• Journal of Industrial Convergence
• /
• v.21 no.2
• /
• pp.111-116
• /
• 2023

Recently, viral infectious diseases and new diseases are not limited to one region, but are spreading worldwide, causing serious economic and social damage. In addition, the development cycle of new diseases is shortening, and the rate of spread is accelerating. In order to prevent the spread of disease, passive forms of response after a disease outbreak, such as personal and regional quarantine and border closure, are prioritized. This type of response has many shortcomings as a fundamental response to preventing the spread of disease. Therefore, this study proposes a disease prevention monitoring system including new disease occurrence information. In this study, disease information and user information are collected through the establishment of the IoMT environment. Information collection using an agent collects and classifies data registered in the disease information server. In the IoMT environment, user data is collected, and whether the user is infected with a disease is evaluated and provided to the user. Through this study, individual disease symptom information can be provided and active countermeasures against the spread of disease can be provided.

• The Journal of the Korea Contents Association
• /
• v.21 no.12
• /
• pp.322-330
• /
• 2021

In modern aquaculture, mass mortality is a very important issue that determines the success of aquaculture business. If a fish disease is not detected at an early stage in the farm, the disease spreads quickly because the farm is a closed environment. Therefore, early detection of diseases is crucial to prevent mass mortality of fish raised in farms. Recently deep learning-based automatic identification of fish diseases has been widely used, but there are many difficulties in identifying objects due to insufficient images of fish diseases. Therefore, this paper suggests a method to generate a large number of fish disease images by synthesizing normal images and disease images using SinGAN deep learning model in order to to solve the lack of fish disease images. We generate images from the three most frequently occurring Paralichthys Olivaceus diseases such as Scuticociliatida, Vibriosis, and Lymphocytosis and compare them with the original image. In this study, a total of 330 sheets of scutica disease, 110 sheets of vibrioemia, and 110 sheets of limphosis were made by synthesizing 10 disease patterns with 11 normal halibut images, and 1,320 images were produced by quadrupling the images.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37C no.12
• /
• pp.1263-1270
• /
• 2012

Livestock disease that decreases the farm productivity and income leads to not only financial loss but also national loss from the spread of contagious disease. The purpose of this paper is to propose a livestock disease forecasting system that can diagnose disease of livestock at an early stage based on the livestock activity and body temperature. The proposed livestock disease forecasting system collect data on livestock activity and body temperature using a acceleration sensor and thermal imaging camera and comparing the data with control according to disease. It is expected that, this system can be accurately identify and prevent spread of livestock disease beforehand to minimize damages caused by disease to improve the productivity and the rate of return of livestock farms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.10a
• /
• pp.965-967
• /
• 2013

The paper proposed the DRCS(Disease Rule Creation Scheme) which generates the disease rules for efficient disease management in Healthcare system. The DRCS uses basically Rough Set Theory and computes support between each attributes and decision attributes. It creates the disease rules that judges disease after it removes the attribute which is the lowest support. Therefore, it reduces the number of disease rules and improves the exactness, compared with C4.5 algorithm.

• The Journal of the Korea Contents Association
• /
• v.9 no.4
• /
• pp.114-120
• /
• 2009

In this study, we employed the idea that disease-related proteins tend to be work as an important factor for architecture of the disease network. We initially obtained 43 atopy-related proteins from the Online Mendelian Inheritance in Man (OMIM) and then constructed atopy-related protein interaction network. The protein network can be derived the map of the relationship between different disease proteins, denoted disease interaction network. We demonstrate that the associations between diseases are directly correlated to their underlying protein-protein interaction networks. From constructed the disease-protein bipartite network, we derived three diseasomal proteins, CCR5, CCL11, and IL/4R. Although we use the relatively small subnetwork, an atopy-related disease network, it is sufficient that the discovery of protein interaction networks assigned by diseases will provide insight into the underlying molecular mechanisms and biological processes in complex human disease system.

