• Journal of Korean Dental Science
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• v.15 no.2
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• pp.101-111
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• 2022

The purpose of laboratory tests in the field of oral medicine can be divided into two categories: (1) medical evaluation of patients with systemic diseases that are planning to receive dental care and (2) diagnosis of patients with certain oral diseases. First, laboratory tests are commonly used to evaluate patients with systemic diseases who need dental management. A combination of multiple tests is usually prescribed as a test panel to diagnose and assess a specific disease. Test panels closely related to oral medicine include those for rheumatoid arthritis, connective tissue disease/lupus, liver function, thyroid screening, anemia, and bleeding disorders. Second, laboratory tests are used as auxiliary diagnostic methods for certain oral diseases. They often provide crucial diagnostic information for infectious diseases caused by bacteria, fungi, and viruses that are associated with pathology in the oral and maxillofacial regions. Laboratory tests for infectious diseases are composed of growth-dependent methods, immunologic assays, and molecular biology. As the field develops, further application of laboratory tests, including synovial fluid analysis in temporomandibular joint disorders, salivary diagnostics, and hematologic biomarkers associated with temporomandibular disorders and orofacial pain conditions, is currently under scrutiny for their reliability as diagnostic tools.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.6_2
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• pp.1017-1027
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• 2022

Development of social overhead capital (SOC) requires huge national finance, and performance issues such as cost-efficiency, safety, and environment have been constantly raised. However, currently each construction client has limited access to its own projects' performance without analytic methodology for industry-level comparisons and benchmarking for improvement. To overcome this problem, this study proposes a comprehensive performance analysis framework for post-evaluation of large-scale construction projects. To this end, this study performed a case study of advanced countries (the U.S., the U.K. and Japan) and consultation with related experts to develop a tailored performance analysis framework for the Post- Construction Evaluation and Management system in Korea. The developed framework covers three categories (project performance, project efficiency, and ripple effect), nine areas (cost, schedule, change, safety, quality, demand, benefit-cost ratio, civil complaint, and defect), and 31 detailed metrics. Using industry-level project performance database and statistical techniques, the proposed framework can be used not only to diagnose excellent and unsatisfactory performance areas for completed construction projects, but also to provide reference data for future similar projects. This study can contribute to the improvement of clients' performance management practices and effectiveness of construction projects.

• ETRI Journal
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• v.44 no.4
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.425-427
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• 2022

The grade of glioma is important information related to survival and thus is important to classify the grade of glioma before treatment to evaluate tumor progression and treatment planning. Glioma grading is mostly divided into high-grade glioma (HGG) and low-grade glioma (LGG). In this study, image preprocessing techniques are applied to analyze magnetic resonance imaging (MRI) using the deep neural network model. Classification performance of the deep neural network model is evaluated. The highest-performance EfficientNet-B6 model shows results of accuracy 0.9046, sensitivity 0.9570, specificity 0.7976, AUC 0.8702, and F1-Score 0.8152 in 5-fold cross-validation.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.73-88
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• 2023

Brain tumors can also be an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis is critical to the success of treatment planning, and resonant imaging is the primary diagnostic imaging method used to diagnose brain tumors and their extent. Deep learning methods for computer vision applications have shown significant improvements in recent years, primarily due to the undeniable fact that there is a large amount of data on the market to teach models. Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using these deep learning techniques has made great strides by providing reliable, annotated open data sets. Reduce computational effort and learn specific spatial and temporal relationships. This white paper describes transfer models such as the MobileNet model, VGG19 model, InceptionResNetV2 model, Inception model, and DenseNet201 model. The model uses three different optimizers, Adam, SGD, and RMSprop. Finally, the pre-trained MobileNet with RMSprop optimizer is the best model in this paper, with 0.995 accuracies, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational cost.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.819-822
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• 2021

AI World Cup 환경은 다수 가상 에이전트들이 팀을 이뤄서 서로 상호작용하며 대전이 가능한 가상 축구 환경이다. 본 논문에서는 AI World Cup 환경에서 멀티 에이전트기반 학습/추론 기술을 사용하여 다양한 전략과 전술을 구사하는 가상 축구 에이전트 구현과 시뮬레이션 결과를 소개한다. 먼저, 역할을 바탕으로 협동하여 상대방과 대전할 수 있는 논리 기반 추론형 멀티 에이전트 기술이 적용된 Dynamic planning 축구 에이전트 9 세트를 구현하였다. 이후, 강화학습 에이전트 기반, 단일 에이전트를 조합한 Independent Q-Learning 방식의 학습형 축구 에이전트를 구현한 후, 이를 멀티 에이전트 강화학습으로 확장하여 역할 기반 전략 학습이 가능한 가상 축구 에이전트를 구현하고 시뮬레이션 하였다. 구현된 가상 축구 에이전트들 간 대전을 통해 승률을 확인하고, 전략의 우수성을 분석하였다. 시뮬레이션 예제는 다음에서 확인할 수 있다 (https://github.com/I-hate-Soccer/Simulation).

