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In this paper, we propose a compound pyramid model to predict protein secondary structure, where homology analysis and an improved support vector machine (SVM) technology are used for predicting protein secondary structure. The homology analysis is based on BP network model which uses pair-wise sequence alignment, and SVM classification considers the physical and chemical properties of amino acids...
In this paper, we proposed a useful methodology for the diagnosis of dyslipidemia disease by using novel various features of carotid arterial wall thickness. We measured and tested intima-media thickness of carotid arteries and used them as diagnostic feature vectors. In order to evaluate extracted various features, we tested on five classification methods and evaluated performance of classifiers...
Credit scoring has attracted lots of research interests in the literature. The credit scoring manager often evaluates the consumer's credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant's credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves...
Thyroid gland produces thyroid hormones to help the regulation of the body's metabolism. The abnormalities of producing thyroid hormones are divided into two categories. Hypothyroidism which is related to production of insufficient thyroid hormone and hyperthyroidism related to production of excessive thyroid hormone. Separating these two diseases is very important for thyroid diagnosis. Therefore...
Microarray technology has been widely applied to search for biomarkers of diseases, diagnose diseases and analyze gene regulatory network. Abundance of expression data from microarray experiments are processed by informatics tools, such as supporting vector machines (SVM), artificial neural network (ANN), and so on. These methods achieve good results in single dataset. Nevertheless, most analyses...
In this paper, the classification results obtained from several kinds of support vector machines (SVM) and neural networks (NN) are compared with our proposed classifier. Our approach is based on neural networks and interval neutrosophic sets which are used to classify the input patterns into one of the two binary class outputs. The comparison is based on several classical benchmark problems from...
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