• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1898-1908
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• 2017

Accurately Diagnosing and managing disease in livestock can help sustainable livestock productivity and maintain human health. Maintaining the health of livestock is an important part of human health. The prediction of calf diseases is carried out by pre-processing the calf biometric data. calf information is used as information for calf birth history, calf biometric information, environmental information on housing, and disease management. It can be developed as an ontology and used as a knowledge base. The Bayesian network was used and inferred in the process of analyzing the correlations of calf diseases. Prediction of diseases based on knowledge of calf disease on calf diseases name, causes, occur timing, care and symptoms, etc., will be able to respond to accurate disease treatment and prevent other livestock from being infected in advance.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.335-343
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• 2019

Healthy living is an essential element of human happiness. Quality eating provides the basis for life, and the health of livestock, which provides meat and dairy products, has a direct impact on human health. In the case of calves, diarrhea is the cause of all diseases.In this paper, we use a sensor to measure calf 's biometric data to diagnose calf diarrhea. The collected biometric data is subjected to a preprocessing process for use as meaningful information. We measure calf birth history and calf biometrics. The ontology is constructed by inputting environmental information of housing and biochemistry, immunity, and measurement information of human body for disease management. We will build a knowledge base for predicting calf diarrhea by predicting calf diarrhea through logical reasoning. Predict diarrhea with the knowledge base on the name of the disease, cause, timing and symptoms of livestock diseases. These knowledge bases can be expressed as domain ontologies for parent ontology and prediction, and as a result, treatment and prevention methods can be suggested.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.78-95
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• 2022

The result of previous studies and epidemiological invstigations for infectious diseases epidemic in livestock have shown that trips made by livestock-related vehicles are the main cause of the spread of these epidemics. In this study, the OD traffic volume of livestock freight vehicle during the week in each zone was calculated using livestock facility visit history data and digital tachograph data. Based on this, a model for predicting the spread of infectious diseases in livestock was developed. This model was trained using zonal records of foot-and-mouth disease in Gyeonggi-do for one week in January and February 2015 and in positive, it was succesful in predicting the outcome in all out of a total 13 actual infected samples for test.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.31-31
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• 2017

On farm evaluation for the control of airborne diseases like FMD and flu virus has been done in past but control of disease in process of transportation of livestock and manures is still needed. The objective of this study was to predict a common pattern of livestock vehicles movement. The analysis were done on GPS data, collected from drivers of livestock vehicles in Jeju Island, South Korea in year 2012 and 2013. The GPS data include the coordinates of moving vehicles according to time and dates, livestock farms and manure keeping sites. 2012 year data was added to ArcGIS and different tools were used for predicting common vehicle moving pattern. The common pattern of year 2012 were determined and considered as predicted common pattern for year 2013. To compare with actual pattern of year 2013 the same analysis was done to find the difference in 2012 and 2013 pattern. When the manure keeping sites and livestock farms were same in both years, as a result common pattern of 2012 and 2013 were similar but difference were found in patterns when the manure keeping sites and livestock farms were changed. In future for more accurate results and to predict the accurate pattern of vehicles movement, more dependent and independent variables will be required to make a suitable model for prediction.

• Journal of the Korea Society of Computer and Information
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• v.21 no.10
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• pp.91-98
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• 2016

Outbreaks of highly contagious livestock diseases can cause direct and indirect economic impacts such as lower productivity of cattle farms, fall in tourism in damaged areas and countries, and decline in exports. They also incur tremendous social costs associated with disease elimination and restoration work. Thus, it is essential to prevent livestock diseases through monitoring and prediction efforts. Currently, however, it is still difficult to provide accurate predictive information regarding occurrences of livestock diseases, because existing cattle health monitoring or forecasting systems are only limited to monitor environmental conditions of livestock barns and check activities of cattle by using a pedometer or thermal image. In this paper, we present a biosensor-based cattle health monitoring system capable of collecting bio-signals of farm animals in an effective way. For the presented monitoring system, we design an integrated monitoring device consisting of a sensing module to measure bio-signals of cattle such as the heartbeat, the breath rate and the momentum, as well as a Zigbee module designed to transmit the biometric data based on Wireless Sensor Network (WSN). We verify the validity of the monitoring system by the comparison of the correlations of designed device with a commercial ECG equipment through analyzing the R-peak of measured signals.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.10
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• pp.1411-1420
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• 2003

Comparative maps, representing chromosomal locations of homologous genes in different species, are useful sources of information for identifying candidate disease genes and genes determining complex traits. They facilitate gene mapping and linkage prediction in other species, and provide information on genome organization and evolution. Here, the current gene mapping and comparative mapping status of the major livestock species are presented. Two techniques were widely used in comparative mapping: FISH (Fluorescence In Situ Hybridization) and PCR-based mapping using somatic cell hybrid (SCH) or radiation hybrid (RH) panels. New techniques, using, for example, ESTs (Expressed Sequence Tags) or CASTS (Comparatively Anchored Sequence Tagged Sites), also have been developed as useful tools for analyzing comparative genome organization in livestock species, further enabling accurate transfer of valuable information from one species to another.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.292-301
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• 2016

