• The Plant Pathology Journal
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• v.36 no.5
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• pp.406-417
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• 2020

Early warning services for crop diseases are valuable when they provide timely forecasts that farmers can utilize to inform their disease management decisions. In South Korea, collaborative disease controls that utilize unmanned aerial vehicles are commonly performed for most rice paddies. However, such controls could benefit from seasonal disease early warnings with a lead time of a few months. As a first step to establish a seasonal disease early warning service using seasonal climate forecasts, we developed the EPIRICE Daily Risk Model for rice blast by extracting and modifying the core infection algorithms of the EPIRICE model. The daily risk scores generated by the EPIRICE Daily Risk Model were successfully converted into a realistic and measurable disease value through statistical analyses with 13 rice blast incidence datasets, and subsequently validated using the data from another rice blast experiment conducted in Icheon, South Korea, from 1974 to 2000. The sensitivity of the model to air temperature, relative humidity, and precipitation input variables was examined, and the relative humidity resulted in the most sensitive response from the model. Overall, our results indicate that the EPIRICE Daily Risk Model can be used to produce potential disease risk predictions for the seasonal disease early warning service.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.352-358
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• 2022

In this paper, we study the relationship between infection rates of covid 19 and the precautionary measures and strict protocols taken by Saudi Arabia to combat the spread of the coronavirus disease and minimize the number of infected people. Based on the infection rates and the timetable of precautionary measures, the best framework of precautionary measures was identified by applying the traveling salesman problem (TSP) that relies on ant colony optimization (ACO) algorithms. The proposed algorithm was applied to daily infected cases data in Saudi Arabia during three periods of precautionary measures: partial curfew, whole curfew, and gatherings penalties. The results showed the partial curfew and the whole curfew for some cities have the minimum total cases over other precautionary measures. The gatherings penalties had no real effect in reducing infected cases as the other two precautionary measures. Therefore, in future similar circumstances, we recommend first applying the partial curfew and the whole curfew for some cities, and not considering the gatherings penalties as an effective precautionary measure. We also recommend re-study the application of the grouping penalty, to identify the reasons behind the lack of its effectiveness in reducing the number of infected cases.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.2
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• pp.117-123
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• 2022

Currently the number of COVID-19 cases is increasing rapidly around the world. One way to restrict the spread of COVID-19 infection is to find confirmed cases using rapid diagnosis. The previously proposed group testing problem assumed without measurement noise, but recently, false positive and false negative cases have occurred during COVID-19 testing. In this paper, we define the noisy group testing problem and analyze how much measurement noise affects the performance. In this paper, we show that the group testing system should be designed to be less susceptible to measurement noise when conducting group testing with a low positive rate of COVID-19 infection. And compared with other developed reconstruction algorithms, our proposed algorithm shows superior performance in noisy group testing.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.50 no.4
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• pp.216-221
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• 2024

Objectives: This study aimed to develop and validate a model to predict the need for intensive care unit (ICU) admission in patients with dental infections using an automated machine learning (ML) program called H2O-AutoML. Materials and Methods: Two models were created using only the information available at the initial examination. Model 1 was parameterized with only clinical symptoms and blood tests, excluding contrast-enhanced multi-detector computed tomography (MDCT) images available at the initial visit, whereas model 2 was created with the addition of the MDCT information to the model 1 parameters. Although model 2 was expected to be superior to model 1, we wanted to independently determine this conclusion. A total of 210 patients who visited the Department of Oral and Maxillofacial Surgery at the Dankook University Dental Hospital from March 2013 to August 2023 was included in this study. The patients' demographic characteristics (sex, age, and place of residence), systemic factors (hypertension, diabetes mellitus [DM], kidney disease, liver disease, heart disease, anticoagulation therapy, and osteoporosis), local factors (smoking status, site of infection, postoperative wound infection, dysphagia, odynophagia, and trismus), and factors known from initial blood tests were obtained from their medical charts and retrospectively reviewed. Results: The generalized linear model algorithm provided the best diagnostic accuracy, with an area under the receiver operating characteristic values of 0.8289 in model 1 and 0.8415 in model 2. In both models, the C-reactive protein level was the most important variable, followed by DM. Conclusion: This study provides unprecedented data on the use of ML for successful prediction of ICU admission based on initial examination results. These findings will considerably contribute to the development of the field of dentistry, especially oral and maxillofacial surgery.

• Archives of Plastic Surgery
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• v.36 no.4
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• pp.397-405
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• 2009

Purpose: Soft tissue defect of anterior chest wall is caused by trauma, infection, tumors and irradiation. To reconstruct damaged anterior chest wall does require to consider the patient's body condition, the cause, the location, the depth and the size of deletion, the circulation of surrounding tissue and minimization of functional and cosmetic disability. In this report, we suggest the algorithm of configuration for reconstruction methods. Methods: A retrospective study of 20 patients who underwent anterior chest wall reconstruction with pedicled musculocutaneous flap and fasciocutaneous flap was conducted. We collected the information of the patient's body condition, the cause, the size, the depth and the location of deletion, implemented flap and complication. We observed and evaluated flap compatibility, functional and cosmetic results. Patients completed survey about the extent to their satisfaction. Result: Follow up period after surgery was from 6 to 26 months, survival of flap were confirmed in all of patients' case. Two cases of local necrosis, one case of wound disruption were reported, but all these were cured by the debridement and primary closure. One hematoma and one seroma formation were observed in donor site. Longer surgery time, more bleeding amount and more transfusion volume were reported in the group of musculocutenous flap. Conclusion: Long term follow up result showed the successful reconstruction in all patients without recurrence and with minimal donor site morbidity. In addition, the patients' satisfaction for cosmetic and functional results were scaled relatively higher. This confirmed the importance of reconstruction algorithm for the chest wall reconstruction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.127-132
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• 2019

