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This study presents spatial analysis of Dengue Fever (DF) outbreak using Geographic Information System (GIS) in the state of Selangor, Malaysia. DF is an Aedes mosquito-borne disease. The aim of the study is to map the spread of DF outbreak in Selangor by producing a risk map while the objective is to identify high risk areas of DF by producing a risk map using GIS tools. The data used was DF dengue...
It is deficiency to use accuracy as a measurement to evaluate model classifying ability. This paper proposes a measurement method which uses the area under the ROC curve, or AUC value, to evaluate the performance of the model. Furthermore, applying cross validation and grid-search methods, through designed algorithms, to build an optimization of support vector machines medical prediction model. The...
The quality of peanut kernels is referred to the every aspect of the profit of supply and marketing. A BP neural network model of quality grade testing and identification is built which is based on 52 appearance features such as the form, texture, and color and so on with technology of computer image processing. The testing aiming at 1400 grains is made separately in unsound kernel, mildewing, impurity,...
Support vector machine (SVM) is discussed to use for recognizing cucumber leaf diseases in this paper. Considering that it is a small number of samples, a new experimental program has been proposed which takes each spot of leaves as a sample instead of taking each leaf as a sample. In the experiments Radial Basis Function (RBF), polynomial and Sigmoid kernel function were also used to carry out comparative...
Artificial intelligence techniques have been extensively used for the identification of several disorders related with the voice signal analysis, such as Parkinson's disease (PD). However, some of these techniques flaw by assuming some separability in the original feature space or even so in the one induced by a kernel mapping. In this paper we propose the PD automatic recognition by means of Optimum-Path...
This paper presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of the Alzheimer type dementia. The proposed methodology is based on the calculation of the skewness to each m-by-m sliding block of the transaxial slices of the SPECT brain images. We replace the center pixel in the m-by-m block by the skewness value and build a new 3-D brain image which will...
Nowadays, with advancement of technology and science and expansion of computer usage in high-tech calculations, especially in the field of medicine, intelligence systems and in particular ANN are becoming of significant importance in automatic diagnosis and prognoses of different diseases. In this article, we have used several ANN architectures (namely RBF, PNN, LVQ) and SVMs, diagnosing thyroid diseases...
In this paper, we use support vector machine (SVM) and artificial neural networks to diagnosis hepatitis diseases. Furthermore, we use those networks to identify the type and the phase of disease. Considering the most important hepatitis cases leads us to six classes: hepatitis B (two phases), hepatitis C (two phases), non-viral hepatitis and no-hepatitis. For this purpose, we design various networks...
This paper reports results from a comprehensive performance comparison among standalone machine learning algorithms (SVM, MLP and GRNN) and their combinations in ensembles of classifiers when applied to a medical diagnosis problem in the field of orthopedics. All the aforementioned learning strategies, which currently comprises the classification module of the SINPATCO platform, are evaluated according...
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