• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.12-14
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• 2022

In this paper, we present a standard conceptual model of livestock epidemic service in the field of smart livestock, which is emerging as an important issue in smart agriculture. By using the network to identify the global livestock epidemic disease risk and provide relevant models to service users, it is expected that it will actually provide economic benefits to livestock owners and ultimately help the national livestock industry economy. In order to apply the standard livestock epidemic service standard model and the livestock infectious disease crisis mitigation standard model sharing method that is presented in conjunction with ICT to the standards in the domestic and international agricultural and livestock industries in the future, continuous research will be carried out.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.139-148
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• 2020

Infectious diseases have long plagued mankind, and predicting and preventing them has been a big challenge for mankind. For this reasen, various studies have been conducted so far to predict infectious diseases. Most of the early studies relied on epidemiological data from the Centers for Disease Control and Prevention (CDC), and the problem was that the data provided by the CDC was updated only once a week, making it difficult to predict the number of real-time disease outbreaks. However, with the emergence of various Internet media due to the recent development of IT technology, studies have been conducted to predict the occurrence of infectious diseases through web data, and most of the studies we have researched have been using single Web data to predict diseases. However, disease forecasting through a single Web data has the disadvantage of having difficulty collecting large amounts of learning data and making accurate predictions through models for recent outbreaks such as "COVID-19". Thus, we would like to demonstrate through experiments that models that use multiple Web data to predict the occurrence of infectious diseases through LSTM models are more accurate than those that use single Web data and suggest models suitable for predicting infectious diseases. In this experiment, we predicted the occurrence of "Malaria" and "Epidemic-parotitis" using a single web data model and the model we propose. A total of 104 weeks of NEWS, SNS, and search query data were collected, of which 75 weeks were used as learning data and 29 weeks were used as verification data. In the experiment we predicted verification data using our proposed model and single web data, Pearson correlation coefficient for the predicted results of our proposed model showed the highest similarity at 0.94, 0.86, and RMSE was also the lowest at 0.19, 0.07.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.97-100
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• 2002

뇌 MR 영상의 분석을 통해 질환을 자동적으로 진단하고 판별을 하기 위한 전처리 과정으로 정상인의 MR 영상 모델과 현재 고려되어지는 대상 영상과의 비교 작업이 요구된다. 이를 통해 보다 정확한 질병에 대한 근거를 제시함으로서 진단이 가능하게 된다. 이러한 비교 작업을 위해 우선적으로 해결해야 하는 것이 현재 대상 영상이 정상인의 MR 영상 시리즈 중 어느 위치의 영상과 일치하는 지를 판별해야 한다. 실질적으로 뇌 MR 시리즈는 영상의 특징에 따라 크게 몇 개의 그룹으로 분류된다. 그루핑 결과 뇌척수액이 존재하는 그룹은 또 다시 4 종류의 세부분류로 나누어지는데, 본 논문에서는 이 뇌 척수액의 모양에 따라 분류하는 알고리즘을 소개한다.

• KOREAN POULTRY JOURNAL
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• v.44 no.8
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• pp.138-140
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• 2012

본고는 서울대학교 한재용 교수 연구팀(공동 연구자 박태섭 박사)이 닭에서 바이러스를 사용하지 않은 유전자 전이방법을 이용한 효율적인 형질전환 닭 생산 기술 확립에 성공하여 미국 학술원 회보(Proceedings of the National Academy of Sciences)에 게재함에 따라 그 성과를 기리고 독자들에게 알리기 위해 한재용 교수에 의뢰해 농가들이 알기쉽게 정리한 내용이다. 형질전환 닭은 인간의 질병 연구 및 새로운 치료제 개발을 위한 다양한 실험 모델 생산에 활용되어 양계산업에 다양하게 사용될 것으로 기대되고 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.1031-1033
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• 2020

최근 지구온난화에 따른 기온 및 강수량 증가 등으로 인해 모기 개체 수가 증가함에 따라 말라리아, 일본뇌염, 뎅기열 등 모기를 통해 전파되는 질병에 감염병의 위험률도 높아지고 있어 머신러닝기반 기후 데이터를 활용하여 모기 개체 수를 예측할 수 있는 모델을 제안하였다.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.339-345
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• 2022

Cardiovascular diseases is one of the leading causes of death in the world. The objectives of this study were to build various models using sociodemographic variables based on three variable selection methods and seven machine learning algorithms for the identification of hypertension and dyslipidemia and to evaluate predictive powers of the models. In experiments based on full variables and correlation-based feature subset selection methods, our results showed that performance of models using naive Bayes was better than those of models using other machine learning algorithms in both two diseases. In wrapper-based feature subset selection method, performance of models using logistic regression was higher than those of models using other algorithms. Our finding may provide basic data for public health and machine learning fields.

• The Journal of the Acoustical Society of Korea
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• v.14 no.5
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• pp.94-101
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• 1995

In this paper, a new vocal cords model, called a four-mass model, is proposed for a hoarse speech mechanism. Pathological changes of vocal cords cause hoarse speech and glottal waveform reflects motion states of vocal cords. From these facts, we assumed that the morbid vocal cords be dissymmetric and take the four-mass type. The glottal waveforms and the model parameters of normal and hoarse speech signals are analyzed, and some relations bet ween the model parameters and the hoarse pathology are discussed. Experimental results show that the new research method of hoarse speech can reveal relations between the acoustic features of hoarse speech and the hoarse pathology, and be used to diagnose laryngeal diseases and to improve tone quality of hoarse speech.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.3
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• pp.626-634
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• 2012

It is required that system can seamlessly identify and manage change history and comprehensive assessment of several types of data as well as individual information of feeding and water environment for scientific and systematic management of fish farming environment and fish farmer. In this study, we implemented the system which can present and simulate current status of water quality and feeding based on th historical data of them, and check changes of state step by step using visual C++. In addition, we proposed the entropy model which can be comprehensive analysis about water quality and feed status information based on knowledge of fisheries. It can be the foundation to create high-level environment model reflecting the more diverse fisheries knowledge such as disease.

• Journal of the Korean Society for information Management
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• v.32 no.2
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• pp.87-103
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• 2015

It is useful to find something valuable for researching through literature based discovery. Swanson's ABC model, known as literature based discovery, suggests the relationship between entities undiscovered yet. This study tries to find the valid relationship between entities by referring to citation which connects articles on similar topic. We collect citation from references in articles, and extract important concepts in titles and abstracts through text mining techniques. We reproduce the relationship between fish oil and Raynaud's disease, which is known as one of Swanson's works, and compare the results with entities identified from traditional approach.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.289-290
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• 2022

Arteriosclerosis is a disease in which the carotid vessel wall becomes thick, and it is important to monitor the thickness of the vessel wall for diagnosis. In this study, we propose a model for extracting 324 radiomics features from carotid MRI images and diagnosing arteriosclerosis using machine learning techniques. We learned a total of four classification models: logistic regression, support vector machine, random forest, and XGBoost through radiomics features. XGBoost model, which showed the highest performance in 5-fold cross-validation, shows the results of accuracy 0.9023, sensitivity 0.9517, specificity 0.8035, AUC 0.8776.