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The compressed sensing (CS) paradigm unifies sensing and compression of sparse signals in a simple linear measurement step. Reconstruction of the signal from the CS measurements relies on the knowledge of the measurement matrix used for sensing. Generation of the pseudo-random sensing matrix utilizing a cryptographic key, offers a natural method for(More)
This paper deals with the strategies for feature selection and multi-class classification in the emotion detection problem. The aim is two-fold: to increase the effectiveness of four feature selection algorithms and to improve accuracy of multi-class classifiers for emotion detection problem under different frameworks and strategies. Although, a large(More)
This paper is mainly concerned with an investigation of the suitability of Hopfield neural network structures in solving the power economic dispatch problem. For Hopfield neural network applications to this problem three important questions have been answered: what the size of the power system is; how efficient the computational method; and how to handle(More)
In literature improvements in neural learning are reported on, which have been achieved through input data manipulation, based on entirely experimental studies. Theoretical background is not supplied for these studies and neural networks are employed as a “black box” model. Within this work, this problem is highlighted and the impact of the modified(More)
Accepted: 14.05.2014 Students might have different type and different level of perceptions: Positive or negative perceptions on programming; a perception on benefit of programming, perceptions related to difficulties of programming process etc. The perception of student on their own competence is defined as self-efficacy. Based on the discussions reported(More)
In this study, the performance of two neural classifiers; namely Multi Layer Perceptron (MLP) and Radial Basis Fuction (RBF), are compared for a multivariate classification problem. MLP and RBF are two of the most widely neural network architecture in literature for classification and have successfully been employed for a variety of applications. A(More)
In this paper, we propose two new frameworks, so as to boost the feature selection algorithms in a way that the selected features will be more informative in terms of class-separability. In the first framework, features that are more informative in discriminating an emotional class from the rest of the classes are favoured for selection by the feature(More)
Emotion detection has gained increasing attention and become an active research area. The problem is solved with improved feature set with different number of feature groups, by employing different classifiers in order to achieve satisfactory recognition rate. In this study, speech related features are employed to evaluate the performance of different(More)
Color detection is generally a primary stage in most of the image processing application, if the application is based on the color information, such as road sign detection, face detection, skin color detection, object detection and object tracking etc. As the performance of subsequent modules in an image processing application is adversely affected by the(More)