• 제목/요약/키워드: Training based on internet

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주부들의 인터넷 쇼핑 활용 및 만족에 관한 연구 (An Analysis of Satisfaction factors on the Use of Housewives′ Internet Shopping)

  • 김미량
    • 가정과삶의질연구
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    • 제21권3호
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    • pp.123-131
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    • 2003
  • The potential of information and communication technologies has already had a profound impact on many ways of our living and society. To catch up with this trend, Korean government has demonstrated the vision for informatization called‘Cyber Korea 21'/'e-Korea’project and has fully supported the education for informatization which is one of the key factors to reduce the digital divide. But, In spite of developing a variety of training programs with different target groups and policy objectives, a digital divide remains in some cases even while Internet access and computer ownership are rising rapidly for almost all groups. For example, the noticeable divides still exists between men and women. To accelerate the process of women informatization, which we believe to be major contributors for the high quality of life for women, we need to promote full-time housewives to become aggressive information users and producers. Internet shopping, for example, might be a good starting point for motivating women to become active information users. In this paper, we present a model for explaining the factors affecting the degree of satisfaction of housewives from internet shopping. Based on data collected from a questionnaire survey from housewives in Seoul, we conclude that the perceived usefulness, ease of use and the playfulness significantly affect the level of satisfaction, but the playfulness does not directly affect the intention to revisit and purchase. In addition, we found out that the perceived usefulness is affected by efficiency, attitude and easy to access. We also provide other interesting statistical results and implications.

기계학습을 기반으로 한 인터넷 학술문서의 효과적 자동분류에 관한 연구 (The Study on the Effective Automatic Classification of Internet Document Using the Machine Learning)

  • 노영희
    • 한국도서관정보학회지
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    • 제32권3호
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    • pp.307-330
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    • 2001
  • 본 연구에서는 kNN분류기를 이용한 범주화 방법에 대한 성능 실험을 하였다. kNN분류기와 같은 대부분의 예제기반 자동 분류기법은 학습문서집단의 자질을 축소하게 되는데 자질을 몇 퍼센트 축소함으로써 높은 성능을 얻을 수 있는지를 알아보고자 하였다. 또한, kNN분류기는 학습문서집단에서 검증문서와 가장 유사한 k개의 학습문서를 찾아야 하는데, 이때 가장 적합한 k값은 얼마인지를 실험을 통하여 검증하여 보고자 하였다.

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Enhanced Network Intrusion Detection using Deep Convolutional Neural Networks

  • Naseer, Sheraz;Saleem, Yasir
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제12권10호
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    • pp.5159-5178
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    • 2018
  • Network Intrusion detection is a rapidly growing field of information security due to its importance for modern IT infrastructure. Many supervised and unsupervised learning techniques have been devised by researchers from discipline of machine learning and data mining to achieve reliable detection of anomalies. In this paper, a deep convolutional neural network (DCNN) based intrusion detection system (IDS) is proposed, implemented and analyzed. Deep CNN core of proposed IDS is fine-tuned using Randomized search over configuration space. Proposed system is trained and tested on NSLKDD training and testing datasets using GPU. Performance comparisons of proposed DCNN model are provided with other classifiers using well-known metrics including Receiver operating characteristics (RoC) curve, Area under RoC curve (AuC), accuracy, precision-recall curve and mean average precision (mAP). The experimental results of proposed DCNN based IDS shows promising results for real world application in anomaly detection systems.

Weighted DCT-IF for Image up Scaling

  • Lee, Jae-Yung;Yoon, Sung-Jun;Kim, Jae-Gon;Han, Jong-Ki
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제13권2호
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    • pp.790-809
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    • 2019
  • The design of an efficient scaler to enhance the edge data is one of the most important issues in video signal applications, because the perceptual quality of the processed image is sensitively affected by the degradation of edge data. Various conventional scaling schemes have been proposed to enhance the edge data. In this paper, we propose an efficient scaling algorithm for this purpose. The proposed method is based on the discrete cosine transform-based interpolation filter (DCT-IF) because it outperforms other scaling algorithms in various configurations. The proposed DCT-IF incorporates weighting parameters that are optimized for training data. Simulation results show that the quality of the resized image produced by the proposed DCT-IF is much higher than that of those produced by the conventional schemes, although the proposed DCT-IF is more complex than other conventional scaling algorithms.

Deep Learning-Based Inverse Design for Engineering Systems: A Study on Supervised and Unsupervised Learning Models

  • Seong-Sin Kim
    • International Journal of Internet, Broadcasting and Communication
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    • 제16권2호
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    • pp.127-135
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    • 2024
  • Recent studies have shown that inverse design using deep learning has the potential to rapidly generate the optimal design that satisfies the target performance without the need for iterative optimization processes. Unlike traditional methods, deep learning allows the network to rapidly generate a large number of solution candidates for the same objective after a single training, and enables the generation of diverse designs tailored to the objectives of inverse design. These inverse design techniques are expected to significantly enhance the efficiency and innovation of design processes in various fields such as aerospace, biology, medical, and engineering. We analyzes inverse design models that are mainly utilized in the nano and chemical fields, and proposes inverse design models based on supervised and unsupervised learning that can be applied to the engineering system. It is expected to present the possibility of effectively applying inverse design methodologies to the design optimization problem in the field of engineering according to each specific objective.

