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This paper presents an alternative and efficient method for solving a class of constraint parametric optimization problems using particle swarm optimization algorithm (PSO). In this paper, for the first time PSO is used for solving convex parametric programming, but PSO must be adaptive for doing it. So, for obtaining particles velocities, adaptation weight(More)
In this paper, a new method of arrhythmia classification is proposed. At first we extract twenty two features from electrocardiogram signal. We propose a novel classification system based on genetic algorithm to improve the generalization performance of the SVM classifier. For this purpose, we have optimized the SVM classifier design by searching for the(More)
In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: Artificial neural network is a favorable technique to solve optimization problems(More)
Classifying non-stationary and imbalanced data streams encompasses two important challenges, namely concept drift and class imbalance. ''Concept drift'' (or non-stationarity) is changes in the underlying function being learnt, and class imbalance is vast difference between the numbers of instances in different classes of data. Class imbalance is an obstacle(More)
Lip detection is used in many applications such as face detection and lips reading. We have proposed a novel approach for detecting lip using particle swarm optimization (PSO). PSO is used to obtain an optimized map. The image is mapped to Y CbCr color space. The main idea of the method is based on that lip has the high values of Cr and low values of Cb.(More)
State estimation in the presence of non-Gaussian noise is discussed. Since the Kalman filter uses only second-order signal information, it is not optimal in non-Gaussian noise environments. The maximum correntropy criterion (MCC) is a new approach to measure the similarity of two random variables using information from higher-order signal statistics. The(More)
This research is on presenting a new approach for cardiac arrhythmia disease classification. The proposed method combines both Support Vector Machine (SVM) and Genetic Algorithm approaches. First, twenty two features from electrocardiogram signal are extracted. These features are obtained semiautomatically from time-voltage of R, S, T, P, Q features of an(More)