• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1087-1101
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• 2020

Various security devices are used to protect internal networks and valuable information from rapidly evolving cyber attacks. Firewall, which is the most commonly used security device, tries to prevent malicious attacks based on a text-based filtering rule (i.e., access control policy), by allowing or blocking access to communicate between inside and outside environments. However, in order to protect a valuable internal network from large networks, it has no choice but to increase the number of access control policy. Moreover, the text-based policy requires time-consuming and labor cost to analyze various types of vulnerabilities in firewall. To solve these problems, this paper proposes a 3D-based hierarchical visualization method, for intuitive analysis and management of access control policy. In particular, by providing a drill-down user interface through hierarchical architecture, Can support the access policy analysis for not only comprehensive understanding of large-scale networks, but also sophisticated investigation of anomalies. Finally, we implement the proposed system architecture's to verify the practicality and validity of the hierarchical visualization methodology, and then attempt to identify the applicability of firewall data analysis in the real-world network environment.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.795-814
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• 2021

With the development of science and technology in recent years, various types of unstructured buildings have begun to be implemented by combining traditional architectural styles with digital tools, which are significantly different from conventional formal buildings. Designers will use various methods or digital tools to complete the unstructured and freer architectural forms when building an unstructured building. Based on this background, the need for research on the criteria for evaluating the characteristics of unstructured architectural forms is raised. First, the text was reconstructed by considering and integrating the elements of the unstructured exterior form based on prior research, with the external form of the unstructured building as the main research object. Second, the purpose of this study was to classify various types of unstructured forms and to provide important basic data for designing digital processes of unstructured architectural forms.Third, from 2000 to 2020, the text focused on the study of unstructured buildings and conducted in-depth analysis of the characteristics of their form expressions. While providing a case basis for the study of related fields, distribution laws and presence values related to the characteristics of unstructured buildings were sought. In addition, the analysis was conducted in combination with the distribution of functional use of buildings, and this study is differentiated from the existing research in that the atypical form is applied to the buildings by use, and the application trend of this type is understood to enhance understanding of the atypical form of buildings.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.163-171
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• 2022

This study was conducted to understand the research trends and central concepts of middle-aged women' health in Korea. For the analysis of this study, target papers published from 2012 to 2021 were collected by entering the keywords of 'middle-aged woman' or 'menopausal woman'. 1,116 papers were used for analysis. The co-occurrence network of key words was developed and analyzed, and the research trends were analyzed through topic modeling of the LSD by dividing it into five-year units (2012-2016, 2017-2021), and visualized word cloud and sociogram were used. The keywords that appeared the most during the last 10 years were obesity, depression, body composition, stress, and menopause symptom. Five topics analyzed in the thesis data for 5 years from 2012 to 2016 were 'postmenopausal self-efficacy and satisfaction enhancement strategy', 'exercise to manage obesity and risk factors', 'intervention for obesity and stress', 'promotion of happiness and life management' and 'menopausal depression and quality of life' were confirmed. Five topics of research conducted for the next five years (2017-2021) were 'menopausal depression and quality of life', 'management of obesity and cardiovascular risk factors', 'life experience as a middle-aged woman', and 'life satisfaction and psychological well-being' and 'menopausal symptom relief strategy'. Through the results, the trend of research topics related to middle-aged women's health over the past 10 years have been identified, and research on health of middle-aged women that reflects the trend of the future should be continued.

• Korean small business review
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• v.43 no.4
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• pp.27-57
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• 2021

How does authenticity influence the process that conducts a company's CSR Strategy? Authenticity, an internal/external alignment condition that an employee feels in relation to an organization, means the decision on how true and beneficial to employees through their experiences, such as thoughts and emotions. Also, it can be understood as a process of meaning formation between the organization's strategy to conduct CSR and the perception of employees conducting CSR. To prove the relation between authenticity and CSR clearly, we used various techniques like Text Mining, Topic Modeling and Semantic network analysis about O corporation's 657 review data, from 2015 to 2021. As a result of the analysis, we find out the special issues and types. The analysis shows that the issue concerning the 'external image' is the biggest characteristic of authenticity perception in other conditions. Furthermore, the types of authenticity perception evaluations are largely divided into acceptance and rejection, in detail, five categories. This study indicates that organizations should consider both external and internal conditions when establishing CSR strategies. In addition, it is necessary to be an interactive circular relationship between the organization and employee, collecting and reflecting employee's perceptions. Finally, this study proposes ways to overcome problems related to interaction.

• Journal of Korea Entertainment Industry Association
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• v.14 no.7
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• pp.487-500
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• 2020

This research analyzed articles about the disability under the 「The Disabled Person Welfare Law」 in a major daily newspaper. A total of 7,684 articles on disability were collected from homepages of the three newspapers , , and . Through network text analysis and content analysis, we considered about "The perspective of Disability" based on "Multiple Disability Model". As a result of this research, when comparing individual models versus social models, individual models have a higher rate 64.31% than social models 35.69%. According to the newspapers, the major perception of Disability is a traditional individual model, which means disability must be solved by individuals. In addition, due to low social and institutional supports, the public's attention and consideration required for the disabled, socially weak people. This research implied that despite the changing times of looking at disability, three newspapers are still staying in the traditional paradigm. Therefore, It is required that viewing a disability from the perspective on disabled people, and a mature awareness that recognizes the diversity of individual needs. The significance of this study can be found in the fact that no attempt has been made to treat the disability perspectivec in newspaper articles as quantitative and qualitative data.

