• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1524-1530
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• 2016

Recently, research about an electronic attendance-absence recording system has been actively carried out using smart devices. Using an electronic attendance-absence recording system, professors can check their students' attendance on a real-time basis and manage their attendance records. In this paper, we proposed a real-time electronic attendance-absence recording system using face detection and face recognition based on web application. It can solve the spatial, temporal, cost issues belong to electronic attendance-absence recording system using AIDC(Automatic Identification and Data Capture). A proposed system is running on web server and made by HTML5(Hyper Text Markup Language ver.5). So professor connect to server using mobile web browser on mobile device and real-time manage electronic attendance-absence recording with real-time send or receive image data. In addition, the proposed system has an advantage capable of installation and operation, regardless of the operating system because it operates based on the Python flask framework.

• Journal of KIISE
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• v.42 no.4
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• pp.468-474
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• 2015

Corporations should make efforts to recognize the importance of projects, identify their failure factors, prevent risks in advance, and raise the success rates, because the corporations need to make quick responses to rapid external changes. There are some previous studies on success and failure factors of projects, however, most of them have limitations in terms of objectivity and quantitative analysis based on data gathering through surveys, statistical sampling and analysis. This study analyzes the failure factors of projects based on data mining to find problems with projects in an audit report, which is an objective project evaluation report. To do this, we identified the texts in the paragraph of suggestions about improvement. We made use of the superior classification algorithms in this study, which were NaiveBayes, SMO and J48. They were evaluated in terms of data of Recall and Precision after performing 10-fold-cross validation. In the identified texts, the failure factors of projects were analyzed so that they could be utilized in project implementation.

• The Journal of the Korea Contents Association
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• v.5 no.6
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• pp.343-350
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• 2005

During the design of a successful digital watermarking systems, Pseudo-Noise(PN) sequences are widely used to modulate information bits into watermark signals. In this method, the number of bits that can be hidden within a small image by means of frequency domain watermarking is limited. In this paper, we show the possibility of introducing chaotic sequences into digital watermarking systems as potential substitutes to commonly used PN-sequences. And we propose a method that transforms the text to chaotic sequence. In our current implementation, we show how the sample text is expressed by an implied unit data(watermark) and the implied unit data is regenerated into the original left. Because we use this implied data as watermark for information hiding, we can insert much more watermark compared with previous method.

• Journal of Korean Academy of Nursing
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• v.47 no.6
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• pp.806-816
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• 2017

Purpose: As comprehensive nursing care service has gradually expanded, it has become necessary to explore the various opinions about it. The purpose of this study is to explore the large amount of text data regarding comprehensive nursing care service extracted from online news and social media by applying a semantic network analysis. Methods: The web pages of the Korean Nurses Association (KNA) News, major daily newspapers, and Twitter were crawled by searching the keyword 'comprehensive nursing care service' using Python. A morphological analysis was performed using KoNLPy. Nodes on a 'comprehensive nursing care service' cluster were selected, and frequency, edge weight, and degree centrality were calculated and visualized with Gephi for the semantic network. Results: A total of 536 news pages and 464 tweets were analyzed. In the KNA News and major daily newspapers, 'nursing workforce' and 'nursing service' were highly rated in frequency, edge weight, and degree centrality. On Twitter, the most frequent nodes were 'National Health Insurance Service' and 'comprehensive nursing care service hospital.' The nodes with the highest edge weight were 'national health insurance,' 'wards without caregiver presence,' and 'caregiving costs.' 'National Health Insurance Service' was highest in degree centrality. Conclusion: This study provides an example of how to use atypical big data for a nursing issue through semantic network analysis to explore diverse perspectives surrounding the nursing community through various media sources. Applying semantic network analysis to online big data to gather information regarding various nursing issues would help to explore opinions for formulating and implementing nursing policies.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.223-228
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• 2020

The stock price reflects people's psychology, and factors affecting the entire stock market include economic growth rate, economic rate, interest rate, trade balance, exchange rate, and currency. The domestic stock market is heavily influenced by the stock index of the United States and neighboring countries on the previous day, and the representative stock indexes are the Dow index, NASDAQ, and S & P500. Recently, research on stock price analysis using stock news has been actively conducted, and research is underway to predict the future based on past time series data through artificial intelligence-based analysis. However, even if the stock market is hit for a short period of time by the forecasting system, the market will no longer move according to the short-term strategy, and it will have to change anew. Therefore, this model monitored Samsung Electronics' stock data and news information through text mining, and presented a predictable model by showing the analyzed results.

