• YAKHAK HOEJI
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• v.43 no.4
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• pp.494-501
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• 1999

The skin whitening effects of pine needle extract, hop extract and chestnut inner shell extract were evaluated both in vitro and in B 16 mouse melanoma cell lines. Each extracts significantly inhibited tyrosinase activity, dopa auto-oxidation and melanin biosynthesis in vitro and in B 16 cell lines. In vitro, hop extract inhibited melanin biosynthesis 15 times stronger than kojic acid at $10{\;}\mu\textrm{g}/ml$ concentration. Each extracts were stronger inhibitors of melanin biosynthesis than kojic acid in B 16 mouse melanoma cell at less than $4{\;}\mu\textrm{g}/ml$ concentration. These results show that extracts fo pine needle, hop and chestnut inner shell could be developed as skin whitening component of cosmetics.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.56-62
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• 2019

Recently, research using artificial neural networks has further expanded the field of neural network optimization and automatic structuring from improving inference accuracy. The performance of the machine learning algorithm depends on how the hyperparameters are configured. Open-source hyperparameter optimization software can be an important step forward in improving the performance of machine learning algorithms. In this paper, we review open-source hyperparameter optimization softwares.

• International Journal of Advanced Culture Technology
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• v.8 no.2
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• pp.297-304
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• 2020

Recently, open source automatic machine learning solutions have been applied in many fields. To apply open source automated machine learning to real world problems, you need to write code with expertise in machine learning. Writing code without machine learning knowledge is challenging. To solve this problem, the automatic machine learning solutions provided by startups are made easy to use with a clean user interface. In this paper, we review automatic machine learning solutions of startups.

• Journal of Microbiology and Biotechnology
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• v.29 no.8
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• pp.1177-1183
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• 2019

Grapefruit seed extract (GSE) is a safe and effective preservative that is used widely in the food industry. However, there are few studies addressing the anti-biofilm effect of GSE. In this study, the anti-biofilm effect of GSE was investigated against biofilm-forming strains of Staphylococcus aureus and Escherichia coli. The GSE minimum inhibitory concentration (MIC) for S. aureus and E. coli were $25{\mu}g/ml$ and $250{\mu}g/ml$, respectively. To investigate biofilm inhibition and degradation effect, crystal violet assay and stainless steel were used. Biofilm formation rates of four strains (S. aureus 7, S. aureus 8, E. coli ATCC 25922, and E. coli O157:H4 FRIK 125) were 55.8%, 70.2%, 55.4%, and 20.6% at $1/2{\times}MIC$ of GSE, respectively. The degradation effect of GSE on biofilms attached to stainless steel coupons was observed (${\geq}1$ log CFU/coupon) after exposure to concentrations above the MIC for all strains and $1/2{\times}MIC$ for S. aureus 7. In addition, the specific mechanisms of this anti-biofilm effect were investigated by evaluating hydrophobicity, auto-aggregation, exopolysaccharide (EPS) production rate, and motility. Significant changes in EPS production rate and motility were observed in both S. aureus and E. coli in the presence of GSE, while changes in hydrophobicity were observed only in E. coli. No relationship was seen between auto-aggregation and biofilm formation. Therefore, our results suggest that GSE might be used as an anti-biofilm agent that is effective against S. aureus and E. coli.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.391-392
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• 2021

해마다 실내 냉방기 냉매누설 문제가 고질적으로 반복되며 소비자들의 피해도 커져가고 있다. 특히 제조사와 설치 업체가 다른 경우 냉매 누수의 원인이 제품인지, 설치하자인지 책임소재를 두고 갈등을 빚는 경우가 빈번하다. 이에 더 이상 소비자들의 피해를 막기 위해 냉매누설 검출 방안 마련이 필요해 보인다. 본 연구에서는 실내 냉방기 설치 후 냉매누설 검출을 위한 별도의 하드웨어 장치 추가 없이 냉방기의 운영을 위해 설치된 센서들의 값을 이용하여 냉매누설의 유무를 판단할 수 있는 방안을 제안하는 것을 목적으로 한다. 데이터 분석을 위하여 제조사의 제품 출하 전 현장 테스트 단계에서 측정한 온도값, 전류값, 습도값을 취합하여 데이터 셋을 구축하였다. 이때 자동화된 머신러닝(AutoML)을 이용하여 데이터의 80%를 훈련 데이터로 20%를 테스트 데이터로 사용하여 냉매량 80%는 1, 그 이하는 0으로 훈련시켰다. 구축한 데이터 셋을 이용하여 훈련시킨 결과 99% 정확도로 냉매누설 검출을 분별할 수 있었다. 또한 냉매누설과 관련성이 높은 중요 특징 4개를 추출할 수 있었다. 본 연구를 통하여 별도의 하드웨어 장치 추가 없이 소프트웨어적인 접근 방법으로 문제를 해결할 수 있는 feasibility를 확인할 수 있었다.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.267-273
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• 2024

