• The Journal of Korean Obstetrics and Gynecology
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• v.34 no.1
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• pp.1-14
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• 2021

Objectives: We studied for the adjustment of the patterns of 'The Diagnostic Tool for Climacteric and Postmenopausal Syndrome Pattern Identification (hereinafter CaPSPI)' (studyI) and the correlation between CaPSPI and Korean medicine doctors' diagnosis which was carried out without knowing the results of CaPSPI (studyII). Methods: The studyI followed the previous study method in 2018 (2018-3). The studyII was conducted from June 1, 2019 to July 10, 2020 with ◯◯ University Korean Medicine Hospital IRB's approval (2019-4). Doctors' diagnosis was conducted face-to-face with the subjects. Doctors' diagnosis was carried out based on the Kupperman's questionnaire, 'Diagnosis System of Oriental Medicine (hereinafter DSOM)' and four examinations (四診) records. The diagnosis was marked with 0 for 'no', 1 for 'somewhat', 2 for 'yes' and 3 for 'very yes'. The correlation between CaPSPI and the mean of doctors diagnostic scores were investigated statistically. Results: The studyI showed that heart-heat (心火) pattern was added. The Factor loading coefficient for heart-heat was 0.551 to 0.789, and the Cronbach's coefficient was 0.896. The studyII showed that the diagnosis (Kappa statistic) of two doctors showed statistically significant concordance (all eight patterns), with correlation of them were 0.3 or higher. And the correlation between the CaPSPI score and the mean of doctors' diagnostic score showed a statistically significant correlation, with liver qi depression (肝鬱) being the highest at 0.552 and dual deficiency of the heart-spleen (心脾兩虛) being the lowest at 0.301. Conclusions: Since the diagnosis results of CaPSPI showed a significant correlation with the diagnosis of Korean traditional medicine experts, it was believed that the CaPSPI results can be trusted and used for clinical purposes.

• Korean Journal of Construction Engineering and Management
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• v.23 no.4
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• pp.15-25
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• 2022

As the incidence and mortality of serious disasters in the construction industry are the highest, various efforts are being made in Korea to reduce them. Among them, risk assessment is used as data for disaster reduction measures and evaluation of risk factors at the construction stage. However, the existing risk assessment involves the subjectivity of the performer and is vulnerable to the domestic construction site. This study established a DB classification system for risk assessment with the aim of early identification and pre-removal of risks by quantitatively deriving risk factors using BIM in the risk assessment field and presents a methodology for risk assessment using BIM. Through this, prior removal of risks increases the safety of construction workers and reduces additional costs in the field of safety management. In addition, since it can be applied to new construction methods, it improves the understanding of project participants and becomes a tool for communication. This study proposes a framework for deriving quantitative risks based on BIM, and will be used as a base technology in the field of risk assessment using BIM in the future.

• 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.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.217-242
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• 2022

With the launch of Artificial Intelligence(AI)-based intelligent products on the market, innovative changes are taking place not only in business but also in consumers' daily lives. Intelligent products have the potential to realize technology differentiation and increase market competitiveness through advanced functions of artificial intelligence. However, there is no new product development methodology that can sufficiently reflect the characteristics of artificial intelligence for the purpose of developing intelligent products with high market acceptance. This study proposes a KANO-QFD integrated model as a methodology for intelligent product development. As a specific example of the empirical analysis, the types of consumer requirements for hair loss prediction and treatment device were classified, and the relative importance and priority of engineering characteristics were derived to suggest the direction of intelligent medical product development. As a result of a survey of 130 consumers, accurate prediction of future hair loss progress, future hair loss and improved future after treatment realized and viewed on a smartphone, sophisticated design, and treatment using laser and LED combined light energy were realized as attractive quality factors among the KANO categories. As a result of the analysis based on House of Quality of QFD, learning data for hair loss diagnosis and prediction, micro camera resolution for scalp scan, hair loss type classification model, customized personal account management, and hair loss progress diagnosis model were derived. This study is significant in that it presented directions for the development of artificial intelligence-based intelligent medical product that were not previously preceded.

• The Journal of Internal Korean Medicine
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• v.44 no.3
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• pp.313-353
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• 2023

Objectives: Heart failure is a chronic disease with increasing prevalence rates despite advancements in medical technology. Korean medicine utilizes herbal prescriptions to treat heart failure, but little is known about the specific herbal medicines comprising the network of herbal prescriptions for heart failure. This study proposes a novel methodology that can efficiently develop prescriptions and facilitate experimental research on heart failure by utilizing existing databases. Methods: Herbal medicine prescriptions for heart failure were identified through a PubMed search and compiled into a Google Sheet database. NetMiner 4 was used for network analysis, and the individual networks were classified according to the herbal medicine classification system to identify trends. K-HERB NETWORK was utilized to derive related prescriptions. Results: Network analysis of heart failure prescriptions and herbal medicines using NetMiner 4 produced 16 individual networks. Uhwangcheongsim-won (牛黃淸心元), Gamiondam-tang (加味溫膽湯), Bangpungtongseong-san (防風通聖散), and Bunsimgi-eum (分心氣飮) were identified as prescriptions with high similarity in the entire network. A total of 16 individual networks utilized K-HERB NETWORK to present prescriptions that were most similar to existing prescriptions. The results provide 1) an indication of existing prescriptions with potential for use to treat heart failure and 2) a basis for developing new prescriptions for heart failure treatment. Conclusion: The proposed methodology presents an efficient approach to developing new heart failure prescriptions and facilitating experimental research. This study highlights the potential of network pharmacology methodology and its possible applications in other diseases. Further studies on network pharmacology methodology are recommended.

