• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1283-1292
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• 2022

At present, several artificial intelligence projects in the healthcare and medical field are competing with each other, and the interfaces between the systems lack unified specifications. Thus, this study presents an artificial intelligence platform for healthcare and medical fields which adopts the deep learning technology to provide algorithms, models and service support for the health and medical enterprise applications. The suggested platform can provide a large number of heterogeneous data processing, intelligent services, model managements, typical application scenarios, and other services for different types of business. In connection with the suggested platform application, we represents a medical service which is corresponding to the trusted and comprehensible tracking and analyzing patient behavior system for Health and Medical treatment using Internet of Behavior concept.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.91-94
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• 2023

According to most experts and health workers, a living creature's body heat is little understood and crucial in the identification of disorders. Doctors in ancient medicine used wet mud or slurry clay to heal patients. When either of these progressed throughout the body, the area that dried up first was called the infected part. Today, thermal cameras that generate images with electromagnetic frequencies can be used to accomplish this. Thermography can detect swelling and clot areas that predict cancer without the need for harmful radiation and irritational touch. It has a significant benefit in medical testing because it can be utilized before any observable symptoms appear. In this work, machine learning (ML) is defined as statistical approaches that enable software systems to learn from data without having to be explicitly coded. By taking note of these heat scans of breasts and pinpointing suspected places where a doctor needs to conduct additional investigation, ML can assist in this endeavor. Thermal imaging is a more cost-effective alternative to other approaches that require specialized equipment, allowing machines to deliver a more convenient and effective approach to doctors.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.693-700
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• 2023

Sentiment analysis, a branch of natural language processing that classifies and identifies subjective opinions and emotions in text documents as positive or negative, can be used for various promotions and services through customer preference analysis. To this end, recent research has been conducted utilizing various techniques in machine learning and deep learning. In this study, we propose an optimal language model by comparing the accuracy of sentiment analysis for movie, product, and game reviews using existing RNN-based models and recent Transformer-based language models. In our experiments, LMKorBERT and GPT3 showed relatively good accuracy among the models pre-trained on the Korean corpus.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.133-135
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• 2021

본 논문에서는 목조 문화재 영상에서의 변위 현상 중 하나인 크랙이 발생하는 영역을 감지하기 위한 임베딩 유사도 기반 모델을 제안한다. 우선 변위가 존재하지 않는 정상으로만 구성된 학습 이미지는 사전 학습된 합성 곱 신경망을 통과하여 임베딩 벡터들을 추출한다. 그 이후 임베딩 벡터들을 가지고 정상 클래스에 대한 분포의 파라미터 값을 구한다. 실제 추론 과정에 사용되는 테스트 이미지에 대해서도 마찬가지로 임베딩 벡터를 구한다. 그런 다음 테스트 이미지의 임베딩 벡터와 이전에 구한 정상 클래스를 대표하는 가우시안 분포 정보와의 거리를 계산하여 이상치 맵을 생성하여 최종적으로 변위가 존재하는 영역을 감지한다. 데이터 셋으로는 충주시 근처의 문화재에 방문해서 수집한 목조 문화재 이미지를 가지고 정상 및 비정상으로 구분한 데이터 셋을 사용하였다. 실험 결과 우리가 제안한 임베딩 유사도 기반 모델이 목조 문화재에서 크랙이 발생하는 변위 영역을 잘 감지함을 확인하였다. 이러한 결과로부터 우리가 제안한 방법이 목재 문화재의 크랙 현상에 대한 변위 영역 검출에 있어서 매우 적합함을 보여준다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.141-143
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• 2021

본 논문에서는 목조 문화재의 변위량을 감지할 수 있는 앙상블 딥러닝 모델 모델을 제안한다. 우선 총 2개의 서로 다른 사전 학습된 합성 곱 신경망을 사용하여 입력 영상에 대한 심층 특징들을 추출한다. 그 이후 2개의 서로 다른 심층 특징들을 결합하여 하나의 특징 벡터를 생성한다. 그 이후 합쳐진 특징 벡터는 완전 연결 계층의 입력 값으로 들어와서 최종적으로 변위의 심각 단계에 대한 예측을 수행하게 된다. 데이터 셋으로는 충주시 근처의 문화재에 방문해서 수집한 목조 문화재 이미지를 가지고 정상 및 비정상으로 구분한 데이터 셋을 사용하였다. 실험 결과 앙상블 딥러닝 기법을 사용한 모델이 앙상블 기법을 사용하지 않는 모델보다 더 좋은 성능을 나타냄을 확인하였다. 이러한 결과로부터 우리가 제안한 방법이 목재 문화재의 변위량 예측에 있어서 매우 적합함을 보여준다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.861-868
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• 2023

