training data. The method suggested in this research uses SVM classifier which is a main type of supervised learning and shows higher efficiency binary classification problem. and it grasps the difference between two groups of data and makes a distinction of deterioration of high-speed railway track.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.125-133
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• 2017

Recently, as the programming education has become essential in school, discussion of how to teach programming has been important. This study performed a meta-analysis of the effect size depending on the teaching and learning method for the programming education. 78 research data selected from 45 papers were analyzed from cognitive and affective aspects according to dependent variables. The analysis from the cognitive aspect showed that there was no statistically significant difference in the effect size depending on whether or not the teaching and learning method was specified in the research paper. Meta-analysis of the research data where the teaching and learning method was designated displayed significances in CPS, PBL and Storytelling. Unlike the cognitive aspect, the analysis from the affective aspect showed that the effect size of the research data without the specified teaching and learning method was larger than those with specified teaching and learning method with a statistical significance. Meta-analysis of the data according to the teaching and learning method displayed no statistical significance. Based upon these research results, this study suggested implications for the effective programming education.