• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.208-214
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• 2021

Recently, much of the intelligent security scenario and criminal investigation demands for matching photo and non-photo. Existing face recognition system can not sufficiently guarantee these needs. In this paper, we propose an algorithm to improve the performance of heterogeneous face recognition systems by reducing the different modality between sketches and photos of the same person. The proposed algorithm extracts each image's texture features through texture descriptors (gray level co-occurrence matrix, multiscale local binary pattern), and based on this, generates a transformation matrix through eigenfeature regularization and extraction techniques. The score value calculated between the vectors generated in this way finally recognizes the identity of the sketch image through the score normalization methods.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.2
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• pp.263-287
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• 2021

Researches on inequality in Korean society has been sporadically conducted in various areas. In this study, research trend related to inequality was analyzed through basic statistical analysis, co-occurrence analysis, and main path analysis using articles related to inequality from Korea citation index. In basic statistical analysis, key authors, journals, and articles are identified. In co-occurrence analysis, income inequality, educational inequality, welfare inequality, and policy on inequality were identified as main topics. Main path analysis showed two research trends after 2004. One was research trend on economic inequality, and the other was on health inequality and social structural inequality.

• Korean Journal of Schizophrenia Research
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• v.23 no.2
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• pp.58-64
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• 2020

Objectives: In this study, we conducted an exploratory analysis of the current media trends on schizophrenia using text-mining methods. Methods: First, web-crawling techniques extracted text data from 575 news articles in 10 major newspapers between 2018 and 2019, which were selected by searching "schizophrenia" in the Naver News. We had developed document-term matrix (DTM) and/or term-document matrix (TDM) through pre-processing techniques. Through the use of DTM and TDM, frequency analysis, co-occurrence network analysis, and topic model analysis were conducted. Results: Frequency analysis showed that keywords such as "police," "mental illness," "admission," "patient," "crime," "apartment," "lethal weapon," "treatment," "Jinju," and "residents" were frequently mentioned in news articles on schizophrenia. Within the article text, many of these keywords were highly correlated with the term "schizophrenia" and were also interconnected with each other in the co-occurrence network. The latent Dirichlet allocation model presented 10 topics comprising a combination of keywords: "police-Jinju," "hospital-admission," "research-finding," "care-center," "schizophrenia-symptom," "society-issue," "family-mind," "woman-school," and "disabled-facilities." Conclusion: The results of the present study highlight that in recent years, the media has been reporting violence in patients with schizophrenia, thereby raising an important issue of hospitalization and community management of patients with schizophrenia.

• The Korean Journal of Food & Health Convergence
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• v.9 no.1
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• pp.19-28
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• 2023

The purpose of this study is to explore which areas have been more interested in depression research in Korea through analysis of academic papers related to depression, and then to provide insights that can solve future depression problems. 1,032 papers searched with the keyword "depression" in scienceON were analyzed using Python 3.7 for word frequency analysis, word co-occurrence analysis, BERTopic, LDA, and OLS regression analysis. The results of word frequency and co-occurrence frequency analysis showed that related words were composed around words such as patient, disorder and symptom. As a result of topic modeling, a total of 13 topics including 'childhood depression' and 'eating anxiety' were derived. And it has been identified as a topic of interest that 'suicidal thoughts', 'treatment', 'occupational health', and 'health treatment program' were statistically significant topics, while 'child depression' and 'female treatment' were relatively less. As a result of the analysis of research trends, future research will not only study physiological and psychological factors but also social and environmental causes, as well as it was suggested that various collaborative studies of experts in academia were needed such as convergence and complex perspectives for depression relief and treatment.

• The Journal of Economics, Marketing and Management
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• v.11 no.4
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• pp.53-63
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• 2023

Purpose: The purpose of this study is to examine the research trends in the papers published by Korean researchers related to traditional markets, to check what topics have been studied, and to make various suggestions for research directions and effective ways to revitalize traditional markets. Research design, data and methodology: To this end, this study conducted word frequency analysis, co-occurrence frequency analysis, BERTopic, LDA, dynamic topic modeling and OLS regression analysis using Python 3.7 on the English abstracts of a total of 502 papers extracted through ScienceON. Results: As a result of word frequency analysis and co-occurrence frequency analysis, it was found that studies related to traditional markets have been conducted not only on factors related to customers, but also on traditional market merchants and government policies, and the degree of service, quality, and satisfaction perceived by customers using traditional markets. Through BERTopic and LDA, three topics such as 'Traditional market safety management' were identified, and among them, it was found that 'Traditional market safety management' is relatively less attention by researchers. Conclusions: The results of this study suggest that future research on the revitalization of traditional markets should be conducted from a specific consulting perspective along with the establishment of various data, a causal model study from various perspectives such as the characteristics of merchants as well as consumers, and an integrated and convergent approach to policy formulation by the government and local governments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.307-310
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• 2021

