• Geomechanics and Engineering
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• 제37권4호
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• pp.395-418
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• 2024

The epicentral region of earthquakes is typically where liquefaction-related damage takes place. To determine the maximum distance, such as maximum epicentral distance (R_e), maximum fault distance (R_f), or maximum hypocentral distance (R_h), at which an earthquake can inflict damage, given its magnitude, this study, using a recently updated global liquefaction database, multiple ML models are built to predict the limiting distances (R_e, R_f, or R_h) required for an earthquake of a given magnitude to cause damage. Four machine learning models LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory), CNN (Convolutional Neural Network), and XGB (Extreme Gradient Boosting) are developed using the Python programming language. All four proposed ML models performed better than empirical models for limiting distance assessment. Among these models, the XGB model outperformed all the models. In order to determine how well the suggested models can predict limiting distances, a number of statistical parameters have been studied. To compare the accuracy of the proposed models, rank analysis, error matrix, and Taylor diagram have been developed. The ML models proposed in this paper are more robust than other current models and may be used to assess the minimal energy of a liquefaction disaster caused by an earthquake or to estimate the maximum distance of a liquefied site provided an earthquake in rapid disaster mapping.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제4권6호
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• pp.247-252
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• 2015

소프트웨어의 규모가 커지고 복잡성이 증가함에 따라 소프트웨어의 유지보수가 보다 중요해지고 있으며 유지보수성에 많은 영향을 미치는 요인 중 하나는 소스코드 가독성이다. 가독성의 90% 이상 영향을 끼치는 요인은 소스코드에서 사용되는 식별자들의 이름이며 이를 위한 기존 연구들에서는 클래스의 식별자로 사용된 어휘를 이용하여 식별자의 이름을 검증한다. 하지만 대부분의 관련 연구는 그 특성상 개체의 도메인 관련 특성만을 고려하게 되며 클래스 내의 어휘가 적절하지 못한 경우 적용할 수 있는 범위가 한정적이라는 한계점이 있다. 본 논문에서는 클래스의 특성을 추출하여 의사결정트리 기법을 통해 기계학습을 시킨 후 클래스 역할 모델을 생성하며 이를 이용하여 이름을 검증할 대상 클래스의 역할에 해당하는 접미사를 추천하게 되어 클래스 이름 검증 보고서를 생성한다. 본 연구 기법의 효용성을 검증하기 위해 4개의 오픈소스 프로젝트에 대하여 본 연구 기법을 적용하였고 클래스 역할 정보를 담고 있는 5개의 접미사에 대해 정확도와 재현율, ROC 곡선과 같은 지표를 제시하였다.

• 정보교육학회논문지
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• 제15권1호
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• pp.1-10
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• 2011

교육 현장에서 프로그래밍 교육은 고등 사고력 향상을 위한 학습보다는 프로그래밍의 개념이나 기초 문법의 주입 및 단순 반복으로 진행되고 있다. 따라서 학습자들이 프로그래밍 교육에 대해 긍정적인 흥미를 가지고 적극 참여하여 교육적인 효과를 거둘 수 있는 새로운 프로그래밍 교육 방안에 대한 모색이 필요하다. 스크래치 EPL(Educational Programming Languages)은 블록 쌓기를 통해 프로그래밍이 가능한 직관적인 언어로 초등학생들도 보다 쉽게 프로그래밍을 이해할 수 있다. 스크래치의 이런 특징은 프로그래밍 교육에 대한 학습자의 학습 몰입(Flow)에 긍정적인 영향을 미칠 수 있다. 따라서 본 연구에서는 프로그래밍 교육의 새로운 방안으로 스크래치 프로그래밍 교육을 진행하고 초등학생의 학습 몰입과 프로그래밍 능력에 미치는 영향에 대해 검증하고자 한다. 본 연구의 결과 스크래치 프로그래밍 교육이 학습자의 전반적인 학습 몰입 수준 향상에 긍정적인 효과가 있음을 알 수 있다.

• 한국컴퓨터정보학회논문지
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• 제19권11호
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• pp.73-79
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• 2014

소프트웨어 교육의 중요성이 부각되는 가운데, 다양한 교육 방법 중 언플러그드 활동이 주목받고 있다. 컴퓨터를 통하지 않고 컴퓨터과학의 원리를 학습하는 측면에서 교육의 효과성이나 용이성 등에서 좋은 평가를 받고 있다. 이러한 장점을 높이고 접근성을 증대하기 위하여 언플러그드 학습 콘텐츠를 HTML5 기반의 웹 환경에서 구현될 수 있는 도구를 제안한다. 제안된 도구는 별도의 응용프로그램이나 플러그인 없이 웹브라우저 수준에서 운영되어 도입에 대한 진입장벽이 낮고 웹 환경에서 상호작용성을 높이기 위한 학습자 중심의 콘텐츠로 학교 수업환경에 사용하기 용이하게 구성되었다.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.79-88
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• 2024

