• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3583-3597
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• 2020

In this paper, a parallel deep learning model using a convolutional neural network and a dilated convolutional neural network is proposed to classify Alzheimer's disease with high accuracy in PET/CT images. The developed model consists of two pipelines, a conventional CNN pipeline, and a dilated convolution pipeline. An input image is sent through both pipelines, and at the end of both pipelines, extracted features are concatenated and used for classifying Alzheimer's disease. Complimentary abilities of both networks provide better overall accuracy than single conventional CNNs in the dataset. Moreover, instead of performing binary classification, the proposed model performs three-class classification being Alzheimer's disease, mild cognitive impairment, and normal control. Using the data received from Dong-a University, the model performs classification detecting Alzheimer's disease with an accuracy of up to 95.51%.

• The Plant Pathology Journal
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• v.32 no.6
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• pp.537-544
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• 2016

We developed a model, termed D-PSA-K, to estimate the accumulated potential damage on kiwifruit canes caused by bacterial canker during the growing and overwintering seasons. The model consisted of three parts including estimation of the amount of necrotic lesion in a non-frozen environment, the rate of necrosis increase in a freezing environment during the overwintering season, and the amount of necrotic lesion on kiwifruit canes caused by bacterial canker during the overwintering and growing seasons. We evaluated the model's accuracy by comparing the observed maximum disease incidence on kiwifruit canes against the damage estimated using weather and disease data collected at Wando during 1994-1997 and at Seogwipo during 2014-2015. For the Hayward cultivar, D-PSA-K estimated the accumulated damage as approximately nine times the observed maximum disease incidence. For the Hort16A cultivar, the accumulated damage estimated by D-PSA-K was high when the observed disease incidence was high. D-PSA-K could assist kiwifruit growers in selecting optimal sites for kiwifruit cultivation and establishing improved production plans by predicting the loss in kiwifruit production due to bacterial canker, using past weather or future climate change data.

• BMB Reports
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• v.52 no.4
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• pp.250-258
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• 2019

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by selective and progressive loss of dopaminergic neurons. Genetic and environmental risk factors are associated with this disease. The genetic factors are composed of approximately 20 genes, such as SNCA, parkin, PTEN-induced kinase1 (pink1), leucine-rich repeat kinase 2 (LRRK2), ATP13A2, MAPT, VPS35, and DJ-1, whereas the environmental factors consist of oxidative stress-induced toxins such as 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP), rotenone, and paraquat. The analyses of their functions and mechanisms have provided important insights into the disease process, which has demonstrated that these factors cause oxidative damage and mitochondrial dysfunction. The most invaluable studies have been performed using disease model organisms, such as mice, fruit flies, and worms. Among them, Drosophila melanogaster has emerged as an excellent model organism to study both environmental and genetic factors and provide insights to the pathways relevant for PD pathogenesis, facilitating development of therapeutic strategies. In this review, we have focused on the fly model organism to summarize recent progress, including pathogenesis, neuroprotective compounds, and newer approaches.

• The Korean Journal of Applied Statistics
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• v.31 no.3
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• pp.329-342
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• 2018

Nomogram is a statistical tool that visualizes the risk factors of the disease and then helps to understand the untrained people. This study used risk factors of chronic obstructive pulmonary disease (COPD) and compared with logistic regression model and naïve Bayesian classifier model. Data were analyzed using the Korean National Health and Nutrition Examination Survey 6th (2013-2015). First, we used 6 risk factors about COPD. We constructed nomogram using logistic regression model and naïve Bayesian classifier model. We also compared the nomograms constructed using the two methods to find out which method is more appropriate. The receiver operating characteristic curve and the calibration plot were used to verify each nomograms.

• The Plant Pathology Journal
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• v.28 no.2
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• pp.172-184
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• 2012

An infection risk model for Phytophthora blight on chili pepper was developed to estimate the first date of disease occurrence in the field. The model consisted of three parts including estimation of zoosporangium formation, soil water content, and amount of active inoculum in soil. Daily weather data on air temperature, relative humidity and rainfall, and the soil texture data of local areas were used to estimate infection risk level that was quantified as the accumulated amount of active inoculum during the prior three days. Based on the analysis on 190 sets of weather and disease data, it was found that the threshold infection risk of 224 could be an appropriate criterion for determining the primary infection date. The 95% confidence interval for the difference between the estimated date of primary infection and the observed date of first disease occurrence was $8{\pm}3$ days. In the model validation tests, the observed dates of first disease occurrence were within the 95% confidence intervals of the estimated dates in the five out of six cases. The sensitivity analyses suggested that the model was more responsive to temperature and soil texture than relative humidity, rainfall, and transplanting date. The infection risk model could be implemented in practice to control Phytophthora blight in chili pepper fields.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.301-312
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• 2015