Because damages arising from the occurrence of foot-and-mouth disease (FMD) are very great, it is essential to make a preemptive diagnosis to cope with it in order to minimize those damages. The main symptoms of foot-and-mouth disease are body temperature increase, loss of appetite, formation of blisters in the mouth, on hooves and breasts, etc. in a cow or a bull, among which the body temperature check is the easiest and quickest way to detect the disease. In this paper, an algorithm to detect FMD from the hooves of cattle was developed and implemented for preemptive coping with foot-and-mouth disease, and a hoof check test is conducted after the installation of a high-resolution camera module, a thermo-graphic camera, and a temperature/humidity module in the cattle shed. Through the algorithm and system developed in this study, it is possible to cope with an early-stage situation in which cattle are suspected as suffering from foot-and-mouth disease, creating an optimized growth environment for cattle. In particular, in this study, the system to cope with FMD does not use a portable thermo-graphic camera, but a fixed camera attached to the cattle shed. It does not need additional personnel, has a function to measure the temperature of cattle hooves automatically through an image algorithm, and includes an automated alarm for a smart phone. This system enables the prediction of a possible occurrence of foot-and-mouth disease on a real-time basis, and also enables initial-stage disinfection to be performed to cope with the disease without needing extra personnel.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.2
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• pp.60-74
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• 2017

To facilitate prevention of highly pathogenic avian influenza (HPAI), a GIS is widely used for monitoring, investigating epidemics, managing HPAI-infected farms, and eradicating the disease. After the outbreak of foot-and-mouth disease in 2010 and 2011, the government of the Republic of Korea (ROK) established the GIS-based Korean Animal Health Integrated System (KAHIS) to avert livestock epidemics, including HPAI. However, the KAHIS is not sufficient for controlling HPAI outbreaks due to lack of responsibility in fieldwork, such as sterilization of HPAI-infected poultry farms and regions, control of infected animal movement, and implementation of an eradication strategy. An outbreak prediction model to support efficient HPAI control in the ROK is proposed here, constructed via analysis of HPAI outbreak patterns in the ROK. The results show that 82% of HPAI outbreaks occurred in Jeolla and Chungcheong Provinces. The density of poultry farms in these regions were $2.2{\pm}1.1/km^2$ and $4.2{\pm}5.6/km^2$, respectively. In addition, reared animal numbers ranged between 6,537 and 24,250 individuals in poultry farms located in HPAI outbreak regions. Following identification of poultry farms in HPAI outbreak regions, an HPAI outbreak prediction model was designed using factors such as the habitat range for migratory birds(HMB), freshwater system characteristics, and local road networks. Using these factors, poultry farms which reared 6,500-25,000 individuals were filtered and compared with number of farms actually affected by HPAI outbreaks in the ROK. The HPAI prediction model shows that 90.0% of the number of poultry farms and 54.8% of the locations of poultry farms overlapped between an actual HPAI outbreak poultry farms reported in 2014 and poultry farms estimated by HPAI outbreak prediction model in the present study. These results clearly show that the HPAI outbreak prediction model is applicable for estimating HPAI outbreak regions in ROK.

• Journal of Animal Science and Technology
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• v.63 no.4
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• pp.854-863
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• 2021

Weaning induces physiological changes in intestinal development that affect pigs' growth performance and susceptibility to disease. As a posttranscriptional regulator, microRNAs (miRNAs) regulate cellular homeostasis during intestinal development. We performed small RNA expression profiling in the small intestine of piglets before weaning (BW), 1 week after weaning (1W), and 2 weeks after weaning (2W) to identify weaning-associated differentially expressed miRNAs. We identified 38 differentially expressed miRNAs with varying expression levels among BW, 1W, and 2W. Then, we classified expression patterns of the identified miRNAs into four types. ssc-miR-196a and ssc-miR-451 represent pattern 1, which had an increased expression at 1W and a decreased expression at 2W. ssc-miR-499-5p represents pattern 2, which had an increased expression at 1W and a stable expression at 2W. ssc-miR-7135-3p and ssc-miR-144 represent pattern 3, which had a stable expression at 1W and a decreased expression at 2W. Eleven miRNAs (ssc-miR-542-3p, ssc-miR-214, ssc-miR-758, ssc-miR-4331, ssc-miR-105-1, ssc-miR-1285, ssc-miR-10a-5p, ssc-miR-4332, ssc-miR-503, ssc-miR-6782-3p, and ssc-miR-424-5p) represent pattern 4, which had a decreased expression at 1W and a stable expression at 2W. Moreover, we identified 133 candidate targets for miR-196a using a target prediction database. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that the target genes were associated with 19 biological processes, 4 cellular components, 8 molecular functions, and 7 KEGG pathways, including anterior/posterior pattern specification as well as the cancer, PI3K-Akt, MAPK, GnRH, and neurotrophin signaling pathways. These findings suggest that miRNAs regulate the development of the small intestine during the weaning process in piglets by anterior/posterior pattern specification as well as the cancer, PI3K-Akt, MAPK, GnRH, and neurotrophin signaling pathways.