The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Naïve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권1호
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• pp.99-118
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• 2013

Electrocardiograms (ECGs) are widely used by clinicians to identify the functional status of the heart. Thus, there is considerable interest in automated systems for real-time monitoring of arrhythmia. However, intra- and inter-patient variability as well as the computational limits of real-time monitoring poses significant challenges for practical implementations. The former requires that the classification model be adjusted continuously, and the latter requires a reduction in the number and types of ECG features, and thus, the computational burden, necessary to classify different arrhythmias. We propose the use of adaptive learning to automatically train the classifier on up-to-date ECG data, and employ adaptive feature selection to define unique feature subsets pertinent to different types of arrhythmia. Experimental results show that this hybrid technique outperforms conventional approaches and is therefore a promising new intelligent diagnostic tool.

• Computers and Concrete
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• 제25권6호
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• pp.565-574
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• 2020

Over the years, several machine learning approaches have been proposed and utilized to create a prediction model for the high-performance concrete (HPC) slump flow. Despite HPC is a highly complex material, predicting its pattern is a rather ambitious process. Hence, choosing and applying the correct method remain a crucial task. Like some other problems, prediction of HPC slump flow suffers from abnormal attributes which might both have an influence on prediction accuracy and increases variance. In recent years, different studies are proposed to optimize the prediction accuracy for HPC slump flow. However, more state-of-the-art regression algorithms can be implemented to create a better model. This study focuses on several methods with different mathematical backgrounds to get the best possible results. Four well-known algorithms Support Vector Regression, M5P Trees, Random Forest, and MLPReg are implemented with optimum parameters as base learners. Also, redundant features are examined to better understand both how ingredients influence on prediction models and whether possible to achieve acceptable results with a few components. Based on the findings, the MLPReg algorithm with optimum parameters gives better results than others in terms of commonly used statistical error evaluation metrics. Besides, chosen algorithms can give rather accurate results using just a few attributes of a slump flow dataset.

• ETRI Journal
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• 제37권1호
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• pp.77-87
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• 2015

In wireless networks, cooperation is necessary for many protocols, such as routing, clock synchronization, and security. It is known that cooperator nodes suffer greatly from problems such as increasing energy consumption. Therefore, rational nodes have no incentive to cooperatively forward traffic for others. A rational node is different from a malicious node. It is a node that makes the best decision in each state (cooperate or non-cooperate). In this paper, game theory is used to analyze the cooperation between nodes. An evolutionary game has been investigated using two nodes, and their strategies have been compared to find the best one. Subsequently, two approaches, one based on a genetic algorithm (GA) and the other on learning automata (LA), are presented to incite nodes for cooperating in a noisy environment. As you will see later, the GA strategy is able to disable the effect of noise by using a big enough chromosome; however, it cannot persuade nodes to cooperate in a noisefree environment. Unlike the GA strategy, the LA strategy shows good results in a noise-free environment because it has good agreement in cooperation-based strategies in both types of environment (noise-free and noisy).

• International Journal of Contents
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• 제7권1호
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• pp.14-22
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• 2011

Temporal Sign Language Recognition (TSLR) from hand motion is an active area of gesture recognition research in facilitating efficient communication with deaf people. TSLR systems consist of two stages: a motion sensing step which extracts useful features from signers' motion and a classification process which classifies these features as a performed sign. This work focuses on two of the research problems, namely unknown time varying signal of sign languages in feature extraction stage and computing complexity and time consumption in classification stage due to a very large sign sequences database. In this paper, we propose a combination of Dynamic Time Warping (DTW) and application of the Single hidden Layer Feedforward Neural networks (SLFNs) trained by Extreme Learning Machine (ELM) to cope the limitations. DTW has several advantages over other approaches in that it can align the length of the time series data to a same prior size, while ELM is a useful technique for classifying these warped features. Our experiment demonstrates the efficiency of the proposed method with the recognition accuracy up to 98.67%. The proposed approach can be generalized to more detailed measurements so as to recognize hand gestures, body motion and facial expression.

• 한국멀티미디어학회논문지
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• 제18권12호
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• pp.1432-1438
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• 2015

We present a methodology to detect moving shadows in video sequences, which is considered as a challenging and critical problem in the most visual surveillance systems since 1980s. While most previous moving shadow detection methods used hand-crafted features such as chromaticity, physical properties, geometry, or combination thereof, our method can automatically learn features to classify whether image segments are shadow or foreground by using a deep learning architecture. Furthermore, applying Markov Random Field enables our system to refine our shadow detection results to improve its performance. Our algorithm is applied to five different challenging datasets of moving shadow detection, and its performance is comparable to that of state-of-the-art approaches.