One of the key technologies in providing data analysis in the deep learning while maintaining security is fully homomorphic encryption. Due to constraints in operations on fully homomorphically encrypted data, non-arithmetic functions used in deep learning must be approximated by polynomials. Until now, the degrees of approximation polynomials with composite minimax polynomials have been uniformly set across layers, which poses challenges for effective network designs on fully homomorphic encryption. This study theoretically proves that setting different degrees of approximation polynomials constructed by composite minimax polynomial in each layer does not pose any issues in the inference on convolutional neural networks.

• Journal of Korean Society for Quality Management
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• v.51 no.3
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• pp.363-379
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• 2023

Purpose: The main objective of this research is to construct an AI-based Composite Supplementary Index (ACSI) model to achieve accurate predictions of the Composite Index of Business Indicators. By incorporating various economic indicators as independent variables, the ACSI model enables the prediction and analysis of both the leading index (CLI) and coincident index (CCI). Methods: This study proposes an AI-based Composite Supplementary Index (ACSI) model that leverages diverse economic indicators as independent variables to forecast leading and coincident economic indicators. To evaluate the model's performance, advanced machine learning techniques including MLP, RNN, LSTM, and GRU were employed. Furthermore, the study explores the potential of employing deep learning models to train the weights associated with the independent variables that constitute the composite supplementary index. Results: The experimental results demonstrate the superior accuracy of the proposed composite supple- mentary index model in predicting leading and coincident economic indicators. Consequently, this model proves to be highly effective in forecasting economic cycles. Conclusion: In conclusion, the developed AI-based Composite Supplementary Index (ACSI) model successfully predicts the Composite Index of Business Indicators. Apart from its utility in management, economics, and investment domains, this model serves as a valuable indicator supporting policy-making and decision-making processes related to the economy.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.97-115
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• 2023

In South Korea, chronic kidney disease(CKD) impacts around 4.6 million adults, leading to a high reliance on hemodialysis. For effective dialysis, vascular access is crucial, with decisions about vascular surgeries often made during dialysis sessions. Anticipating these needs could improve dialysis quality and patient comfort. This study investigates the use of Artificial Intelligence(AI) to predict the timing of surgeries for dialysis vessels, an area not extensively researched. We've developed an AI algorithm using predictive maintenance methods, transitioning from machine learning to a more advanced deep learning approach with Long Short-Term Memory(LSTM) models. The algorithm processes variables such as venous pressure, blood flow, and patient age, demonstrating high effectiveness with metrics exceeding 0.91. By shortening the data collection intervals, a more refined model can be obtained. Implementing this AI in clinical practice could notably enhance patient experience and the quality of medical services in dialysis, marking a significant advancement in the treatment of CKD.

• Korean Journal of Radiology
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• v.25 no.6
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• pp.550-558
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• 2024

Hepatocellular carcinoma (HCC) is a biologically heterogeneous tumor characterized by varying degrees of aggressiveness. The current treatment strategy for HCC is predominantly determined by the overall tumor burden, and does not address the diverse prognoses of patients with HCC owing to its heterogeneity. Therefore, the prognostication of HCC using imaging data is crucial for optimizing patient management. Although some radiologic features have been demonstrated to be indicative of the biologic behavior of HCC, traditional radiologic methods for HCC prognostication are based on visually-assessed prognostic findings, and are limited by subjectivity and inter-observer variability. Consequently, artificial intelligence has emerged as a promising method for image-based prognostication of HCC. Unlike traditional radiologic image analysis, artificial intelligence based on radiomics or deep learning utilizes numerous image-derived quantitative features, potentially offering an objective, detailed, and comprehensive analysis of the tumor phenotypes. Artificial intelligence, particularly radiomics has displayed potential in a variety of applications, including the prediction of microvascular invasion, recurrence risk after locoregional treatment, and response to systemic therapy. This review highlights the potential value of artificial intelligence in the prognostication of HCC as well as its limitations and future prospects.