• Journal of The Korean Association For Science Education
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• v.42 no.4
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• pp.429-438
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• 2022

In this study, we propose a way to improve science teacher expertise through infographics creation teacher training program by analyzing the infographics types focusing on the Earth Science subject of the 2015 revised curriculum, and inspecting the teachers' utilization of graphic tools. The data visualization characteristics of Earth Science textbooks were analyzed, the execution results of the infographics creation teacher training program were presented, and a survey on science teachers' change in perception and competency of infographics. As a result of the Earth Science textbook analysis, diagram-type, map-type, and comparative analysis-type infographics were frequently used, and were mainly presented as text-assisted-type infographics. The infographics creation teacher training program was conducted five times for 112 science teachers to create the complete, text-assisted, incomplete, and gradient-type infographics. Incomplete infographics for development of evaluation questions were most needed. Although many science teachers recognize the importance of infographics, they lacked the competency to create high-quality infographics because there were no training opportunities for infographics creation. After completing the training, 74.1% of teachers felt that the quality of developments of supplementary textbooks and evaluation questions had improved, and answered that it was helpful in re-educating knowledge and improving teaching-learning methods. Based on the research results, ways to improve science teacher expertise through infographics creation teacher training program were suggested.

• Journal of Life Science
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• v.33 no.8
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• pp.663-679
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• 2023

This review analyzes research trends related to new drug development using artificial intelligence from 2010 to 2022. This analysis organized the abstracts of 2,421 studies into a corpus, and words with high frequency and high connection centrality were extracted through preprocessing. The analysis revealed a similar word frequency trend between 2010 and 2019 to that between 2020 and 2022. In terms of the research method, many studies using machine learning were conducted from 2010 to 2020, and since 2021, research using deep learning has been increasing. Through these studies, we investigated the trends in research on artificial intelligence utilization by field and the strengths, problems, and challenges of related research. We found that since 2021, the application of artificial intelligence has been expanding, such as research using artificial intelligence for drug rearrangement, using computers to develop anticancer drugs, and applying artificial intelligence to clinical trials. This article briefly presents the prospects of new drug development research using artificial intelligence. If the reliability and safety of bio and medical data are ensured, and the development of the above artificial intelligence technology continues, it is judged that the direction of new drug development using artificial intelligence will proceed to personalized medicine and precision medicine, so we encourage efforts in that field.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.21-30
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• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

• Journal of Korea Entertainment Industry Association
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• v.13 no.4
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• pp.139-147
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• 2019

This study conducted bibliometric analysis on studies of Korean intangible cultural heritage dance in the Seoul area and it aimed to figure out the tendencies of that research. For this, a list of Korean intangible cultural heritage dance studies of 24 events was collected and analysis was conducted through the big data analysis solution of TEXTOM. Text mining was used as the method for analysis. Research results showed that first, most of the studies were conducted on the Bongsan Talchum and studies on teaching and learning methods were especially actively conducted. On the other hand, there were not many studies on Gut and the need for research vitalization in that area was confirmed. Second, in studies on Cheoyongmu events, the term'contemporary Cheoyongmu' was used frequently. This can be considered the use of meaningful terms with regard to intangible cultural heritage dance that has changed throughout history. At this, the vitalization of research that can reveal the typicality of dance is demanded from research of other events as well. Third, there was a notable amount of research that compared and analyzed dance styles with regard to the Munmyoilmu. This was seen as the result of discussions in the Korean dancing world regarding archetypal dance styles expanding into academic discussions. Therefore, it was revealed that academic discussions can connect to academic outcomes apart from whether the matter is right or wrong.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.112-119
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• 2023

Traditionally used for networking computers and communications, the Internet has been evolving from the beginning. Internet is the backbone for many things on the web including social media. The concept of social networking which started in the early 1990s has also been growing with the internet. Social Networking Sites (SNSs) sprung and stayed back to an important element of internet usage mainly due to the services or provisions they allow on the web. Twitter and Facebook have become the primary means by which most individuals keep in touch with others and carry on substantive conversations. These sites allow the posting of photos, videos and support audio and video storage on the sites which can be shared amongst users. Although an attractive option, these provisions have also culminated in issues for these sites like posting offensive material. Though not always, users of SNSs have their share in promoting hate by their words or speeches which is difficult to be curtailed after being uploaded in the media. Hence, this article outlines a process for extracting user reviews from the Twitter corpus in order to identify instances of hate speech. Through the use of MPCA (Modified Principal Component Analysis) and ECNN, we are able to identify instances of hate speech in the text (Enhanced Convolutional Neural Network). With the use of NLP, a fully autonomous system for assessing syntax and meaning can be established (NLP). There is a strong emphasis on pre-processing, feature extraction, and classification. Cleansing the text by removing extra spaces, punctuation, and stop words is what normalization is all about. In the process of extracting features, these features that have already been processed are used. During the feature extraction process, the MPCA algorithm is used. It takes a set of related features and pulls out the ones that tell us the most about the dataset we give itThe proposed categorization method is then put forth as a means of detecting instances of hate speech or abusive language. It is argued that ECNN is superior to other methods for identifying hateful content online. It can take in massive amounts of data and quickly return accurate results, especially for larger datasets. As a result, the proposed MPCA+ECNN algorithm improves not only the F-measure values, but also the accuracy, precision, and recall.