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Helitron is considered as very important type of DNA involved in mechanism's evolution. These elements are not well studied and the major researches done are biological experiments. In this paper, we propose a novel approach aiming to characterize and classify helitron's types. Accordingly, we use the Support Vector Machine (SVM) classification technique known to be preferment in DNA related studies...
I-vector space feature has been recently proved to be very efficient in speaker recognition field. In this paper, we assess using the i-vector approach for emotional speaker recognition in order to boost the performance which is deteriorated by emotions. The key idea of the i-vector algorithm is to represent each speaker by a fixed length and low dimensional feature vector. The concatenation of these...
Activity recognition has received a lot of attention from research scholars in the past few years. There has been a huge demand for activity recognition because of its ability to ease human-machine interaction, help in care for the elderly, and monitor the habitat requirements of the wildlife. In this paper, a Support Vector Machine (SVM) classifier to recognize the human activities has been built...
Epileptic seizure detection requires the study of electroencephalogram (EEG) data. Visual marking of seizure onset in such EEG recordings is quite tedious, naturally subjective, extremely time consuming, and it may lead to inaccurate detection. Thus, the development of a robust framework for automatic seizure classification is necessary and can be very useful in epilepsy investigation. In this paper,...
Analysis of breath sounds for the purpose of diagnosing respiratory pathology is of great interest in recent years. In this paper, classification of normal, wheeze, rhonchi, line and coarse crackles using breath sound signal recording is performed using signal processing and machine learning tools. Breath sounds were filtered from noise and segmented into breath cycles followed by feature extraction...
In this research, a prototype of home appliances control system based on steady-state visually evoked potential (SSVEP) is designed. The system is designed using two SSVEP datasets with different characteristics: the first dataset consists eight frequencies within 6-12 Hz, while the second consists frequencies of 8, 14, and 28 Hz. The EEG signal from the datasets is processed using three components:...
In this contribution, classification of two main neuromuscular diseases namely Myopathy and Neuropathy and Healthy signals is performed using cross-correlation based feature extraction technique. For this purpose, cross-correlation of Healthy, Myopathy and Neuropathy disease EMG signal is done with a reference Healthy signal. Selective features like Hjorth, Adaptive Autoregressive and statistical...
In human computer interaction, speech emotion recognition is playing a pivotal part in the field of research. Human emotions consist of being angry, happy, sad, disgust, neutral. In this paper the features are extracted with hybrid of pitch, formants, zero crossing, MFCC and its statistical parameters. The pitch detection is done by cepstral algorithm after comparing it with autocorrelation and AMDF...
Aiming at the problem that the action recognition algorithms based on vision have a high requirement of the background and the human position relative to the sensor, an algorithm which is robust to the position changing of the human is proposed. The Microsoft v2 is used to collect skeleton data and standardize it, then the feature vectors are extracted from the data, at last after the correction of...
In this paper, a robust technique to construct feature vector for gender classification has been proposed. Discrete Wavelet transform is used in concatenation with Discrete Cosine transform to form the feature vector. Initially, multi-level Discrete Wavelet transform is applied to images to obtain the approximation coefficients of image. Discrete Cosine transform are then calculated for the obtained...
With the development of stone processing and sales, effective stone surface texture image recognition methods are needed. We proposed a new stone surface texture image recognition method based on texture and colour. We combine the following visual features: Gabor features which can well simulate the single cell sensing profile of mammalian visual neurons, The Grey-level Co-occurrence Matrices(GLCM)...
This paper implements a feature extraction technique for recognizing online handwritten Gurmukhi characters. For attaining high recognition accuracy in such a system, computation of suitable features is an important task. DFT (Discrete Fourier Transform) based feature extraction technique is employed in this work. In this paper, we have considered 86 stroke classes of Gurmukhi script. We have taken...
The detection and identification of the kinds of ships, i.e., warship or merchant ship, is of great interest for military use. Ships are usually detected and recognized based on ship physical fields, and the commonly used ship physical fields include sound field, magnetic field, hydraulic pressure field, electric field, gravity field, etc, which all contain plenty of discriminative information. However,...
Corner reflector is a common passive electronic countermeasure, which has the advantages of low cost, high effectiveness, etc. SVM is a kind of new method in the field of data mining. It is mainly to solve the classification and discrimination problems. This paper analyzes the characteristics difference of radar echo between ship and corner reflector, then discriminates ship and corner reflector by...
User intention understanding from text is an important task in NLP. In this paper, we study the problem of phone-changing intention prediction. And we propose a novel feature extraction method, which selects the most representative intention feature, to represent user's intention from text scratch. Then we adopt a supervised learning approach, that is to train SVM classifier, for intention prediction...
The most widely used classification techniques for whole brain image classification rely on kernel machines such as support vector machines and Gaussian processes, due to their computational efficiency, accurate prediction and suitability to tackle the combination of small sample sizes and high dimensionality that make neuroimaging data a challenging problem. Such methods generally make use of linear...
A robust model is sought for the identification of electroencephalographic (EEG) signals including movements of three distinct parts of the user's arm, namely hand, elbow and shoulder. This study investigates the classification performances of the same upper limb motor movements using various kernel functions of the support vector machine (SVM). Polynomial, linear and radial basis (RBF) functions...
A new technique to construct feature vector for gender classification is proposed in this paper. Here, new feature reduction technique is used to remove the irrelevant features of images. Feature reduction also helps in reducing the over fitting problem of the dataset. KPCA is a kernel based PCA which maps data from original space to non-linear feature space. Kernel trick helps in reducing the expensive...
In this paper, a new technique for constructing feature vector from DCT coefficients for gender classification has been presented. Firstly, images are divided into 8 × 8 sub images. DCT coefficients are calculated for each block in image. New technique is used for constructing the feature vector from DCT coefficients. Finally, SVM with Rbf kernel is used for classifying the images into male and female...
To efficiently identifying the bearing fault in gear-rotor-bearing system of wind turbines, this paper presented an algorithmic solution to carry out the analysis of vibration signals of bearings by the use of ensemble empirical mode decomposition (EEMD) and support vector machine (SVM). Through appropriately decomposing the obtained original vibration signals into a collection of intrinsic mode functions...
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