• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of Korea Water Resources Association
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• v.51 no.8
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• pp.703-711
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• 2018

Water distribution system (WDS) pipe bursts are caused from excessive pressure, pipe aging, and ground shift from temperature change and earthquake. Prompt detection of and response to the failure event help prevent large-scale service interruption and catastrophic sinkhole generation. To that end, this study proposes a improved Western Electric Company (WECO) method to improve the detection effectiveness and efficiency of the original WECO method. The original WECO method is an univariate Statistical Process Control (SPC) technique used for identifying any non-random patterns in system output data. The improved WECO method multiples a threshold modifier (w) to each threshold of WECO sub-rules in order to control the sensitivity of anomaly detection in a water distribution network of interest. The Austin network was used to demonstrated the proposed method in which normal random and abnormal pipe flow data were generated. The best w value was identified from a sensitivity analysis, and the impact of measurement frequency (dt = 5, 10, 15 min etc.) was also investigated. The proposed method was compared to the original WECO method with respect to detection probability, false alarm rate, and averaged detection time. Finally, this study provides a set of guidelines on the use of the WECO method for real-life WDS pipe burst detection.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.53-59
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• 2016

Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.

• The Journal of Bigdata
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• v.3 no.1
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• pp.83-94
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• 2018

The purpose of this study is to investigate how startup companies with the UGC platform service model can traverse the death valley for the company's survival with limited resources and create a mutually beneficial market. To do this, an interview-based exploratory study was conducted to analyze the cause and effect of each factor on the initial activation strategy of the UGC platform. For many start-up companies, this research helps minimize errors in strategic trial and error.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.1009-1017
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• 2022

Log data has been used as a basis in understanding and deciding the main functions and state of information systems. It has also been used as an important input for the various applications in cybersecurity. It is an essential part to get necessary information from log data, to make a decision with the information, and to take a suitable countermeasure according to the information for protecting and operating systems in stability and reliability, but due to the explosive increase of various types and amounts of log, it is quite challenging to effectively and efficiently deal with the problem using existing tools. Therefore, this study has suggested a multiclass classification of the security severity level of multi-source event log using machine learning based on natural language processing. The experimental results with the training and test samples of 472,972 show that our approach has archived the accuracy of 99.59%.

• Journal of Korea Technology Innovation Society
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• v.21 no.1
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• pp.385-419
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• 2018

This study suggests an integrated model composed of factors of industrial environments and technology capacity for corporate diversification decision based on industrial organization theory and resource based perspectives. We examine the proposed model using patents and financial data of 272 applicants for 6 years (2010~2015) in the smart factory ICT convergence technology (application and platform field) sectors. The result of analyzing the fixed effect panel model shows that technological competitiveness has a positive effect on corporate diversification. Also, the additional result of analyzing the two-stage least square fixed effect model indicates that the convergence patent ratio increases technological competitiveness. Based on the results, we provide implications for corporate diversification strategies and government R & D policies for commercialization of corporate convergence technology resources and competencies.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.329-338
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• 2009

The importance performance analysis is a simple and condensed analytic method for decision making based on the level of performance or satisfaction. Many researches already have witnessed usefulness of the importance performance analysis, but it also has some drawbacks from the statistical points of view. In this article, some additional techniques dealing the importance performance analysis are introduced and it is shown that these techniques would turn out to be very informative. The importance performance analysis uses the arithmetic average as the main statistic, but by the use of the median, the frequency and the cluster analysis it is shown that the importance performance analysis can be carried out with more crucial information. In addtion to that, it is demonstrated that the combination of the analytic hierarchy process and importance performance analysis could enable more reliable decision making.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.6
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• pp.100-105
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• 2017

This work presents an ECG biometric recognition system for the purpose of biometric authentication. ECG biometric approaches are divided into two major categories, fiducial-based and non-fiducial-based methods. This paper proposes a new non-fiducial framework using discrete cosine transform and a Random Forest classifier. When using DCT, most of the signal information tends to be concentrated in a few low-frequency components. In order to apply feature vector of Random Forest, DCT feature vectors of ECG heartbeats are constructed by using the first 40 DCT coefficients. RF is based on the computation of a large number of decision trees. It is relatively fast, robust and inherently suitable for multi-class problems. Furthermore, it trade-off threshold between admission and rejection of ID inside RF classifier. As a result, proposed method offers 99.9% recognition rates when tested on MIT-BIH NSRDB.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.464-469
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• 2015

Long-term electrocardiogram data can be acquired by linking a Holter monitor to a mobile phone. However, most systems are designed to detect arrhythmia through heartbeat classification, and not just for supporting clinical decisions. In this paper, we propose an Abstracting algorithm, and introduce an analogous pateint search system using this algorithm. An analogous patient searcher summarizes each patient's typical pattern using the results of heartbeat, which can greatly simplify clinical activity. It helps to find patients with similar arrhythmia patterns, which can help in contributing to diagnostic clues. We have simulated these processes on data from the MIT-BIH arrhythmia database. As a result, the Abstracting algorithm provided a typical pattern to assist in reaching rapid clinical decisions for 64% of the patients. On an average, typical patterns and results generated by the abstracting algorithm summarized the results of heartbeat classification by 98.01%.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.35-40
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• 2022

In this paper, we propose a method of constructing clinical knowledge with clinical concept relations and predicting diseases based on a deep learning model to support clinical decision-making. Clinical terms in UMLS(Unified Medical Language System) and cancer-related medical knowledge are classified into five categories. Medical related documents in Wikipedia are extracted using the classified clinical terms. Clinical concept relations are established by matching the extracted medical related documents with the extracted clinical terms. After deep learning using clinical knowledge, a disease is predicted based on medical terms expressed in a query. Thereafter, medical terms related to the predicted disease are selected as an extended query for clinical document retrieval. To validate our method, we have experimented on TREC Clinical Decision Support (CDS) and TREC Precision Medicine (PM) test collections.