The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In recent years, Image classification has been a growing research area in the computer vision field. Thus, many approaches were proposed in literature. Moreover, many content-based image classification approaches are widely used in developing applications and techniques for many areas such as remote-sensing and content-based image retrieval. In this study, we introduce a new technique for content-based...
Ensemble learning is a method to improve the performance of classification and prediction algorithms. It has received considerable attention because of its prominent generalization and performance improvement. However, its performance can be degraded due to multicollinearity problem where multiple classifiers of an ensemble are highly correlated with. This paper proposes genetic algorithm-based coverage...
Gender classification problem is an active area of research; recently it had attracted many researchers. This study presents an efficient gender classification technique. Weighted Majority Voting (WMV) is the most popular technique used to combine individual classifiers in an ensemble based classification. Genetic Algorithm (GA) is a global optimization technique and is being widely used by the researchers...
With the advancement in modulation schemes and cognitive techniques, Powerline Communications (PLC), have gained tremendous importance as a medium for transmission of variety of signals. The varied signals when sent through a common channel require a rigorous classification procedure for effective routing at both the transmission and receiving ends. In this paper we present complete software based,...
The selection for the number of hidden nodes for a neural network is of critical importance. This paper proposes a novel algorithm to determine the number of hidden nodes of a neural network and optimize it. In the method, the number of hidden nodes H is first computed by empirical formulas, and the range of H is determined according to computed result. Then, the "three points search" is...
The need to secure sensitive data and computer systems from intruders, while allowing ease of access for authenticate user is one of the main problems in computer security. Traditionally, passwords have been the usual method for controlling access to computer systems but this approach has many inherent flaws. Keystroke dynamics is a promising biometric technique to recognize an individual based on...
This paper, that continues a previous research, has as primer goal the improvement of a brain computer interface (BCI) system that uses a new features extracting method named Adaptive Nonlinear Amplitude and Phase Process (ANAPP). The ANAPP method models the EEG signals as a combination of five a priori ??spontaneous cortical oscillations?? whose amplitudes and phases are established using an adaptive...
Band selection is an important preprocessing procedure for analysis of hyperspectral data, which suffers from the vast amount of data and Hughes phenomenon. In recent years, band (feature) selection using Neural Network such as Multi-layer Forward Neural Network (MLFNN), Radial Basis Function Neural Network (RBFNN) and Double Parallel Feedforward Neural Network (DPFNN) becomes a promising method for...
In this paper, a new combinational method for improving the recognition rate of multiclass classifiers is proposed. The main idea behind this method is using pairwise classifiers to enhance the ensemble. Because of more accuracy of them, they can decrease the error rate in error-prone feature space. Firstly, a multiclass classifier has been trained. Then, regarding to confusion matrix and evaluation...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.