Using a cloud-based vertex AI platform that can develop an artificial intelligence learning model without coding, this study easily developed an artificial intelligence learning model by the non-professional general public and confirmed its clinical applicability. Nine dental diseases and 2,999 root disease X-ray images released on the Kaggle site were used for the learning data, and learning, verification, and test data images were randomly classified. Image classification and multi-label learning were performed through hyper-parameter tuning work using a learning pipeline in vertex AI's basic learning model workflow. As a result of performing AutoML(Automated Machine Learning), AUC(Area Under Curve) was found to be 0.967, precision was 95.6%, and reproduction rate was 95.2%. It was confirmed that the learned artificial intelligence model was sufficient for clinical diagnosis.

• Journal of KIISE:Software and Applications
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• v.33 no.2
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• pp.143-153
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• 2006

Microarray which enables us to obtain hundreds and thousands of expression of gene or genotype at once is an epoch-making technology in comparative analysis of genes. First of all, we have to measure the intensity of each gene in an microarray image from the experiment to gain the expression level of each gene. But it is difficult to analyze the microarray image in manual because it has a lot of genes. Meta-gridding method and various auto-gridding methods have been proposed for this, but thew still have some problems. For example, meta-gridding requires manual-work due to some variations in spite of experiment in same microarray, and auto-gridding nay not carried out fully or correctly when an image has a lot of noises or is lowly expressed. In this article, we propose Hierarchical Grid Alignment algorithm for new methodology combining meta-gridding method with auto-gridding method. In our methodology, we necd a meta-grid as an input, and then align it with the microarray image automatically. Experimental results show that the proposed method serves more robust and reliable gridding result than the previous methods. It is also possible for user to do more reliable batch analysis by using our algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.1-6
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• 2021

Most of the match-3 puzzle games supports automatic play using the MCTS algorithm. However, implementing reinforcement learning agents is not an easy job because it requires both the knowledge of machine learning and the way of complex interactions within the development environment. This study proposes a method in which we can easily design reinforcement learning agents and implement game play agents by applying PPO(Proximal Policy Optimization) algorithms. And we could identify the performance was increased about 44% than the conventional method. The tools we used are the Unity 3D game engine and Unity ML SDK. The experimental result shows that agents became to learn game rules and make better strategic decisions as experiments go on. On average, the puzzle gameplay agents implemented in this study played puzzle games better than normal people. It is expected that the designed agent could be used to speed up the game level design process.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.50 no.4
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• pp.216-221
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• 2024

Objectives: This study aimed to develop and validate a model to predict the need for intensive care unit (ICU) admission in patients with dental infections using an automated machine learning (ML) program called H2O-AutoML. Materials and Methods: Two models were created using only the information available at the initial examination. Model 1 was parameterized with only clinical symptoms and blood tests, excluding contrast-enhanced multi-detector computed tomography (MDCT) images available at the initial visit, whereas model 2 was created with the addition of the MDCT information to the model 1 parameters. Although model 2 was expected to be superior to model 1, we wanted to independently determine this conclusion. A total of 210 patients who visited the Department of Oral and Maxillofacial Surgery at the Dankook University Dental Hospital from March 2013 to August 2023 was included in this study. The patients' demographic characteristics (sex, age, and place of residence), systemic factors (hypertension, diabetes mellitus [DM], kidney disease, liver disease, heart disease, anticoagulation therapy, and osteoporosis), local factors (smoking status, site of infection, postoperative wound infection, dysphagia, odynophagia, and trismus), and factors known from initial blood tests were obtained from their medical charts and retrospectively reviewed. Results: The generalized linear model algorithm provided the best diagnostic accuracy, with an area under the receiver operating characteristic values of 0.8289 in model 1 and 0.8415 in model 2. In both models, the C-reactive protein level was the most important variable, followed by DM. Conclusion: This study provides unprecedented data on the use of ML for successful prediction of ICU admission based on initial examination results. These findings will considerably contribute to the development of the field of dentistry, especially oral and maxillofacial surgery.