GCNXSS: An Attack Detection Approach for Cross-Site Scripting Based on Graph Convolutional Networks

  • Pan, Hongyu;Fang, Yong;Huang, Cheng;Guo, Wenbo;Wan, Xuelin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제16권12호
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    • pp.4008-4023
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    • 2022
  • Since machine learning was introduced into cross-site scripting (XSS) attack detection, many researchers have conducted related studies and achieved significant results, such as saving time and labor costs by not maintaining a rule database, which is required by traditional XSS attack detection methods. However, this topic came across some problems, such as poor generalization ability, significant false negative rate (FNR) and false positive rate (FPR). Moreover, the automatic clustering property of graph convolutional networks (GCN) has attracted the attention of researchers. In the field of natural language process (NLP), the results of graph embedding based on GCN are automatically clustered in space without any training, which means that text data can be classified just by the embedding process based on GCN. Previously, other methods required training with the help of labeled data after embedding to complete data classification. With the help of the GCN auto-clustering feature and labeled data, this research proposes an approach to detect XSS attacks (called GCNXSS) to mine the dependencies between the units that constitute an XSS payload. First, GCNXSS transforms a URL into a word homogeneous graph based on word co-occurrence relationships. Then, GCNXSS inputs the graph into the GCN model for graph embedding and gets the classification results. Experimental results show that GCNXSS achieved successful results with accuracy, precision, recall, F1-score, FNR, FPR, and predicted time scores of 99.97%, 99.75%, 99.97%, 99.86%, 0.03%, 0.03%, and 0.0461ms. Compared with existing methods, GCNXSS has a lower FNR and FPR with stronger generalization ability.

Remaining Useful Life Estimation based on Noise Injection and a Kalman Filter Ensemble of modified Bagging Predictors

  • Hung-Cuong Trinh;Van-Huy Pham;Anh H. Vo
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제17권12호
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    • pp.3242-3265
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    • 2023
  • Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.

산학협력을 통한 인력양성 성과도출 사례 연구 (A Case Study of Human Resource Nurturing Achievements through Industry-University Cooperation)

  • 한정수
    • 사물인터넷융복합논문지
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    • 제8권5호
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    • pp.41-46
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    • 2022
  • 본 논문은 인력양성의 성과에 미치는 요인별 영향을 분석한 I-O 모델을 기반으로 모션그래픽스 전공과 글로벌 호텔리어 전공을 기준으로 산학협력 인력양성 과정을 수행한 결과와 그 의미를 분석하였다. 산학협력을 통한 인력양성을 위해서는 먼저 산업체 수요조사를 통한 공통직무 개발과 기업의 환경에 맞도록 미러형 실습실을 구축하고 교과, 비교과 등 교육과정 전반에 걸친 산업체 전문가의 적극적인 참여가 있어야 맞춤형 인력양성이 가능하다는 것을 알 수 있었다. 성공적인 산학 맞춤형 인력양성을 위한 전략으로는 산업체의 교육 참여를 통한 학생-산업체 매칭, 산학 일체형 교육, 교육 품질 고도화, 맞춤형 교육인프라 구축 등 4개의 추진전략으로 추진하였다. 본 논문에서는 모션그래픽스와 호텔리어 분야에서 5년간 산학협력을 통한 인력양성 성과를 분석하였고, 성과도출을 위한 노력이 어느 정도인지를 분석 및 개선점을 제시하였다.

Automatic melody extraction algorithm using a convolutional neural network

  • Lee, Jongseol;Jang, Dalwon;Yoon, Kyoungro
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제11권12호
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    • pp.6038-6053
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    • 2017
  • In this study, we propose an automatic melody extraction algorithm using deep learning. In this algorithm, feature images, generated using the energy of frequency band, are extracted from polyphonic audio files and a deep learning technique, a convolutional neural network (CNN), is applied on the feature images. In the training data, a short frame of polyphonic music is labeled as a musical note and a classifier based on CNN is learned in order to determine a pitch value of a short frame of audio signal. We want to build a novel structure of melody extraction, thus the proposed algorithm has a simple structure and instead of using various signal processing techniques for melody extraction, we use only a CNN to find a melody from a polyphonic audio. Despite of simple structure, the promising results are obtained in the experiments. Compared with state-of-the-art algorithms, the proposed algorithm did not give the best result, but comparable results were obtained and we believe they could be improved with the appropriate training data. In this paper, melody extraction and the proposed algorithm are introduced first, and the proposed algorithm is then further explained in detail. Finally, we present our experiment and the comparison of results follows.

Fast Algorithm for Intra Prediction of HEVC Using Adaptive Decision Trees

  • Zheng, Xing;Zhao, Yao;Bai, Huihui;Lin, Chunyu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제10권7호
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    • pp.3286-3300
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    • 2016
  • High Efficiency Video Coding (HEVC) Standard, as the latest coding standard, introduces satisfying compression structures with respect to its predecessor Advanced Video Coding (H.264/AVC). The new coding standard can offer improved encoding performance compared with H.264/AVC. However, it also leads to enormous computational complexity that makes it considerably difficult to be implemented in real time application. In this paper, based on machine learning, a fast partitioning method is proposed, which can search for the best splitting structures for Intra-Prediction. In view of the video texture characteristics, we choose the entropy of Gray-Scale Difference Statistics (GDS) and the minimum of Sum of Absolute Transformed Difference (SATD) as two important features, which can make a balance between the computation complexity and classification performance. According to the selected features, adaptive decision trees can be built for the Coding Units (CU) with different size by offline training. Furthermore, by this way, the partition of CUs can be resolved as a binary classification problem. Experimental results have shown that the proposed algorithm can save over 34% encoding time on average, with a negligible Bjontegaard Delta (BD)-rate increase.