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A phoneme recognition system based on Discrete Wavelet Transforms (DWT) and Support Vector Machines (SVMs), is designed for multi-speaker continuous speech environments. Phonemes are divided into frames, and the DWTs are adopted, to obtain fixed dimensional feature vectors. For the multiclass SVM, the One-against-one method with the RBF kernel was implemented. To further improve the accuracies obtained,...
To handle high dimensional variables in real world, especially multimedia data, dimension reduction techniques provide effective solutions for feature selection which makes the problem easy to deal in a lower dimension subspace. However, the primary problem with traditional dimension reduction method is to estimate intrinsic dimension of manifold supporting the raw data. Since all existing approaches...
This paper presents a weighted support vector machine (WSVM) based on association rules for two-class classification problems. The basic idea of the WSVM is to assign different weights to different data points to minimize impacts of outliers. In this paper, we apply association rules to generate weights to prevent bias to the majority class for imbalanced binary classification problems. Experimental...
Conventional methods of determining Pareto dominance in multi-objective optimization evaluate and compare objective vectors of candidate solutions, but the computation and (or) experiment of evaluating objective vectors are overwhelmingly costly when computationally expensive multi-objective problems are involved. This study investigates a nearest neighbor prediction method of Pareto dominance using...
Gesture recognition has attracted significant interest due to diverse potential applications, including: hand writing recognition, robot control and human-computer interfaces. This paper identifies and addresses three shortcomings in current approaches to feature vector selection and parameter optimisation for continuous gesture recognition. First, in selecting the final feature vector, researchers...
In this paper, a novel method of target identification in foliage environment is presented. This method takes the received signal waveforms to identify the targets between the communication transceivers, which are measured by Ultra WideBand (UWB) Impulse Radio (IR) equipment under foliage environment. In this way, most existing UWB-IR transceivers can be exploited as detecting radar sensors, which...
As computers and information technologies become ubiquitous throughout society, the security of our networks and information technologies is a growing concern. As a result, many researchers have become interested in the security domain. Among them, there is growing interest in observing hacker communities for early detection of developing security threats and trends. Research in this area has often...
Several studies for palmprint-based personal identification have focused on improving the performance of palmprint images captured under visible light. However, during the past few years, some researchers have considered multispectral images to improve the effect of these systems. Compared with color images, multispectral images provide additional information due to its variety of spectral bands....
In this paper, we propose a novel model for Document Representation in an attempt to address the problem of huge dimensionality and vector sparseness that are commonly faced in Text Classification tasks. We conduct our experiments on data sets of Opinion Mining. We use as classifiers Support Vector Machines (SVM) and k-Nearest Neighbors (kNN). We compare the performance of our model with that of the...
The banknote manufacturing industry is shrouded in secrecy, fundamental mechanics of security components are closely guarded trade secrets. Currency forensics is the application of systematic methods to determine authenticity of questioned currency. However, forensic analysis is a difficult task requiring specially trained examiners, the most important challenge is automating the analysis process...
A problem of major interest for local governmental authorities is the monitoring of the number of cars invading the streets and the open parking lots. Different technologies have been used for such purpose. An emerging one is based on the use of remotely sensed images of very high spatial resolution, in particular those acquired from unmanned aerial vehicles. In this paper, we present a method to...
This paper presents a novel technique for detecting bleeding regions in capsule endoscopy images. The proposed algorithm extracts color features from image-regions by calculating mean, standard deviation, skew and energy from the first order histogram of the RGB planes separately. Through the use of RGB color space, three times more number of features can be obtained than while using a grayscale image...
A computational intelligence problem with mapping of multiple classes for a given input feature is addressed in this paper. The objective is to classify a vector of class for a given vector of input features. Each class is a member of disjoint set called dimension and hence, it is called multidimensional learning. Dependency between the classes and dimensions are usually not taken into account while...
Most sign language recognition systems that use gloves and hand trackers combine the data from both devices at the sensor level. In this paper we propose a new approach by combining information acquired from the gloves and the hand tracking systems at the decision level using the Dempster-Shafer theory of evidence. The results using the Dempster-Shafer on the recognition of 100 two-handed signs show...
In this paper we propose a structure based sparse model with different constrains by extending the general sparse model to the multiple pixels case, where each pixel together with its neighboring pixels are used simultaneously in the sparse representation of chromosome classes. We use the model to classify multicolor fluorescence in-situ hybridization (M-FISH) images. Both the simulation and real...
This paper presents a online multi-font numeral recognition method, whose main aim is to recognize overlaid time numeral from video. The portion of the video frame containing the time text is binarized and segmented. Minimum rectangular bounding box is inserted over the isolated numeral images. Euler number of numeral images is found out to initially differentiate into three groups. Then, the numerals...
The desired performance of every childcare and monitoring system is to clearly read the user activity into a relevant category of the solution domain. This categorization highly depends on error free processing methods and systematic regression or classification. The wearable interface acquires multiple signals of the user activity that serves as the input to the monitoring system. The pattern of...
Recently, a novel "completely automated public Turing test to tell computers and humans apart (CAPTCHA)'' system has been proposed, in which users are asked to separate natural faces of humans and artificial faces of virtual world avatars. The system is based on the assumption that computers cannot separate them while it is an easy task for humans. Conventional digital forensics approaches to...
Spectral classification for hyperspectral image is a challenging job because of the number of spectral in a hyperspectral image and high dimensional spectral. In this paper, we proposed a method to enhance the spectral classification using the Adaboost for hyperspectral image analysis. By applying the Adaboost algorithm to the classifier, the classification can be executed iteratively by giving weight...
In this paper, A novel classification approach based on sparse representation framework is proposed. The method finds the minimum Euclidian distance between an input patch (pattern) and atoms (templates) of a learnt-base dictionary for different classes to perform the classification task. A mathematical approach is developed to map the sparse representation vector to Euclidian distances. We show that...
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