• Journal of Korea Multimedia Society
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• v.16 no.3
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• pp.269-280
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• 2013

In this paper, we suggested the possibility of using an efficient classifier for the dependency analysis of retinal disease. First, we analyzed the classification performance and the prediction accuracy of GBN (General Bayesian Network), GBN with reduced features by Markov Blanket and TAN (Tree-Augmented Naive Bayesian Network) among the various bayesian networks. And then, for the first time, we applied TAN showing high performance to the dependency analysis of the clinical data of retinal disease. As a result of this analysis, it showed applicability in the diagnosis and the prediction of prognosis of retinal disease.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.64-79
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• 2017

Identifying disease genes from human genome is a critical task in biomedical research. Important biological features to distinguish the disease genes from the non-disease genes have been mainly selected based on traditional feature selection approaches. However, the traditional feature selection approaches unnecessarily consider many unimportant biological features. As a result, although some of the existing classification techniques have been applied to disease gene identification, the prediction performance was not satisfactory. A small set of the most important biological features can enhance the accuracy of disease gene identification, as well as provide potentially useful knowledge for biologists or clinicians, who can further investigate the selected biological features as well as the potential disease genes. In this paper, we propose a new stepwise random forests (SRF) approach for biological feature selection and disease gene identification. The SRF approach consists of two stages. In the first stage, only important biological features are iteratively selected in a forward selection manner based on one-dimensional random forest regression, where the updated residual vector is considered as the current response vector. We can then determine a small set of important biological features. In the second stage, random forests classification with regard to the selected biological features is applied to identify disease genes. Our extensive experiments show that the proposed SRF approach outperforms the existing feature selection and classification techniques in terms of biological feature selection and disease gene identification.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.211-221
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• 2024

Pakistan is a top producer and exporter of high-quality rice, but traditional methods are still being used for detecting rice diseases. This research project developed an automated rice blast disease diagnosis technique based on deep learning, image processing, and transfer learning with pre-trained models such as Inception V3, VGG16, VGG19, and ResNet50. The modified connection skipping ResNet 50 had the highest accuracy of 99.16%, while the other models achieved 98.16%, 98.47%, and 98.56%, respectively. In addition, CNN and an ensemble model K-nearest neighbor were explored for disease prediction, and the study demonstrated superior performance and disease prediction using recommended web-app approaches.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.303-308
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• 2017

Nowadays, living standard is improved and people have high interest to the personal health care problem. Accordingly, people desire to know the personal physical condition and the related medical treatment. Thus, there is the necessary of the personalized medical treatment, and there are many studies about the automatic disease diagnosis and the related services. Those studies focus on the particular disease prediction which is based on the related particular data. However, there is no studies about the medical treatment prediction. In our study, national health data based medical treatment predictor is built by using SVM, and the performance is evaluated by comparing with other prediction methods. The experimental results show that the health data based medical treatment prediction resulted in the average accuracy of 80%, and the SVM performs better than other prediction algorithms.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• International Journal of Industrial Entomology and Biomaterials
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• v.6 no.2
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• pp.163-169
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• 2003

Studies were conducted on the effect of pruning time, host age, conidial dispersal and weather parameters on the incidence and severity of mulberry leaf spot (Myrothecium roridum). The disease severity (%) increased with increase in shoot age irrespective of pruning date. Maximum disease severity was observed in plants pruned during first week of April and minimum disease severity in plants pruned during first week of March. Significant (P ＜ 0.01) influence of date of pruning, shoot age and their interaction was observed on severity of the disease. Apparent infection rate (r) was significantly higher during the plant growth period from day 48 to day 55. Average apparent yale was higher in plants pruned during first week of April and least in plants pruned during first week of July. The disease infection was negatively correlated to distance from the inoculum source. Leaf spot severity (%) was influenced by weather parameters. Multiple regression analysis revealed contribution of various combinations of weather parameters on the disease severity. Linear prediction model $(Y ＝ －81.803＋1.176x_2＋0.765x_3) with significant $R^2$ was developed for prediction of the disease under natural epiphytotic condition.

• The Plant Pathology Journal
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• v.38 no.4
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.6
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• pp.1113-1118
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• 2011

In this paper, when a physician make a diagnosis of the pattern identification (PI) in Korean stroke patients, the development methods of the PI classification function is considered by diagnostic questionnaire of the PI for stroke patients. Clinical data collected from 1,502 stroke patients who was identically diagnosed for the PI subtypes diagnosed by two physicians with more than 3 years experiences in 13 oriental medical hospitals. In order to develop the classification function into PI using Korean Stroke Syndrome Differentiation Standard was consist of the 44 items (Fire heat(19), Qi deficiency(11), Yin deficiency(7), Dampness-phlegm(7)). Using the 44 items, we took diagnostic and prediction accuracy rate through of discriminant model. The overall diagnostic and prediction accuracy rate of the PI subtypes for discriminant model was 74.37%, 70.88% respectively.

• Smart Media Journal
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• v.11 no.4
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• pp.62-68
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• 2022

To prevent the spread of disease in aquaculture, it is a need for a system to predict fish diseases while monitoring the water quality environment and the status of growing fish in real time. The existing research in predicting fish disease were image processing techniques. Recently, there have been more studies on disease prediction methods through deep learning techniques. This paper introduces the research results on how to predict diseases of Paralichthys Olivaceus with deep learning technology in aquaculture. The method enhances the performance of disease detection rates by including data augmentation and pre-processing in camera images collected from aquaculture. In this method, it is expected that early detection of disease fish will prevent fishery disasters such as mass closure of fish in aquaculture and reduce the damage of the spread of diseases to local aquaculture to prevent the decline in sales.

• Korean Journal Plant Pathology
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• v.14 no.2
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• pp.150-156
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• 1998

To develop a model for prediction of turnip mosaic virus(TuMV) disease progress of Chinese cabbage based on weather information and number of TuMV vector aphids trapped in Taegwallyeong alpine area, data were statistically processed together. As the variables influenced on TuMV disease progress, cumulative portion(CPT) above 13$^{\circ}C$ in daily average temperature was the most significant, and solar radiation, duration of sunshine, vector aphids and cumulative temperature above $0^{\circ}C$ were significant. When logistic model and Gompertz model were compared by detemining goodness of fit for TuMV disease progress using CPT as independent variable, regression coefficient was higher in the logistic model than in the Gompertz model. Epidemic parameters, apparent infection rate and initial value of logistic model, were estimated by examining the relationship between disease proportion linearized by logit transformation equation, In(Y/Yf-Y) and CPT. Models able to describe the progression of TuMV disease were formulated in Y=100/(1+128.4 exp(-0.013.CPT.(-1(1/(1+66.7.exp(-0.11.day). Calculated disease progress from the model was in good agreement with investigated actual disease progress showing high significance of the coefficient of determination with 0.710.