• Journal of KIISE:Computer Systems and Theory
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• v.33 no.12
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• pp.885-893
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• 2006

The Bayesian network(BN) has emerged in recent years as a powerful technique for handling uncertainty iii complex domains. Parameter learning of BN to find the most proper network from given data set has been investigated to decrease the time and effort for designing BN. Off-line learning needs much time and effort to gather the enough data and since there are uncertainties in real world, it is hard to get the complete data. In this paper, we propose an online learning method of Bayesian network parameters from incomplete data. It provides higher flexibility through learning from incomplete data and higher adaptability on environments through online learning. The results of comparison with Voting EM algorithm proposed by Cohen at el. confirm that the proposed method has the same performance in complete data set and higher performance in incomplete data set, comparing with Voting EM algorithm.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1353-1360
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• 2014

This study aimed to evaluate the effects of the nursing skills program, which offers online access to evidence-based skills and procedures. The nursing skills tested in this study was tracheostomy suctioning in the Mosby's Nursing Skills. The design of the study was a control group non-synchronized pre-posttest quasi-experimental research. The experimental group who utilized the Mosby's Nursing Skills had significantly higher level of knowledge and skills of trachosotmy suctioning, but not of self-efficacy. Online accessible nursing skills program was shown to be effective in improving nursing skills of students, and it is suggested to utilize the program in nursing practicum.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.630-646
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• 2022

This study examines online art platforms as a new type of informational technology and seeks to verify the impact of the user's perceived value and acceptance of new technologies on the behavioral intention about art platforms. For this, it was conducted to survey 489 users of the online art platform. Several emotional value, functional value, social value, and value for money were selected as perceived value variables, and performance expectancy, effort expectancy, and facilitating conditions were set as variables of new technology adaptation. As evident in the correlation with perceived values, emotional value and social value have a significant impact on all parameters of new technology adaptation, while functional values and value for money only affect facilitating conditions and behavioral intention, respectively. Furthermore, the impact of acceptance of new technologies confirms that performance expectancy and facilitating conditions affect behavioral intention. This study demonstrates the perceived value of online art platform users and the acceptance of new technologies. Therefore, it is expected that platform providers will be able to use it as primary data to understand and reflect user requirements.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.39-48
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• 2023

This study was conducted to provide a solution to the problem of sizing errors occurring in online purchases due to discrepancies and non-standardization in clothing sizes. This paper discusses an implementation approach for a machine learning-based recommender system capable of providing personalized sizes to online consumers. We trained multiple validated collaborative filtering algorithms including Non-Negative Matrix Factorization (NMF), Singular Value Decomposition (SVD), k-Nearest Neighbors (KNN), and Co-Clustering using purchasing data derived from online commerce and compared their performance. As a result of the study, we were able to confirm that the NMF algorithm showed superior performance compared to other algorithms. Despite the characteristic of purchase data that includes multiple buyers using the same account, the proposed model demonstrated sufficient accuracy. The findings of this study are expected to contribute to reducing the return rate due to sizing errors and improving the customer experience on e-commerce platforms.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1316-1319
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• 2002

In this paper, an online SNR estimator is derived for turbo coded multicode DS/SS systems in Nakagami fading channels. The multicode DS/SS approach is one of promising solutions to obtain higher-rate data transmission in DS/SS technologies. Turbo coding has paid much attention because of the significant improvements on error rate performances in various communication systems including multicode DS/SS systems. However, in the turbo decoding, channel state information, especially signal-to-noise ratio (SNR) at the correlator outputs, is desired in order to obtain such improvements. We evaluate the accuracy of the derived SNR estimation. It is shown that the bit error rate performance using our SNR estimation is close to the performance with perfect knowledge of channel state information.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.41-51
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• 2022

We forecast the US oil consumption level taking advantage of google trends. The google trends are the search volumes of the specific search terms that people search on google. We focus on whether proper selection of google trend terms leads to an improvement in forecast performance for oil consumption. As the forecast models, we consider the least absolute shrinkage and selection operator (LASSO) regression and the structured regularization method for large vector autoregressive (VAR-L) model of Nicholson et al. (2017), which select automatically the google trend terms and the lags of the predictors. An out-of-sample forecast comparison reveals that reducing the high dimensional google trend data set to a low-dimensional data set by the LASSO and the VAR-L models produces better forecast performance for oil consumption compared to the frequently-used forecast models such as the autoregressive model, the autoregressive distributed lag model and the vector error correction model.

• Journal of Communications and Networks
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• v.16 no.1
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• pp.36-44
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• 2014

Exploiting the energy-delay tradeoff for energy saving is critical for developing green wireless communication systems. In this paper, we investigate the delay-constrained energy-efficient packet transmission. We aim to minimize the energy consumption of multiple randomly arrived packets in an additive white Gaussian noise channel subject to individual packet delay constraints, by taking into account the practical on-off circuit power consumption at the transmitter. First, we consider the offline case, by assuming that the full packet arrival information is known a priori at the transmitter, and formulate the energy minimization problem as a non-convex optimization problem. By exploiting the specific problem structure, we propose an efficient scheduling algorithm to obtain the globally optimal solution. It is shown that the optimal solution consists of two types of scheduling intervals, namely "selected-off" and "always-on" intervals, which correspond to bits-per-joule energy efficiency maximization and "lazy scheduling" rate allocation, respectively. Next, we consider the practical online case where only causal packet arrival information is available. Inspired by the optimal offline solution, we propose a new online scheme. It is shown by simulations that the proposed online scheme has a comparable performance with the optimal offline one and outperforms the design without considering on-off circuit power as well as the other heuristically designed online schemes.

• Journal of Information Technology Services
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• v.14 no.2
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• pp.159-176
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• 2015

As a public medium and one of the big data sources that is accumulated informally and real time, online news comments or replies are considered a significant resource to understand mentalities of article readers. The comments are also being regarded as an important medium of WOM (Word of Mouse) about products, services or the enterprises. If the diffusing effect of the comments is referred to as the degrees of agreement and disagreement from an angle of WOM, figuring out which characteristics of the comments would influence the agreements or the disagreements to the comments in very early stage would be very worthwhile to establish a comment-based eWOM (electronic WOM) strategy. However, investigating the effects of the characteristics of the comments on eWOM effect has been rarely studied. According to this angle, this study aims to conduct an empirical analysis which understands the characteristics of comments that affect the numbers of agreement and disagreement, as eWOM performance, to particular news articles which address a specific product, service or enterprise per se. While extant literature has focused on the quantitative attributes of the comments which are collected by manually, this paper used text mining techniques to acquire the qualitative attributes of the comments in an automatic and cost effective manner.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.3
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• pp.790-798
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• 2016

Currently, web-based online games is becoming popular in supporting learning process due to their effective and efficient tool. However, online games have lack of security aspect, in particular due to increase in the number of personal information leakage. Since the data are transmitted over insecure channel, it will be vulnerable of being intercepted by attackers who want to exploit user's identity. This paper aims to propose an online web-based educational game, Vidyanusa which allows the students to register their personal information using a unique code, a user name and a password. It manages the users according to their schools, subject teachers and class levels. In addition, by adopting a unique code, the confidentiality of the user identity can be kept away from attackers. Moreover, in order to provide a secure data communication between client and server, Secure Socket Layer (SSL) protocol is adopted. The performance of the system after implementing SSL protocol is examined by loading a number of requests for various users. From the experiment result, it can be concluded that the SSL protocol can be applied to web-based educational system in order to offer security services and reliable connection.