• Journal of Korea Multimedia Society
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• v.23 no.10
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• pp.1250-1257
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• 2020

The early detection of diseases is important in agriculture because diseases are major threats of reducing crop yield for farmers. The shape and color of plant leaf are changed differently according to the disease. So we can detect and estimate the disease by inspecting the visual feature in leaf. This study presents a vision-based leaf classification method for detecting the diseases of tomato crop. ResNet-50 model was used to extract the visual feature in leaf and classify the disease of tomato crop, since the model showed the higher accuracy than the other ResNet models with different depths. We propose a new ensemble approach using several DCNN classifiers that have the same structure but have been trained at different ranges in the DCNN layers. Experimental result achieved accuracy of 97.19% for PlantVillage dataset. It validates that the proposed method effectively classify the disease of tomato crop.

• Journal of the Korean Mathematical Society
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• v.58 no.1
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• pp.45-67
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• 2021

In this paper, a mathematical dynamical system involving both deterministic (with or without delay) and stochastic "SIR" epidemic model with nonlinear incidence rate in a continuous reactor is considered. A profound qualitative analysis is given. It is proved that, for both deterministic models, if ��d > 1, then the endemic equilibrium is globally asymptotically stable. However, if ��d ≤ 1, then the disease-free equilibrium is globally asymptotically stable. Concerning the stochastic model, the Feller's test combined with the canonical probability method were used in order to conclude on the long-time dynamics of the stochastic model. The results improve and extend the results obtained for the deterministic model in its both forms. It is proved that if ��s > 1, the disease is stochastically permanent with full probability. However, if ��s ≤ 1, then the disease dies out with full probability. Finally, some numerical tests are done in order to validate the obtained results.

• The Pure and Applied Mathematics
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• v.31 no.2
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• pp.131-158
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• 2024

This study investigates the impact of precautionary measures, such as isolating exposed individuals, wearing masks, and maintaining physical distance, on preventing infectious disease. A deterministic SEIQHRV epidemic model is employed for this purpose. The model's positivity, boundedness, disease-free, and endemic equilibrium points are identified. A sensitivity test assesses the impact of preventive measures on infected classes. Results show that a basic reproduction number less than unity drives disease eradiction, while a higher unity value encourages the adoption of preventive measures.

• 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 Oriental Neuropsychiatry
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• v.30 no.4
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• pp.327-340
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• 2019

Objectives: Alzheimer's disease (AD) is a complex disease accompanied by slow impairment of memory and coordination leading to behavioral changes. To date, the only treatment option is to delay the progress of the disease. The purpose of this study was to investigate the synergistic effects of combination treatment with donepezil and three herbal extracts SIP-3 in the AD mouse model induced by amyloid-β (Aβ). Methods: We tested SIP-3 extracts for the cytotoxicity on Aβ-treated SH-SY5Y cells. Then the synergistic effects of SIP-3 and donepezil were evaluated in the AD mouse model using animal experiments and the next generation sequencing (NGS) study. Results: We found that co-treatment with SIP-3 extracts and donepezil increased the viability in Aβ-treated SH-SY5Y cells. The beneficial effects of the co-treatment were also observed in the Aβ-induced AD mouse model. The NGS study was performed to show that the co-treatment of SIP-3 and donepezil restored the disease phenotype closely to the normal level in the AD mouse model in terms of mRNA expression. However, the phenotypes were only partially restored. Conclusions: This study suggests that the combination treatment has a potential to be used for the treatment of AD. However, longer periods of treatment may be required.

• 대한예방의학회:학술대회논문집
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• 1999.10a
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• pp.198-199
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• 1999

• Journal of The Association for Neo Medicine
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• v.1 no.1
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• pp.57-89
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• 1996

A Typification of diagnosis and treatment(DT) model must be done in order to generalize the objective stage to the result of treatment to internal disease in connection with the type of viscera and bowel symptom. We could find 108 DT models in internal disease from the combination of 18 types of viscera and bowel and 6 types of DT treatment processes. Thus, the typification of 108 models of DT can be viewed as a modeling processes of utilizing DT knowledge at each stage. We argue that objectivity in diagnosis and treatment of internal disease can be obtained practically from typification of DT model.

• The Korean Journal of Food And Nutrition
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• v.36 no.3
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• pp.172-184
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• 2023

The objective of this study was to determine the efficacy of a natural product of cherry tree (Prunus serrulata var. spontanea: PS) as a test substance for improving cytokine and ovalbumin-specific IgE using an ovalbumin-induced asthma disease model of 5-week-old male BALB/c mice. Lung tissue pathology was analyzed to confirm anti-inflammatory and asthmatic effects. As a result of examining the effect on changes in inflammatory cells in bronchoalveolar lavage fluid in an ovalbumin-induced asthma disease model by administering the PS sample, total cells, eosinophil, neutrophil, lymphocyte, and monocytes were significantly decreased. Concentrations of cytokine-based TNF-alpha and IL-4 and immunoglobulin E in serum were significantly increased in the asthma-inducing negative control group than in the normal group. However, high concentrations of PS decreased them. In histopathological examination of the lung tissue, it was confirmed that inflammatory cells infiltrated around the alveoli and bronchioles were increased in ovalbumin-induced asthma disease model. After administration of cherry tree extract, bronchiolar morphological changes such as mucosal thickening were slightly improved. From the above results, it was confirmed that extract of cherry tree significantly reduced inflammation expression and tissue damage in alveolar tissues. It was also confirmed that the cherry tree extract had an excellent efficacy in improving asthma inflammation.

• Smart Media Journal
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• v.13 no.8
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• pp.16-25
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• 2024

Recently, there has been a growing emphasis on identifying both known and unknown diseases in plant disease recognition. In this task, a model trained only on images of known classes is required to classify an input image into either one of the known classes or into an unknown class. Consequently, the capability to recognize unknown diseases is critical for model deployment. To enhance this capability, we are considering three factors. Firstly, we propose a new logits-based scoring function for unknown scores. Secondly, initial experiments indicate that a compact feature space is crucial for the effectiveness of logits-based methods, leading us to employ the AM-Softmax loss instead of Cross-entropy loss during training. Thirdly, drawing inspiration from the efficacy of transfer learning, we utilize a large plant-relevant dataset, PlantCLEF2022, for pre-training a model. The experimental results suggest that our method outperforms current algorithms. Specifically, our method achieved a performance of 97.90 CSA, 91.77 AUROC, and 90.63 OSCR with the ResNet50 model and a performance of 98.28 CSA, 92.05 AUROC, and 91.12 OSCR with the ConvNext base model. We believe that our study will contribute to the community.

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