• Journal of Smart Tourism
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• v.3 no.1
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• pp.41-47
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• 2023

Tourism and hospitality have encountered significant changes in recent years as a result of the rapid development of information technology (IT). Customers now expect more expedient services and customized travel experiences, which has intensified competition among service providers. To meet these demands, businesses have adopted sophisticated IT applications such as ChatGPT, which enables real-time interaction with consumers and provides recommendations based on their preferences. This paper focuses on the AI support-prompt middleware system, which functions as a mediator between generative AI and human users, and discusses two operational rules associated with it. The first rule is the Information Processing Rule, which requires the middleware system to determine appropriate responses based on the context of the conversation using techniques for natural language processing. The second rule is the Information Presentation Rule, which requires the middleware system to choose an appropriate language style and conversational attitude based on the gravity of the topic or the conversational context. These rules are essential for guaranteeing that the middleware system can fathom user intent and respond appropriately in various conversational contexts. This study contributes to the planning and analysis of service design by deriving design rules for middleware systems to incorporate artificial intelligence into tourism services. By comprehending the operation of AI support-prompt middleware systems, service providers can design more effective and efficient AI-driven tourism services, thereby improving the customer experience and obtaining a market advantage.

• International Journal of High-Rise Buildings
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• v.11 no.2
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• pp.87-102
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• 2022

In Japan, the population progresses to the extreme aging society and it is entering the phase of the population decrease while the population increase is continuing in the world. The construction market is expected to shrink accordingly, however the situation of labor shortage is expected to continue at a faster rate, because the aging of construction workers is progressing and new younger labor force cannot be secured. In order to supplement the labor shortage, it is required to progress mechanization, automation, labor saving, and efficiency improvement by utilizing the information well in each stage in a series of flow of planning, design, construction, operation, and disassembly in one building. The measures to maintain and expand the construction market by the new efficiency improvement techniques which enhance the utilization degree of building information are required. Currently, the elemental technologies which utilized BIM (Building Information Modeling) are accumulated by advancing digitization in each phase. DX (Digital transformation) in the construction industry can be achieved by the technology maturing and having a series of continuities. It is anticipated that this will evolve to a new method which is unprecedented. Present status of BIM and mechanized constructions in Taisei Construction are introduced, and future prospect is described.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.63-73
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• 2023

This study explores modern portfolio theory by integrating the Black-Litterman portfolio with time-series clustering, specificially emphasizing K-shape clustering methodology. K-shape clustering enables grouping time-series data effectively, enhancing the ability to plan and manage investments in stock markets when combined with the Black-Litterman portfolio. Based on the patterns of stock markets, the objective is to understand the relationship between past market data and planning future investment strategies through backtesting. Additionally, by examining diverse learning and investment periods, it is identified optimal strategies to boost portfolio returns while efficiently managing associated risks. For comparative analysis, traditional Markowitz portfolio is also assessed in conjunction with clustering techniques utilizing K-Means and K-Means with Dynamic Time Warping. It is suggested that the combination of K-shape and the Black-Litterman model significantly enhances portfolio optimization in the stock market, providing valuable insights for making stable portfolio investment decisions. The achieved sharpe ratio of 0.722 indicates a significantly higher performance when compared to other benchmarks, underlining the effectiveness of the K-shape and Black-Litterman integration in portfolio optimization.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.spc
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• pp.1-8
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• 2023

To investigate the national science and technology policy direction in the field of construction machinery, an analysis was conducted on projects selected as national research and development (R&D) initiatives by the government. Assuming that the project titles contain key keywords, text mining was employed to substantiate this assumption. Project information data spanning nine years from 2014 to 2022 was collected through the National Science & Technology Information Service (NTIS). To observe changes over time, the years were divided into three-year sections. To analyze research trends efficiently, keywords were categorized into groups: 'equipment,' 'smart,' and 'eco-friendly.' Based on the collected data, keyword frequency analysis, N-gram analysis, and topic modeling were performed. The research findings indicate that domestic government R&D in the construction machinery field primarily focuses on smart-related research and development. Specifically, investments in monitoring systems and autonomous operation technologies are increasing. This study holds significance in analyzing objective research trends through the utilization of big data analysis techniques and is expected to contribute to future research and development planning, strategic formulation, and project management.