• Korean Journal of Veterinary Research
• /
• v.62 no.1
• /
• pp.4.1-4.5
• /
• 2022

This paper describes a fatal case of pneumonia in a 14-day-old dog caused by extraintestinal pathogenic Escherichia coli (ExPEC). The necropsy showed that almost all of left lobes of the lungs had dark-red consolidation. A histopathology examination revealed moderate acute fibrino-hemorrhagic necrotizing pneumonia with intralesional bacterial colonies. Non-suppurative epicarditis, congestion in the liver, and necrosis in the white pulp of the spleen also were found. E. coli with cytotoxic necrotizing factor 1 and α-hemolysin was isolated from the lung. This case was confirmed to have fatal pneumonia caused by ExPEC that led to a systemic infection.

• Journal of the Korea Society for Simulation
• /
• v.29 no.4
• /
• pp.1-7
• /
• 2020

Early detection and classification of crop diseases play significant role to help farmers to reduce disease spread and to increase agricultural productivity. Recently, many researchers have used deep learning techniques like convolutional neural network (CNN) classifier for crop disease inspection with dataset of crop leaf images (e.g., PlantVillage dataset). These researches present over 90% of classification accuracy for crop diseases, but they have ability to detect only the pre-trained diseases. This paper proposes an efficient disease inspection CNN model for new crops not used in the pre-trained model. First, we present a benchmark crop disease classifier (CDC) for the crops in PlantVillage dataset using VGG16. Then we build a modified crop disease classifier (mCDC) to inspect diseases for untrained crops. The performance evaluation results show that the proposed model outperforms the benchmark classifier.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.2
• /
• pp.39-44
• /
• 2021

In this paper, we propose a method of disease diagnosis system that can diagnose the health status of household pets for the people who lack veterinary knowledge. The proposed diagnosis system holds 50 different kinds of diseases with the symptoms for each of them as a database to provide results from symptom input. Each disease database has its own symptom codes for a disease, and by using the disease database, FCM clustering technique is applied to disease which outputs membership degree to determine diseases close to the input symptom as a pet diagnosis result. The implementation results of the proposed pet diagnosis system were obtained by the number of selected symptoms and the possibility values of the diseases that have the selected symptoms being sorted in descending order to derive top 3 diseases closest to the pet's symptom.

• Journal of Convergence Society for SMB
• /
• v.6 no.1
• /
• pp.33-38
• /
• 2016

Disease of patient who visited the hospital can cause different symptoms of the disease, depending on the environment and lifestyle. Recent medical services offered in patients has changed in the environment that can be selected for treatment by analyzing the patient according to the disease symptoms. In this paper, we propose an efficient method to manage disease control because the treatment method may change at any patients suffering from the disease according to the patient conditions by grouping the different treatments to patients for disease information. The proposed scheme has a feature that can be ingested by the patient big disease information, as well as to improve the treatment efficiency of the medical treatment the increase patient satisfaction. The proposed sheme can handle big data by clustering of disease information for patients suffering from diseases such as patient consent small groups. In addition, the proposed scheme has the advantage that can be conveniently accessed via a particular keyword, the treatment method according to patient disease information. The experimental results, the proposed method has been improved by 23% in terms of efficiency compared to conventional techniques, disease management time is gained 11.3% improved results. Medical service user satisfaction seen from the survey is to obtain a high 31.5% results.

• Proceedings of the Korea Contents Association Conference
• /
• 2009.05a
• /
• pp.306-311
• /
• 2009

We initially obtained human diseases-related proteins dataset from the OMIM and the SWISS PROT and then constructed disease-related protein-protein interaction network. The protein network contains 40 hub proteins such as CALM1, ACTB and ABL2. The protein network can be derived the map of the relationship between different disease proteins, denoted disease interaction network. We demonstrate that the associations between diseases are directly correlated to their underlying protein-protein interaction networks. From constructed the disease-protein bipartite network, we derived 38 diseasomal proteins, including APP, ABL1 and STAT1. We previously demonstrated that hub proteins in the network tend to be diseasomal proteins in the disease-related protein sub-networks. However, we found that 18% hubs are only diseasomal proteins in the whole disease network. At this point, we could not elucidate difference in the hub-diseasomal proteins tendency between sub0network and whole network. In spite of we still have unsolved problems, our results elucidate that the discovery of protein interaction networks assigned by diseases will provide insight into the underlying molecular mechanisms and biological processes in complex human disease system.