Current Issue : January - March Volume : 2014 Issue Number : 1 Articles : 6 Articles
Early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through\r\nappropriate treatment; thus classifying cardiac signals will be helped to immediate diagnosing of heart beat\r\ntype in cardiac patients. The present paper utilizes the case base reasoning (CBR) for classification of ECG\r\nsignals. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia\r\nbeat and atrial fibrillation beat) obtained from the PhysioBank database was classified by the proposed\r\nCBR model. The main purpose of this article is classifying heart signals and diagnosing the type of heart\r\nbeat in cardiac patients that in proposed CBR (Case Base Reasoning) system, Training and testing data for\r\ndiagnosing and classifying types of heart beat have been used. The evaluation results from the model are\r\nshown that the proposed model has high accuracy in classifying heart signals and helps to clinical\r\ndecisions for diagnosing the type of heart beat in cardiac patients which indeed has high impact on\r\ndiagnosing the type of heart beat aided computer....
The primary objective of this paper is to develop the product data models, in which systematic information is defined for\r\naccumulating, exchanging, and sharing in themaintenance of concrete highway bridges.The information requirement and existing\r\nissues and solutions were analyzed based on the life cycle and the standardization for sharing. The member data models and\r\nbusiness data models that defined design and construction information and accumulated results information were developed.The\r\nmaintenance business process in which project participants utilize the product data model was described as utilization scenario.\r\nThe utilization frameworks which define information flow were developed....
In this paper, a new fuzzy group decision making (FGDM) model based on -level sets, is proposed to\r\ngenerate, more accurate fuzzy using, risk priority numbers (RPNs) and ensure to be robust against the\r\nuncertainty. This model allows decision makers (DMs) to evaluate FMEA risk factors using linguistic\r\nterms rather than precise numerical values, allows them to express their opinions independently. A case\r\nstudy is investigated using the proposed model to illustrate its applications in RPN assessment....
Feature selection plays an important role in data mining and pattern recognition, especially in the case of large scale\r\ndata. Feature selection is done due to large amount of noise and irrelevant features in the original data set. Hence,\r\nthe efficiency of learning algorithms will increase incredibly if these irrelevant data are removed by this procedure.\r\nA novel approach for feature selection is introduced in this paper using CHABCF, (Chaotic Artificial Bee Colony\r\nbased on Fuzzy), algorithm which is a combination of three paradigms: (1) Chaos theory (2) Artificial Bee Colony\r\noptimization and (3) Fuzzy logic. The fuzzy logic is used for ambiguity removal while chaos is used for generating\r\nbetter diversity in the initial population of our bee colony optimization algorithm. To demonstrate the efficiency of\r\nour algorithm, we have tested it on some well-known benchmarks such as wine, diabet and iris...
This paper seeks to identify the effective factors on enhancement of employees� participation of central\r\nbank of Iran in suggestions system as well as rating these factors based on analytical hierarchy process\r\n(AHP) technique. The statistical population consists of experts of Economic and Statistic office of central\r\nbank. The means of gathering data is a questionnaire. Also, the statistical method in analysis of descriptive\r\ndata and software us Team expert choice 11. After selecting a 103 population size based on Cochran''s\r\nformula, the obtained results showed that the three effective factors on increase of employees�\r\nparticipation in suggestions system are 1) Background factors, 2) Input factors and 3) Process factors\r\nrespectively....
One of the most important topics in image processing is edge detection. Many methods have been\r\nproposed for this end but most of them have weak performance in noisy images because noise pixels are\r\ndetermined as edge. In this paper, two new methods are represented based on Hierarchical Adaptive Neuro\r\nFuzzy Systems (HANFIS). Each method consists of desired number of HANFIS operators that receive the\r\nvalue of some neighbouring pixels and decide central pixel is edge or not. Simple train images are used in\r\norder to set internal parameters of each HANFIS operator. The presented methods are evaluated by some\r\ntest images and compared with several popular edge detectors. The experimental results show that these\r\nmethods are robust against impulse noise and extract edge pixels exactly....
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