Premature ventricular contraction (PVC) is one of the common cardiac arrhythmias. The occurrence of PVC is dangerous in people who have recently undergone heart. A PVC beat can easily be diagnosed by a doctor based on the shape of the electrocardiogram signal. But in automatic detection, extracting several important features from each beat is required. In this paper, a method for automatic detection of PVC using adaptive neuro-fuzzy inference systems (ANFIS) is presented. In the proposed model first feature selection has been done using backward elimination algorithm, and then an ANFIS has been trained with selected attributes. The performance of the proposed method has been compared with two other methods. Simulation results show that the proposed algorithm, in addition to maintaining the classification accuracy compared to existing methods uses fewer features and requires less computing time, which is suitable for implementation on hardware with limited processing capability. Manuscript profile

Internet applications spreading and its high usage popularity result in significant increasing of cyber-attacks. Consequently, network security has become a matter of importance and several methods have been developed for these attacks. For this purpose, Intrusion detection systems (IDS) are being used to monitor the attacks occurred on computer networks. Data mining Techniques, Machine Learning, Neural networks, Collective Intelligence, Evolutionary algorithms and Statistical methods are some of algorithms which have been used for classification, training and reviewing detection accuracy with analysis based on the standard datasets in Intrusion Detection Systems. In this Paper, the hybrid algorithm is introduced based on decision tree and support vector machine (SVM) using feature selection and decision rules to apply on IDS. The main idea is to use the strengths of both algorithms in order to improve detection, enhance the accuracy and reduce the rate of error detection of the results. In this algorithm, the best features are selected by SVM, afterwards decision tree is used to make decisions and define rules. The results of applying proposed algorithm are analyzed on the standard dataset KDD Cup99. The proposed method guarantees high detection rate which is proved by simulation results. Manuscript profile