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In this paper, we investigate the modulation recognition method based on locality preserved projection (LPP) in AWGN channels. Feature extraction is the precondition of signal modulation recognition. Based on analyzing the characteristic of signal in time and frequency domain, seven feature parameters with fine classification information are selected. In order to wipe off the relativity among different...
In this paper, we present a distributed computing framework for image classification towards the current challenge of image big data due to enormous streaming image data sources, such as image sharing over online social network and massive video surveillance streams from ubiquitous cameras all over our daily life. The proposed framework consists of four modules aiming at feature extraction, dimension...
This paper presents the process of Quranic Accent Automatic Identification. Recent feature extraction technique that is used for Quranic verse rule identification/Tajweed include Mel Frequency Cepstral Coefficients (MFCC) which prone to additive noise and may reduce the classification result. Therefore, to improve the performance of MFCC with addition of Spectral Centroid features and is proposed...
With the development of artificial intelligence and pattern recognition, facial expression recognition plays a more and more important role in intelligent human-computer interaction. In this paper, we present a model named K-order emotional intensity model (K-EIM) which is based on K-Means clustering. Different from other related works, the proposed approach can quantify emotional intensity in an...
Falling can cause significant injury, where quick medical response and fall information are critical to providing aid. In this paper we present a wearable wireless fall detection system utilising a Shimmer accelerometer device, where important additional information is obtained, such as direction and strength of the occurred fall instance. Discrete Wavelet Transforms and multiresolution wavelet analysis...
Multi-instance learning (MIL) has been widely applied to diverse applications involving complicated data objects such as images and genes. However, most existing MIL algorithms can only handle small-or moderate-sized data. In order to deal with the large scale problems in MIL, we propose an efficient and scalable MIL algorithm named miFV. Our algorithm maps the original MIL bags into a new feature...
Currency recognition system is one of the fast growing research fields under image processing. This paper proposes a novel method for Indian currency recognition. Our proposed approach identifies denomination by extracting features like Center Numeral, Shape, RBI Seal, Latent Image and Micro Letter. Principal Component Analysis is used to reduce the dimensions and a similarity based classifier is...
Most of the state-of-the-art methods for action recognition are very complex and variant to the geometric transformation like scaling, translation and rotation. Cuboid based method required all frames to extract the cuboid of action that's why cuboid based methods are expensive. Other methods use contour based approach for feature representation which is not robust to noise. So we require a very fast...
Gender classification can play a significant role in security and surveillance system. It aids in identification of a person by recognizing its gender (male/female) from the face image only. Extracting discriminate features for male and female is a fundamental and challenging problem in the field of computer vision. In this manuscript, a combination of Approximation Face Image (AFI) with Principal...
We present a new method of classifying previously unseen Android applications as malware or benign. The algorithm starts with a large set of features: the frequencies of all possible n-byte sequences in the application's byte code. Principal components analysis is applied to that frequency matrix in order to reduce it to a low-dimensional representation, which is then fed into any of several classification...
Face recognition has been an area of interest among researchers in pattern recognition for the past few decades. Researches in face recognition are basically concentrated on texture based and geometry based features. The main advantage of Face recognition systems utilizing depth information is the availability of geometrical information of the face structure which is more or less unique for a subject...
In this paper, a new type of hybrid method that hybridizes PCA and EBGM as a two-stage procedure is presented to improve recognition performance in large-scale face recognition. Among various methods in face recognition, PCA is considered to identify human faces by holistic views, while EBGM is supposed to distinguish one face from another by details, but they are both excellent representative methods...
Over the past few decades, a considerable amount of literature has been published on shape classification. Since classification of well-segmented shapes has become easy to achieve, a number of recent studies have emphasized the importance of robustness to noise and deformations. So in this paper, we undertake the task of classifying similar & noisy binary shape images, using a biologically inspired...
fMRI (functional magnetic resonance imaging) studies frequently create high dimensional datasets, with far more features (voxels) than examples. It is known that such datasets frequently have properties that make analysis challenging, such as concentration of distances. Here, we calculated the probability of distance concentration and proportion of variance explained by PCA in two fMRI datasets, comparing...
Recently, randomized partition trees have been theoretically shown to be very effective in performing high dimensional nearest neighbor search. In this paper, we introduce a variant of randomized partition trees for high dimensional nearest neighbor search problem and provide theoretical justification for its choice. Experiments on various real-life datasets show that performance of this new variant...
In this paper we present a comparative study of two well-known face recognition algorithms. The contribution of this work is to reveal the robustness of each FR algorithm with respect to various factors, such as variation in pose and low resolution of the images used for recognition. This evaluation is useful for practical applications where the types of the expected images are known. The two FR algorithms...
In this paper, linear discriminant analysis (LDA) based on Lp-norm (LDA-Lp) optimization method is proposed. The objective function utilizing the Lp-norm with arbitrary p value is studied. By maximizing the Lp-norm-based ratio between the between-class scatter and the within-class scatter, LDA-Lp can construct a set of local optimal projection vectors. Moreover, the optimal projection vectors can...
Predicting churners in telecom is an important application area of pattern recognition that helps in responding appropriately for retaining customers and saving the revenue loss a corporation suffers. The aim of the churn predictor is to learn the pattern of churners and thus differentiate between churners and non-churners. Handling the large dimensionality and selecting discriminative features are...
Research in student retention is traditionally survey-based, where researchers use questionnaires to collect student data to analyse and to develop student predictive model. The major issues with survey-based study are the potentially low response rates, time consuming and costly. Nevertheless, a large number of datasets that could inform the questions that students are explicitly asked in surveys...
Data reduction as a critical step in the process of data pre-processing presents a central point of interest across a wide variety of fields. Data pre-processing has a significant impact on the performance of any machine learning algorithm. In this context, we focus our research paper on investigating the data pre-processing phase of a recent evolutionary algorithm named the Dendritic Cell Algorithm...
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