In the recent years, studies using Computer-Aided Diagnostics(CAD) have been actively conducted, such as signal and image processing technology using breast ultrasound images, automatic image optimization technology, and automatic detection and classification of breast masses. As computer diagnostic technology is developed, it is expected that early detection of cancer will proceed accurately and quickly, reducing health insurance and test ice for patients, and eliminating anxiety about biopsy. In this paper, a quantitative analysis of tumors was conducted in ultrasound images using a gray level co-occurrence matrix(GLCM) to experiment with the possibility of use for computer assistance diagnosis.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.784-791
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• 2022

Tack coat is a thin layer of asphalt between the existing pavement and asphalt overlay. During construction, insufficient tack coat layering can later cause surface defects such as slippage, shoving, and rutting. This paper proposed a method for tack coat inspection improvement using an unmanned aerial vehicle (UAV) and deep learning neural network for automatic non-uniform assessment of the applied tack coat area. In this method, the drone-captured images are exploited for assessment using a combination of Mask R-CNN and Grey Level Co-occurrence Matrix (GLCM). Mask R-CNN is utilized to detect the tack coat region and segment the region of interest from the surroundings. GLCM is used to analyze the texture of the segmented region and measure the uniformity and non-uniformity of the tack coat on the existing pavements. The results of the field experiment showed both the intersection over union of Mask R-CNN and the non-uniformity measured by GLCM were promising with respect to their accuracy. The proposed method is automatic and cost-efficient, which would be of value to state Departments of Transportation for better management of their work in pavement construction and rehabilitation.

• The Journal of Economics, Marketing and Management
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• v.11 no.5
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• pp.45-55
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• 2023

Purpose: As one of the continuous studies on the offline distribution industry, the purpose of this study is to find ways for offline stores to respond to the growth of online shopping by identifying research trends on department stores. Research design, data and methodology: To this end, this study conducted word frequency analysis, word co-occurrence frequency analysis, BERTopic, LDA, and dynamic topic modeling using Python 3.7 on a total of 551 English abstracts searched with the keyword 'department store' in scienceON as of October 10, 2022. Results: The results of word frequency analysis and co-occurrence frequency analysis revealed that research related to department stores frequently focuses on factors such as customers, consumers, products, satisfaction, services, and quality. BERTopic and LDA analyses identified five topics, including 'store image,' with 'shopping information' showing relatively high interest, while 'sales systems' were observed to have relatively lower interest. Conclusions: Based on the results of this study, it was concluded that research related to department stores has so far been conducted in a limited scope, and it is insufficient to provide clues for department stores to secure competitiveness against online platforms. Therefore, it is suggested that additional research be conducted on topics such as the true role of department stores in the retail industry, consumer reinterpretation, customer value and lifetime value, department stores as future retail spaces, ethical management, and transparent ESG management.

• Journal of dental hygiene science
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• v.23 no.4
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• pp.255-263
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• 2023

Background: This study used text mining techniques to determine public perceptions of dental fear, extracted keywords related to dental fear, identified the connection between the keywords, and categorized and visualized perceptions related to dental fear. Methods: Keywords in texts posted on Internet portal sites (NAVER and Google) between 1 January, 2000, and 31 December, 2022, were collected. The four stages of analysis were used to explore the keywords: frequency analysis, term frequency-inverse document frequency (TF-IDF), centrality analysis and co-occurrence analysis, and convergent correlations. Results: In the top ten keywords based on frequency analysis, the most frequently used keyword was 'treatment,' followed by 'fear,' 'dental implant,' 'conscious sedation,' 'pain,' 'dental fear,' 'comfort,' 'taking medication,' 'experience,' and 'tooth.' In the TF-IDF analysis, the top three keywords were dental implant, conscious sedation, and dental fear. The co-occurrence analysis was used to explore keywords that appear together and showed that 'fear and treatment' and 'treatment and pain' appeared the most frequently. Conclusion: Texts collected via unstructured big data were analyzed to identify general perceptions related to dental fear, and this study is valuable as a source data for understanding public perceptions of dental fear by grouping associated keywords. The results of this study will be helpful to understand dental fear and used as factors affecting oral health in the future.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_3
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• pp.1183-1195
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• 2023

In this study, we collected and analyzed domestic and international studies related to watersheds in the forest sector. Keyword co-occurrence analysis was conducted using the VOSviewer program to identify the research areas of domestic and international studies and the network structure to compare research trends. As a result, the number of research articles in international watershed-related studies showed an overall increasing trend, and the research areas were diverse and located close to each other, indicating that many convergence studies were conducted. On the other hand, the number of papers in domestic watershed-related studies seems to have stagnated overall from the past to the present, and the research areas are mainly focused on forest disasters and hydrology, with limited interdisciplinary convergence studies. In addition, in both domestic and international studies, watersheds are currently mentioned as research sites rather than management or analysis units in the forest sector. It is important to actively promote interdisciplinary research in Korea to provide a scientific and balanced basis for watershed-level forest management planning.