This paper presents a new method to automate the process of epileptic seizure detection in electroencephalogram (EEG) signals using wavelet transform and an improved version of negative correlation learning (NCL) algorithm. An improved version of NCL is proposed by incorporating the capability of gating network, as a dynamic combining part of the mixture of experts (ME), into the combining outputs of base experts which are trained using negative correlation learning algorithm. The NCL training algorithm encourages the base experts to learn different parts or aspects of data set and the gating network provides the local competence of these base experts. Three types of normal (recorded from five healthy persons with eyes open), seizure-free (recorded from epileptogenic zoon of five patients) and epileptic EEG signals were decomposed into wavelet coefficients using discrete wavelet transform. Then the statistical features of the wavelet coefficients were computed representing them into the classifiers. Experimental results show that our proposed method classifies normal, seizure-free and epileptic EEG signals with the accuracy of 96.92% which is significantly better than previous combining methods.
Video quality assessment plays a key role in evaluating and optimizing video systems. In this paper, objective image quality metric, appropriate for the fiber optic image transmission line without accessing to the original images, is evaluated by conventional neural networks. It allows an efficient continuous time scoring of the video stream efficiently by a mark on a scale of zero to five. The image database in this research has been collected in the Semnan University. The certainty of the trained network is above 81 percent. Simulation results show that the proposed method is highly correlated with experimental data collected through the subjective experiments.