Abdelghani Harrag

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Feature Selection is an important task which can affect the performance of pattern classification and recognition. In this paper, we present a feature selection algorithm based on genetic algorithm optimization. The algorithm adopts classifier performance and the number of the selected features as heuristic information, and selects the optimal feature(More)
Feature extraction is the process of deriving new weakly correlated features from the original features in order to reduce the cost of feature measurement, increase classifier efficiency, and allows higher classification accuracy. The selection and quality of the features representing each pattern have considerable bearing on the success of subsequent(More)
In the last years, face verification has gained a great interest in the pattern recognition community and in many application fields. It is among the most attractive research areas because face images can be captured in a non-intrusive way. Many algorithms have been developed in this area, among them the Principal Component Analysis (PCA) is a typical face(More)
This paper assesses two popular speaker features prosodic and cepstral coefficient. We compare the performance of individual features and features combined via PCA and LDA. The identification process can be performed both in the temporal and cepstral domains. The result show that the reduced sets of the composite LDA feature allow more robust estimates for(More)
This paper concerns the study and simulation of a PV array self-organizing configuration. It introduces a new method to reconfigure the PV array using a genetic algorithm in order to maximize the output power as well as reducing the number of switching. The proposed method involves the simulation of a PV array composed of 16 panels 4 strings with 4 panels(More)
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