• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.609-616
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• 2017

In this paper, we propose an algorithm to obtain more stable and high recognition rate by using left and right rotation information of input image in order to obtain a stable recognition rate in face recognition. The proposed algorithm uses the facial image as the input information in the web camera environment to reduce the size of the image and normalize the information about the brightness and color to obtain the improved recognition rate. We apply Principal Component Analysis (PCA) to the detected candidate regions to obtain feature vectors and classify faces. Also, In order to reduce the error rate range of the recognition rate, a set of data with the left and right $45^{\circ}$ rotation information is constructed considering the directionality of the input face image, and each feature vector is obtained with PCA. In order to obtain a stable recognition rate with the obtained feature vector, it is after scattered in the eigenspace and the final face is recognized by comparing euclidean distant distances to each feature. The PCA-based feature vector is low-dimensional data, but there is no problem in expressing the face, and the recognition speed can be fast because of the small amount of calculation. The method proposed in this paper can improve the safety and accuracy of recognition and recognition rate faster than other algorithms, and can be used for real-time recognition system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.493-506
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• 2018

This paper proposes the OOPP(Optimized Online Portfolio Platform) design for the job seekers to search for the job competency necessary for employment and to write and manage portfolio online efficiently. The OOPP consists of three modules. First, JDCM(Job Data Collection Module) stores the help-wanted advertisements of job information sites in a spreadsheet. Second, CSM(Competency Statistical Model) classifies core competencies for each job by text-mining the collected help-wanted ads. Third, OBBM(Optimize Browser Behavior Module) makes users to look up data rapidly by improving the processing speed of a browser. In addition, The OBBM consists of the PSES(Parallel Search Engine Sub-Module) optimizing the computation of a Search Engine and the OILS(Optimized Image Loading Sub-Module) optimizing the loading of image text, etc. The performance analysis of the CSM shows that there is little difference in accuracy between the CSM and the actual advertisement because its data accuracy is 99.4~100%. If Browser optimization is done by using the OBBM, working time is reduced by about 68.37%. Therefore, the OOPP makes users look up the analyzed result in the web page rapidly by analyzing the help-wanted ads. of job information sites accurately.

• Korean Journal of Clinical Laboratory Science
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• v.51 no.1
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• pp.15-25
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• 2019

The development of reliable and objective definitions as well as automated processes for the detection of health care-associated infections (HAIs) is crucial; however, transformation to an automated surveillance system remains a challenge. Early outbreak identification usually requires clinicians who can recognize abnormal events as well as ongoing disease surveillance to determine the baseline rate of cases. The system screens the laboratory information system (LIS) data daily to detect candidates for health care-associated bloodstream infection (HABSI) according to well-defined detection rules. The system detects and reserves professional autonomy by requiring further confirmation. In addition, web-based HABSI surveillance and classification systems use discrete data elements obtained from the LIS, and the LIS-provided data correlates strongly with the conventional infection-control personnel surveillance system. The system was timely, acceptable, useful, and sensitive according to the prevention guidelines. The surveillance system is useful because it can help health care professionals better understand when and where the transmission of a wide range of potential pathogens may be occurring in a hospital. A national plan is needed to strengthen the main structures in HAI prevention, Healthcare Associated Prevention and Control Committee (HAIPCC), sterilization service (SS), microbiology laboratories, and hand hygiene resources, considering their impact on HAI prevention.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.163-170
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• 2019

Over the past decade, the development of the Web explosively increased the data. Feature selection step is an important step in extracting valuable data from a large amount of data. This study proposes a novel opinion mining model based on combining feature selection (FS) methods with Word embedding to vector (Word2vec) and BOW (Bag-of-words). FS methods adopted for this study are CFS (Correlation based FS) and IG (Information Gain). To select an optimal FS method, a number of classifiers ranging from LR (logistic regression), NN (neural network), NBN (naive Bayesian network) to RF (random forest), RS (random subspace), ST (stacking). Empirical results with electronics and kitchen datasets showed that LR and ST classifiers combined with IG applied to BOW features yield best performance in opinion mining. Results with laptop and restaurant datasets revealed that the RF classifier using IG applied to Word2vec features represents best performance in opinion mining.

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.63-85
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• 2018

The "About Us" page on the website of a corporation provides information regarding the organization's vision, philosophy, and values. We examine the association between institutional information provided on corporate websites (i.e., the "About Us" section) with present and future financial performance. Utilizing a text mining technique, we analyze the institutional information of S&P500 firms in the year 2016. We conduct a factor analysis including words that are intentionally repeated in the introductory text of corporate websites. The results of the analysis reveal that keywords from this institutional information can be grouped into six factors. We then carry out an ordinary least squares regression analysis to determine the associations between these six factors and present financial performance. The results show that keywords in Factor 2 (those related to Purchasing experience) are positively associated with ROE, a variable representing present financial performance, while keywords in Factor 1 (those related to Note to customers) show a negative relationship with ROE. On the other hand, keywords in Factor 1 have a positive relationship with Tobin's Q, a variable representing future financial performance. These results indicate that there is some relationship between the words used in the institutional information in this section of corporate websites and firms' financial performance. Hence, the institutional information on a website may be a useful indicator of current firm performance and future firm value.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.309-311
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• 2014

Despite increasing demands for big data application based on the analysis of scattered unstructured data, few relevant studies have been reported. Accordingly, the present study suggests a technique enabling a sentence-based semantic analysis by extracting objects from collected web information and automatically analyzing the relationships between such objects with collective intelligence and language processing technology. To be specific, collected information is stored in DBMS in a structured form, and then morpheme and feature information is analyzed. Obtained morphemes are classified into objects of interest, marginal objects and objects of non-interest. Then, with an inter-object attribute recognition technique, the relationships between objects are analyzed in terms of the degree, scope and nature of such relationships. As a result, the analysis of relevance between the information was based on certain keywords and used an inter-object relationship extraction technique that can determine positivity and negativity. Also, the present study suggested a method to design a system fit for real-time large-capacity processing and applicable to high value-added services.