The purpose of this paper is to establish the epidemic model to explain the process of disease spread. The process of disease spread can be classified into two types: deterministic process and stochastic process. Most studies supposed that the process follows the deterministic process and established the model using the ordinary differential equation. In this article, we try to build the disease spread prediction model based on the SIR (Suspectible - Infectious - Recovered) model. we first estimated the model parameters using least squared method and applied to a deterministic model using ordinary differential equation. we also applied to a stochastic model based on Gillespie algorithm. The methods introduced in this paper are applied to the data on the number of cases of malaria every week from January 2001 to March 2003, released by Korea Centers for Disease Control and Prevention. As a result, we conclude that our model explains well the process of disease spread.

• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.50-57
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• 2020

The work that detection and elimination to disease and pest have important in agricultural field because it is directly related to the production of the crops, early detection and treatment of the disease insects. Image classification technology based on traditional computer vision have not been applied in part such as disease and pest because that is falling a accuracy to extraction and classification of feature. In this paper, we proposed model that determine to disease and pest of shiitake based on deep-CNN which have high image recognition performance than exist study. For performance evaluation, we compare evaluation with Alexnet to a proposed deep learning evaluation model. We were compared a proposed model with test data and extend test data. The result, we were confirmed that the proposed model had high performance than Alexnet which approximately 48% and 72% such as test data, approximately 62% and 81% such as extend test data.

• Journal of the Korean Society of Safety
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• v.37 no.2
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• pp.88-96
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• 2022

Widespread infectious diseases are a concern for workers working in confined spaces. However, there is no risk assessment index for the risk of infectious disease in the workplace. Therefore, we propose a simple but effective index model to assess the risk of infectious diseases in the workplace. The proposed model identifies the risk of each workplace through an evaluation sheet comprising the frequency and intensity of the infectious disease. The intensity of an infectious disease is generally governed by the density of workers, whereas frequency is the sum of physical-e nvironmental and human management factors. By multiplying intensity and frequency, the risk of the workplace is derived. Through the proposed model, we evaluate the risks of workers at 15 different work sites. The proposed model clearly reveals what should be improved to keep workers safe from infectious diseases and will be helpful in assessing the risk of contagious disease at the work place.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.504-519
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• 2023

Heart disease is becoming the top reason of death all around the world. Diagnosing cardiac illness is a difficult endeavor that necessitates both expertise and extensive knowledge. Machine learning (ML) is becoming gradually more important in the medical field. Most of the works have concentrated on the prediction of cardiac disease, however the precision of the results is minimal, and data integrity is uncertain. To solve these difficulties, this research creates an Integrated Accurate-Secure Heart Disease Prediction (IAS) Model based on Deep Convolutional Neural Networks. Heart-related medical data is collected and pre-processed. Secondly, feature extraction is processed with two factors, from signals and acquired data, which are further trained for classification. The Deep Convolutional Neural Networks (DCNN) is used to categorize received sensor data as normal or abnormal. Furthermore, the results are safeguarded by implementing an integrity validation mechanism based on the hash algorithm. The system's performance is evaluated by comparing the proposed to existing models. The results explain that the proposed model-based cardiac disease diagnosis model surpasses previous techniques. The proposed method demonstrates that it attains accuracy of 98.5 % for the maximum amount of records, which is higher than available classifiers.

• The Journal of the Korean dental association
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• v.51 no.9
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• pp.511-517
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• 2013

To assess the association between oral health and general health, this study examined the relationship between chewing difficulty and twelve chronic health conditions such as hypertension, hyperlipidemia, diabetes, cerebro- and cardiovascular disease, musculoskeletal disease, respiratory disease, eye/nose/throat disease, stomach/intestinal ulcer, renal dysfunction, thyroid disease, depression, and cancer in Korea. The study population was 3,066 elders aged 65 years old and more from the fourth Korean National Health and Nutrition Examination Survey. Chewing difficulty was measured on a 5-point Likert scale. Chronic conditions were assessed by self-reported questionnaire. Confounders were age, gender, education, income, smoking, drinking, and obesity. Chi-square test, general linear model, and multiple logistic regression model were done with complex sampling design. Musculoskeletal disease (adjusted odds ratio=1.33), respiratory disease (adjusted odds ratio=1.52), and cancer (adjusted odds ratio=1.58) were independently associated with chewing difficulty. Multiple chronic conditions with more than 4 chronic disease showed significant association with chewing difficulty (adjusted odds ratio=1.37).