• Journal of the Korea Convergence Society
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• v.11 no.5
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• pp.199-207
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• 2020

Recently, big data analysis has drawn attention in various fields as it can generate value from large amounts of data and is also used to run political campaigns or predict results. However, existing research had limitations in compiling information about candidates at a high-level by analyzing only specific SNS data. Therefore, this study analyses news trends, topics extraction, sentiment analysis, keyword analysis, comment analysis for the 2017 presidential election of South Korea. The results show that various topics had been generated, and online opinions are extracted for trending keywords of respective candidates. This study also shows that portal news and comments can serve as useful tools for predicting the public's opinion on social issues. This study will This paper advances a building strategic course of action by providing a method of analyzing public opinion across various fields.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.461-469
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• 2000

In the paper, the design and the implementation of the netscape plug-in and the speech synthesis enginegenerating the speech sounds from the text information of the web pages are described. The steps of the generating speech sound from an web pages are the speech synthesis plug-in is activated when the netscape finds the audio/xesp MIME data type embedded in the browsed web page; the HTML file referenced in the EMBED MTML tag is down loaded from the referenced URL to send to the commander object located in the said plug-in; The speech synthesis engine control tags and the text characters are extracted from the down loaded HTML document by the commander object the synthesized speech sounds are generated by the speech synthesis engine. The speech synthesis engine interprets the command streams from the commander objects to call the member functions for the processing of the speech segment data in the data banks. The commander object and the speech synthesis engine are designed as an independent object to enhancethe flexitility and the portability.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.550-555
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• 2018

This study aims to identify and compare contents and keywords used in articles related to blockchain applications to various industries. The text mining and Semantic Network Analysis, as methods of keyword network analysis, were used to analyze articles including terms of 'finance' 'energy' and 'logistics', which media and government frequently mentioned as areas that can apply blockchain technologies. For this study, data were collected from 43,093 articles from January, 2017 through July, 2018. Data crawling was carried out by using Python BeautifulSoup and data cleaning was performed in order to eliminate mutual redundancies of the three terms. After that, text mining and semantic network analysis were performed using Textom and UCInet for network analysis between keywords. The results showed that all the three terms were similar in terms of 'technology', but there were differences in the contents of 'government policy' or 'industry' issues. In addition, there were differences in frequencies and centralities of these terms.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.1-17
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• 2012

The Smartphone, one of essential mobile devices widely used recently, can be very effectively applied to capture knowledge on the spot by jointly applying the pervasive functionality of cloud computing. The process of knowledge capturing can be also effectively automated if the topic of knowledge is automatically identified. Therefore, this paper suggests an interdisciplinary approach to automatically acquire knowledge on the spot by combining technologies of text mining-based topic identification and cloud computing-based Smartphone. The Smartphone is used not only as the recorder to record knowledge possessor's dialogue which plays the role of the knowledge source, but also as the sensor to collect knowledge possessor's context data which characterize specific situations surrounding him or her. The support vector machine, one of well-known outperforming text mining algorithms, is applied to extract the topic of knowledge. By relating the topic and context data, a business rule can be formulated, and by aggregating the rule, the topic, context data, and the dictated dialogue, a set of knowledge is automatically acquired.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.5
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• pp.530-539
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• 2002

Recently, there have been a lot of researches to develop an archive system for news videos that usually has a fixed structure. However, since the meta-data representation and storing schemes for news video are different from each other in the previously proposed archive systems, it was very hard to exchange these meta-data. This paper proposes a scheme for news video based on MPEG-7 MDS that is an international standard to represent the contents of multimedia, and a summarization mechanism reflecting the characteristics of shots in the news videos. The proposed scheme for news video uses the MPEG-7 MDS schemes such as VideoSegment and TextAnnotation to keep the original structure of news video, and the proposed summarization mechanism uses a slide-show style presentation of key frames with associated audio to reduce the data size of the summary video.