• Review of KIISC
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• v.31 no.5
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• pp.21-31
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• 2021

컴퓨터 기술의 발전에 따라 ML(Machine Learning) 및 AI(Artificial Intelligence)의 도입이 활발히 진행되고 있으며, 정보보호 분야에서도 활용이 증가하고 있는 추세이다. 그러나 이러한 모델들은 black-box 특성을 가지고 있으므로 의사결정 과정을 이해하기 어렵다. 특히, 오탐지 리스크가 큰 정보보호 환경에서 이러한 문제점은 AI 기술을 널리 활용하는데 상당한 장애로 작용한다. 이를 해결하기 위해 XAI(eXplainable Artificial Intelligence) 방법론에 대한 연구가 주목받고 있다. XAI는 예측의 해석이 어려운 AI의 문제점을 보완하기 위해 등장한 방법으로 AI의 학습 과정을 투명하게 보여줄 수 있으며, 예측에 대한 신뢰성을 제공할 수 있다. 본 논문에서는 이러한 XAI 기술의 개념 및 필요성, XAI 방법론의 정보보호 분야 적용 사례에 설명한다. 또한, XAI 평가 방법을 제시하며, XAI 방법론을 보안 시스템에 적용한 경우의 결과도 논의한다. XAI 기술은 AI 판단에 대한 사람 중심의 해석정보를 제공하여, 한정된 인력에 많은 분석데이터를 처리해야 하는 보안담당자들의 분석 및 의사결정 시간을 줄이는데 기여할 수 있을 것으로 예상된다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.325-327
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• 2024

국내외에서 AI기반 의료 솔루션 시장은 빠른 속도로 확장 중이며 이에 따른 다양한 의학 분야에서 많은 기법을 통한 의료 AI 시스템이 등장하고 있다. 그러나 기존 다양한 AI 연구가 이뤄짐에도 아직 중환자의 징후 예측에는 많은 어려움이 있다. 또한, 중환자의 경우 현재 의료진만으로 모든 환자를 필요한 시기에 진료하기엔 어려움이 있고 환자 상태 조기 예측이 필수적임을 관련 다양한 의학 기사를 통해 쉽게 인지할 수 있다. 본 연구에서는 위와 같은 문제점을 해결하고자 중환자의 진료 결과 데이터를 활용하여 환자의 진료 후 상태를 예측하는 모델을 생성하였다. '용인시산업진흥원'에서 제공하는 60만여 건에 달하는 환자 데이터를 수집하여, 중환자 상태 징후를 조기에 예측할 수 있는 머신러닝/딥러닝 기반 알고리즘으로 구현한 여러 모델에 대해 비교했을 때 딥러닝(DNN) 기반 모델이 약 92%의 분류 정확도를 측정할 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3330-3344
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• 2023

This paper proposes an antenna performance prediction model in the autonomous driving radar manufacturing process. Our research work is based upon a challenge dataset, Driving Radar Manufacturing Process Dataset, and a typical AutoML machine learning workflow engine, Pycaret open-source Python library. Note that the dataset contains the total 70 data-items, out of which 54 used as input features and 16 used as output features, and the dataset is properly built into resolving the multi-output regression problem. During the data regression analysis and preprocessing phase, we identified several input features having similar correlations and so detached some of those input features, which may become a serious cause of the multicollinearity problem that affect the overall model performance. In the training phase, we train each of output-feature regression models by using the AutoML approach. Next, we selected the top 5 models showing the higher performances in the AutoML result reports and applied the ensemble method so as for the selected models' performances to be improved. In performing the experimental performance evaluation of the regression prediction model, we particularly used two metrics, MAE and RMSE, and the results of which were 0.6928 and 1.2065, respectively. Additionally, we carried out a series of experiments to verify the proposed model's performance by comparing with other existing models' performances. In conclusion, we enhance accuracy for safer autonomous vehicles, reduces manufacturing costs through AutoML-Pycaret and machine learning ensembled model, and prevents the production of faulty radar systems, conserving resources. Ultimately, the proposed model holds significant promise not only for antenna performance but also for improving manufacturing quality and advancing radar systems in autonomous vehicles.

• Journal of Korea Game Society
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• v.20 no.1
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• pp.57-66
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• 2020

The goal is to implement AI using reinforcement learning, which replaces the less favored Supporter in MOBA games. ML_Agent implements game rules, environment, observation information, rewards, and punishment. The experiment was divided into P and C group. Experiments were conducted to compare the cumulative compensation values and the number of deaths to draw conclusions. In group C, the mean cumulative compensation value was 3.3 higher than that in group P, and the total mean number of deaths was 3.15 lower. performed cooperative play to minimize death and maximize rewards was confirmed.