• Journal of Environmental Health Sciences
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• v.49 no.3
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• pp.134-148
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• 2023

Background: The conducting of health effect surveys (HESs) in environmentally contaminated vulnerable areas (ECVAs) by the central and local governments has been increasing apace with the increase in demand for HESs since the Environmental Health Act was enacted in South Korea in 2008. Objectives: This study aimed to review the HESs of residents in ECVAs conducted in South Korea. Methods: An analysis was performed on 125 reports obtained from the Environment Digital Library, PRISM, and local government websites after selecting from 803 projects obtained as ECVAs from the Korea ON-Line E-Procurement System (1997~2021), National Institute Environment Research (2000~2021), and Korea Environmental Industry and Technology Institute (2009~2021). The reports were classified by background (residents' demand, HES, and more), research design (cross-sectional study, cohort, ecological study, and panel), pollution source (abandoned metal mine (AMM), industrial complex (IC), and more), and assessment method of exposure and health effects. The survey area was converted into administrative district codes for geographical mapping. Results: There were 37, 34, 18, and 10 cases associated with AMM, IC, waste incinerators, and coal-fired power plants, respectively. Most of the studies conducted were cross-sectional studies and ecological studies. The proportion of epidemiological investigations by residents' demand showed an increase from 0.0% to 8.9% for the central government while decreasing from 16.7% to 14.3% for local governments after 2008 compared to before 2008. HESs increased at both the central and local government levels since 2014. For the evaluation method, 365 environmental hazards, 319 health outcomes, and 302 biological markers were investigated, with the most commonly investigated items being metals, cancer, and blood metals. Conclusions: HESs of residents in ECVAs in South Korea have been continuously developed both quantitatively and qualitatively. Future improvements are expected, and systematic review and classification of the HESs is warranted.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.

• Journal of Korean Society of Archives and Records Management
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• v.23 no.4
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• pp.137-157
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• 2023

The scale of research and development (R&D) investment is increasing to strengthen national competitiveness through technological innovation, leading to an increased interest in investment efficiency. In records management, the National Archives of Korea has been leading the national research and development project since 2008. Accordingly, this study analyzed R&D projects in records management regarding implementing organization, performance or outcomes, and subjects, targeting 111 National Archives of Korea contract research projects from 2008 to 2022. The analysis showed that small and medium-sized enterprises (SMEs) were the most likely to conduct research, the majority of the research outcomes were academic publications, and there were some discrepancies between the reported performance in research and the actual performance. In terms of research subjects, the most common type of records are paper or print documents, establishing an electronic management system among the National Archives' works. In terms of the frequency of keywords in the records management process and research projects, it was found that research was mainly conducted on "preservation." Meanwhile, only 10 cases, or 9% of the 111 projects, were found to be relevant in terms of utilizing big data and developing intelligent technologies related to digital transformation. Therefore, the effectiveness of the R&D project must be improved through follow-up management of the results even after the research project is completed. In addition, in terms of research topics, it was identified that aside from "preservation," studies focusing on "transfer," "classification," "evaluation," and "collection," as well as research that responds to digital transformation, are needed.

• Tuberculosis and Respiratory Diseases
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• v.87 no.2
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• pp.176-184
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• 2024

Background: Results of studies investigating the association between body mass index (BMI) and mortality in patients with coronavirus disease-2019 (COVID-19) have been conflicting. Methods: This multicenter, retrospective observational study, conducted between January 2020 and August 2021, evaluated the impact of obesity on outcomes in patients with severe COVID-19 in a Korean national cohort. A total of 1,114 patients were enrolled from 22 tertiary referral hospitals or university-affiliated hospitals, of whom 1,099 were included in the analysis, excluding 15 with unavailable height and weight information. The effect(s) of BMI on patients with severe COVID-19 were analyzed. Results: According to the World Health Organization BMI classification, 59 patients were underweight, 541 were normal, 389 were overweight, and 110 were obese. The overall 28-day mortality rate was 15.3%, and there was no significant difference according to BMI. Univariate Cox analysis revealed that BMI was associated with 28-day mortality (hazard ratio, 0.96; p=0.045), but not in the multivariate analysis. Additionally, patients were divided into two groups based on BMI ≥25 kg/m² and underwent propensity score matching analysis, in which the two groups exhibited no significant difference in mortality at 28 days. The median (interquartile range) clinical frailty scale score at discharge was higher in nonobese patients (3 [3 to 5] vs. 4 [3 to 6], p<0.001). The proportion of frail patients at discharge was significantly higher in the nonobese group (28.1% vs. 46.8%, p<0.001). Conclusion: The obesity paradox was not evident in this cohort of patients with severe COVID-19. However, functional outcomes at discharge were better in the obese group.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.343-350
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• 2024

Since the release of Meta's Segment Anything Model (SAM), a large-scale vision transformer generation model with rapid image segmentation capabilities, several studies have been conducted to apply this technology in various fields. In this study, we aimed to investigate the applicability of SAM for water bodies detection and extraction using the QGIS Geo-SAM plugin, which enables the use of SAM with satellite imagery. The experimental data consisted of Compact Advanced Satellite 500 (CAS500)-1 images. The results obtained by applying SAM to these data were compared with manually digitized water objects, Open Street Map (OSM), and water body data from the National Geographic Information Institute (NGII)-based hydrological digital map. The mean Intersection over Union (mIoU) calculated for all features extracted using SAM and these three-comparison data were 0.7490, 0.5905, and 0.4921, respectively. For features commonly appeared or extracted in all datasets, the results were 0.9189, 0.8779, and 0.7715, respectively. Based on analysis of the spatial consistency between SAM results and other comparison data, SAM showed limitations in detecting small-scale or poorly defined streams but provided meaningful segmentation results for water body classification.