• The Korean Journal of Archival Studies
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• no.28
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• pp.123-153
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• 2011

In Asia, when cotton and cotton fabrics cultivated and produced in India of Southern Asia had spread to the whole Asia area by land and by sea, the Cotton Road and cotton fabric cultural area could be formed. In Korea, the traditional cotton (Gossypium arboreum) brought by Moon Ik-Jeom in 1363 was cultivated and then the Upland cotton (Gossypium hirsutum) brought via Japan could be produced from 1904. Especially, Gwangju/Jeonnam was the most active place in producing traditional cotton, and eventually became the center of cotton cultivation and fabric production after bringing in Upland cotton. In order to collect and record the cotton cultural resources in the broad area, the Cultural Resources Set, classified its component parts should be made first and then the collecting objects should be investigated. The collecting areas are selected based on the spreading paths and the regional significance of cotton. Since its difficulty of collecting the relevant resources from all of the places in Asia, it should be planned to share the resources through exchanges and cooperation among private, institution and organization. The relevant experts from the various fields should participate in the interdisciplinary researches which are necessary for collecting and recording of wide area cultural resources. Considering the collecting limitation of genuine relics, the digital archives should be established and then offered through a web site that everyone can use them freely by remote. It also needs to plan to display on and off-line for users to perceive the similarity, difference and interconnections of the resources with ease.

• Journal of the Korea Institute of Building Construction
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• v.22 no.3
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• pp.281-291
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• 2022

This study attempts to use big data to determine the indicators necessary for a fire risk assessment of buildings. Because most of the causes affecting the fire risk of buildings are fixed as indicators considering only the building itself, previously only limited and subjective assessment has been performed. Therefore, if various internal and external indicators can be considered using big data, effective measures can be taken to reduce the fire risk of buildings. To collect the data necessary to determine indicators, a query language was first selected, and professional literature was collected in the form of unstructured data using a web crawling technique. To collect the words in the literature, pre-processing was performed such as user dictionary registration, duplicate literature, and stopwords. Then, through a review of previous research, words were classified into four components, and representative keywords related to risk were selected from each component. Risk-related indicators were collected through analysis of related words of representative keywords. By examining the indicators according to their selection criteria, 20 indicators could be determined. This research methodology indicates the applicability of big data analysis for establishing measures to reduce fire risk in buildings, and the determined risk indicators can be used as reference materials for assessment.

• Journal of Space Technology and Applications
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• v.1 no.2
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• pp.199-216
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• 2021

In 2020, the solar and space environment division at the Korea Space Science Society surveyed the status of data archives in solar physics, magnetosphere, and ionosphere/upper atmosphere in Korea to promote broader utilization of the data and research collaboration. The survey includes ground- and satellite-based instruments and developing models by research institutes and universities in Korea. Based on the survey results, this study reports the status of the ground-based instruments, data products in the ionosphere and upper atmosphere, and documentation of them. The ground-based instruments operated by the Korea Polar Research Institute and Korea Astronomy and Space Science Institute include ionosonde, Fabry-Perot interferometer in Arctic Dasan stations, Antarctic King Sejong/Jang Bogo stations, and an all-sky camera, VHF radar in Korea. We also provide information on total electron content and scintillation observations derived from the Global Navigation Satellite System (GNSS) station networks in Korea. All data are available via the webpage, FTP, or by request. Information on ionospheric data and models is available at http://ksss.or.kr. We hope that this report will increase data accessibility and encourage the research community to engage in the establishment of a new Space Science Data Ecosystem, which supports archiving, searching, analyzing, and sharing the data with diverse communities, including educators, industries, and the public as wells as the research scientist.

• Information Systems Review
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• v.24 no.1
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• pp.21-37
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• 2022

Drug repositioning, one of the methods of developing new drugs, is a useful way to discover new indications by allowing drugs that have already been approved for use in people to be used for other purposes. Recently, with the development of machine learning technology, the case of analyzing vast amounts of biological information and using it to develop new drugs is increasing. The use of machine learning technology to drug repositioning will help quickly find effective treatments. Currently, the world is having a difficult time due to a new disease caused by coronavirus (COVID-19), a severe acute respiratory syndrome. Drug repositioning that repurposes drugsthat have already been clinically approved could be an alternative to therapeutics to treat COVID-19 patients. This study intends to examine research trends in the field of drug repositioning using machine learning techniques. In Pub Med, a total of 4,821 papers were collected with the keyword 'Drug Repositioning'using the web scraping technique. After data preprocessing, frequency analysis, LDA-based topic modeling, random forest classification analysis, and prediction performance evaluation were performed on 4,419 papers. Associated words were analyzed based on the Word2vec model, and after reducing the PCA dimension, K-Means clustered to generate labels, and then the structured organization of the literature was visualized using the t-SNE algorithm. Hierarchical clustering was applied to the LDA results and visualized as a heat map. This study identified the research topics related to drug repositioning, and presented a method to derive and visualize meaningful topics from a large amount of literature using a machine learning algorithm. It is expected that it will help to be used as basic data for establishing research or development strategies in the field of drug repositioning in the future.