• Microbiology and Biotechnology Letters
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• v.51 no.1
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• pp.69-82
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• 2023

Nitrilases are a hydrolase group of enzymes that catalyzes nitrile compounds and produce industrially important organic acids. The current objective is to optimize nitrilase production using statistical methods assisted with artificial intelligence (AI) tool from novel nitrile degrading isolate. A nitrile hydrolyzing bacteria Bacillus subtilis AGAB-2 (GenBank Ascension number- MW857547) was isolated from industrial effluent waste through an enrichment culture technique. The culture conditions were optimized by creating an orthogonal design with 7 variables to investigate the effect of the significant factors on nitrilase activity. On the basis of obtained data, an AI-driven support vector machine was used for the fitted regression, which yielded new sets of predicted responses with zero mean error and reduced root mean square error. The results of the above global optimization were regarded as the theoretical optimal function conditions. Nitrilase activity of 9832 ± 15.3 U/ml was obtained under optimized conditions, which is a 5.3-fold increase in compared to unoptimized (1822 ± 18.42 U/ml). The statistical optimization method involving Plackett Burman Design and Response surface methodology in combination with an AI tool created a better response prediction model with a significant improvement in enzyme production.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.12-14
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• 2021

For the past couple of years, the medical data has been stored in centralized systems which is not the ideal storage technique since all data can be altered, stolen, or even used for evil purposes and, furthermore, the data cannot be safely shared with other doctors and hospitals in case of patient's transfer, change of state or country, in addition, patient's health status cannot be tracked and the patient's medical history is unknown. Therefore, powerful decentralized technologies and expertise can help provide better health information and help doctors and patients to better understand the situations before and after treatment, and do more research based on immutable and trusted data. One of the proposed solutions is storing and securing data on the blockchain which is less scalable, slow and expensive. Introducing a scalable, robust medical data storage and sharing system based on AI/ML, IoT, IPFS, and blockchain.

• International Journal of Contents
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• v.15 no.4
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• pp.1-7
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• 2019

As the recent developments of artificial intelligence, particularly machine-learning, impact every aspect of society, they are also increasingly influencing creative fields manifested as new artistic tools and inspirational sources. However, as more artists integrate the technology into their creative works, the issues of diversity and fairness are also emerging in the AI-based creative practice. The data dependency of machine-learning algorithms can amplify the social injustice existing in the real world. In this paper, we present an interactive visualization system for raising the awareness of the diversity and fairness issues. Rather than resorting to education, campaign, or laws on those issues, we have developed a web & ML-based interactive data visualization system. By providing the interactive visual experience on the issues in interesting ways as the form of web content which anyone can access from anywhere, we strive to raise the public awareness of the issues and alleviate the important ethical problems. In this paper, we present the process of developing the ML-based interactive visualization system and discuss the results of this project. The proposed approach can be applied to other areas requiring attention to the issues.

• Tuberculosis and Respiratory Diseases
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• v.86 no.4
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• pp.251-263
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• 2023

The stethoscope has long been used for the examination of patients, but the importance of auscultation has declined due to its several limitations and the development of other diagnostic tools. However, auscultation is still recognized as a primary diagnostic device because it is non-invasive and provides valuable information in real-time. To supplement the limitations of existing stethoscopes, digital stethoscopes with machine learning (ML) algorithms have been developed. Thus, now we can record and share respiratory sounds and artificial intelligence (AI)-assisted auscultation using ML algorithms distinguishes the type of sounds. Recently, the demands for remote care and non-face-to-face treatment diseases requiring isolation such as coronavirus disease 2019 (COVID-19) infection increased. To address these problems, wireless and wearable stethoscopes are being developed with the advances in battery technology and integrated sensors. This review provides the history of the stethoscope and classification of respiratory sounds, describes ML algorithms, and introduces new auscultation methods based on AI-assisted analysis and wireless or wearable stethoscopes.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Journal of the Korean Society of Radiology
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• v.16 no.4
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• pp.453-462
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• 2022

In this paper, the artificial intelligence (AI) technology used in the medical image analysis field was analyzed through a literature review. Literature searches were conducted on PubMed, ResearchGate, Google and Cochrane Review using the key word. Through literature search, 114 abstracts were searched, and 98 abstracts were reviewed, excluding 16 duplicates. In the reviewed literature, AI is applied in classification, localization, disease detection, disease segmentation, and fit degree of registration images. In machine learning (ML), prior feature extraction and inputting the extracted feature values into the neural network have disappeared. Instead, it appears that the neural network is changing to a deep learning (DL) method with multiple hidden layers. The reason is thought to be that feature extraction is processed in the DL process due to the increase in the amount of memory of the computer, the improvement of the calculation speed, and the construction of big data. In order to apply the analysis of medical images using AI to medical care, the role of physicians is important. Physicians must be able to interpret and analyze the predictions of AI algorithms. Additional medical education and professional development for existing physicians is needed to understand AI. Also, it seems that a revised curriculum for learners in medical school is needed.