Machine learning rises in varied areas of computer science. A deep conventional neural network is powerful visual models of machine learning. We tend to present robustness and effective structure for the iris recognition system. The image first pass through these stages: enhancing the image quality, determine the iris and pupil center and radius for iris segmentation, converting the image from the Cartesian coordinates to the polar coordinates to reduce the time of processing. The proposed system is named IRISNet that extracting the feature and classifying them automatically without any domain knowledge. The architecture of IRISNet consists of Convolutional Neural Network layers for extract feature and Softmax layer to classify these features into N classes, for training CNN the backpropagation algorithm and Adam optimization method are used for updating the weights and learning rate, respectively. The performance of the proposed system was evaluated using IITD V1 iris database. The results obtained from the proposed system outperform supervised classification model (SVM, KNN, DT, and NB). The identification rate 97.32% and 96.43% for original and normalized images respectively. The recognition time per person is less than one second.
Electrooculogram (EOG) and power line noise artefact detection and rejection have commonly utilized Stone's blind source separation (Stone's BSS) algorithm. The paper suggests a hybrid method between particle swarm optimization (PSO) and Stone's BSS for the detection and rejection of electrooculogram (EOG) and power line noise in the single-channel without the use of a notch filter. The proposed method contains three major steps: centralizing and whitening of the input EEG signal, incorporating the processing EEG signal into the iterative algorithm of the particle swarm optimization (PSO) to randomly generate the optimal value of (hshort, hlong) and weight vector W parameters, and applying Stone's BSS using the generalized eigenvalue decomposition (GEVD) method to eliminate electrooculogram (EOG) and power line noise artefacts to obtain a clean EEG signal. The authors assess the robustness of the suggested method evaluated using real and simulated electroencephalogram (EEG) data sets. The simulated electroencephalogram (EEG) data and electrooculogram (EOG) and line noise (LN) artefacts are produced and mixed randomly in the MATLAB program; two types of real EEG data are taken in 9 and 19 channels. Evaluation results show the proposed algorithms as effective techniques for extracting both the power line noise and electrooculogram (EOG) artefacts from brain mixtures compared to specific BSS algorithms (e.g., Stone's BSS, evolutionary fast independent component analysis (EFICA), fast independent component analysis (FastICA), and joint approximate diagonalization of Eigen matrices (JADE)) while preserving the clinical features of the reconstructed EEG signal.
In this paper the Jeffery prior information and the extension of Jeffery prior information for estimating the parameter Weibull distribution is presented. Through simulation study the performance of this estimator was compared to the standard Bayes with Jeffery prior information with respect to the mean square error (MSE) and mean percentage error (MPE). In the results, The new estimator with extension of Jeffery prior information is the best estimator for Weibull Distribution, when compared it with standard Bayes with Jeffery prior information. Also depending on MSE and MPE, the is the best survival function for Weibull distribution when compared it with survival function based on posterior distribution. We can easily conclude that MSE and MPE of Bayes estimators decrease with an increase of sample size.
Electroencephalogram (EEG) extraction has widely used Stone's Blind Source Separation (Stone's BSS) algorithm. However, Stone's BSS algorithm is sensitive to the initial half-life (ℎlong, ℎshort) and weight vector W parameters, which affect the convergence of the algorithm. This paper proposes a hybridization of Stone's BSS with Particle Swarm Optimization (PSO) to boost the separation process. An improved Stone's BSS (ISBSS) method is employed to reject eye blinking from the electroencephalogram (EEG) mixture. The electroencephalogram (EEG) mixed-signal is first centralized and whitened; then, it is incorporated into the particle swarm optimization (PSO) iterative algorithm to process the initial (ℎlong, ℎshort) and generate the weight vector W parameters randomly. Finally, the generalized eigenvalue decomposition (GEVD) method is applied to extract EEG singles to obtain a clean EEG signal. A clinical EEG database is used to test the improved and other algorithms. The GEVD method estimates the measurement performance of the proposed algorithm using a carrier-to-interference ratio and integral square error and compares the proposed algorithm with the conventional Stone's BSS, fast independent component analysis (FastICA), evolutionary fast independent component analysis (EFICA), and joint approximate diagonalization of eigen matrices (JADE) algorithms to check its effectiveness. The results show that the suggested hybrid method has a better performance and decreasing elapsed time than conventional Stone's BSS and other algorithms.
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