Influenza is an infectious disease caused by an influenza virus with symptoms of high fever and headache. Since influenza especially mutates into multiple subtypes in the carrier's body, it is a serious threat for mankind such as Spanish influenza. The treatment of influenza infection mandates the use of antiviral drugs through rapid diagnostic test. Generally, immunochromatography-based rapid influenza diagnostic tests are used for rapid diagnosis in an emergency. In this paper, we propose an influenza analysis algorithm based on image processing to examine a large number of patients suspected of being infected with influenza. Also, we propose a robust influenza analysis algorithm based on the joint cumulative mass function under varying radiometric conditions such as illuminant and exposure differences. Simulation results show that the proposed algorithm significantly reduces the error of influenza diagnosis under different radiometric conditions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.193-198
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• 2022

Recently, place that you need to check wearing mask and body temperature to prevent the proliferation of COVID-19 increased. But these things often measured by man manually or by machine one by one, result may be different by measuring ways, so it wastes workforce. Also, the machine generally just measures the highest temperature of the face, criteria for fever can't be trusted too. A bottleneck may occur due to crowding of people at the entrance, and because most of the measurement sites are at one entrance, it is inconvenient to track the movement of COVID-19 Confirmed cases. Thus, in this study, we intend to propose a method for suppressing the spread of infection by automatically classifying and displaying in real time using camera, thermal camera, Haar Cascade, and result selection algorithm.

• Korean Journal of Poultry Science
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• v.50 no.1
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• pp.41-50
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• 2023

Over the last decade, avian influenza (AI) has been considered an emerging disease that would become the next pandemic, particularly in countries like South Korea, with continuous animal outbreaks. In this situation, risk assessment is highly needed to prevent and prepare for human infection with AI. Thus, we developed the risk assessment matrix for a high-risk area of human infection with AI in South Korea based on the notion that risk is the multiplication of hazards with vulnerability. This matrix consisted of highly pathogenic avian influenza (HPAI) in poultry farms and the number of poultry-associated production facilities assumed as hazards of avian influenza and vulnerability, respectively. The average number of HPAI in poultry farms at the 229-municipal level as the hazard axis of the matrix was predicted using a negative binomial regression with nationwide outbreaks data from 2003 to 2018. The two components of the matrix were classified into five groups using the K-means clustering algorithm and multiplied, consequently producing the area-specific risk level of human infection. As a result, Naju-si, Jeongeup-si, and Namwon-si were categorized as high-risk areas for human infection with AI. These findings would contribute to designing the policies for human infection to minimize socio-economic damages.

• Journal of Internet Computing and Services
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• v.13 no.4
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• pp.45-52
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• 2012

T cells induce immune responses and thereby eliminate infected micro-organisms when peptides from the microbial proteins are bound to HLAs in the host cell surfaces, It is known that the more stable the binding of peptide to HLA is, the stronger the T cell response gets to remove more effectively the source of infection. Accordingly, if peptides (HLA binder) which can be bound stably to a certain HLA are found, those peptieds are utilized to the development of peptide vaccine to prevent infectious diseases or even to cancer. However, HLA is highly polymorphic so that HLA has a large number of alleles with some frequencies even in one population. Therefore, it is very inefficient to find the peptides stably bound to a number of HLAs by testing random possible peptides for all the various alleles frequent in the population. In order to solve this problem, computational methods have recently been developed to predict peptides which are stably bound to a certain HLA. These methods could markedly decrease the number of candidate peptides to be examined by biological experiments. Accordingly, this paper not only introduces a method of machine learning to predict peptides binding to an HLA, but also suggests a new prediction model so called 'knowledge-based genetic algorithm' that has never been tried for HLA binding peptide prediction. Although based on genetic algorithm (GA). it showed more enhanced performance than GA by incorporating expert knowledge in the process of the algorithm. Furthermore, it could extract rules predicting the binding peptide of the HLA alleles common in Koreans.

• Convergence Security Journal
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• v.22 no.2
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• pp.21-26
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• 2022

Wearing face masks is an effective measure to prevent COVID-19 infection. Infrared thermal image based temperature measurement and identity recognition system has been widely used in many large enterprises and universities in China, so it is totally necessary to research the face mask detection of thermal infrared imaging. Recently introduced MTCNN (Multi-task Cascaded Convolutional Networks)presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask MTCNN is an algorithm that extends MTCNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. It is easy to generalize the R-CNN to other tasks. In this paper, we proposed an infrared image detection algorithm based on R-CNN and detect heating elements which can not be distinguished by RGB images.