Sharif Digital Repository

This paper introduces a new supergraph, called the k-Semi-Yao graph (k-SYG), of the k-nearest neighbor graph (k-NNG) of a set P of points in Rd. We provide a kinetic data structure (KDS) to maintain the k-SYG on moving points, where the trajectory of each point is a polynomial function whose degree is bounded by some constant. Our technique gives the first KDS for the theta graph (i.e., 1-SYG) in Rd. It generalizes and improves on previous work on maintaining the theta graph in R2. As an application, we use the kinetic k-SYG to provide the first KDS for maintenance of all the k-nearest neighbors in Rd, for any k≥1. Previous works considered the k=1 case only. Our KDS for all the 1-nearest... 

A novel distance measure for distance-based speaker segmentation is proposed. This distance measure is nonparametric, in contrast to common distance measures used in speaker segmentation systems, which often assume a Gaussian distribution when measuring the distance between two audio segments. This distance measure is essentially a k-nearest-neighbor distance measure. Non-vowel segment removal in preprocessing stage is also proposed. Speaker segmentation performance is tested on artificially created conversations from the TIMIT database and two AMI conversations. For short window lengths, Missed Detection Rated is decreased significantly. For moderate window lengths, a decrease in both... 

User-generated data in TripAdvisor.com consists of considerable amount of useful information that can help managers to provide better services to their customers. This study aims to forecast a new user's rate to a hotel based on information of the hotel and user. To do so, all reviews on all hotels of Tehran on TripAdvisor.com as real data are selected and 8 different supervised machine learning models are applied to the data to select the best method including K-nearest neighbors (KNN), Naïve Bayes, decision tree, logistic regression, support vector machine, neural network, random forest, and gradient boosting. KNN algorithm which uses similarity and distance measures for classification is... 

Data classification in large scale systems, such as peer-to-peer networks, can be very communication-expensive and impractical due to the huge amount of available data and lack of central control. Frequent data updates pose even more difficulties when applying existing classification techniques in peer-to-peer networks. We propose a distributed, scalable and robust classification algorithm based on k-nearest neighbor estimation. Our algorithm is asynchronous, considers data updates and imposes low communication overhead. The proposed method uses a content based overlay structure to organize data and moderate the number of query messages propagated in the network. Simulation results show that... 

Journal-bearings play a significant role in industrial applications and the necessity of condition monitoring with nondestructive tests is increasing. This paper deals a proper fault detection technique based on power spectral density (PSD) of vibration signals in combination with K-Nearest Neighbor and Support Vector Machine (SVM). The frequency domain vibration signals of an internal combustion engine with three journal-bearing conditions were gained, corresponding to, (i) normal, (ii) corrosion and (iii) excessive wear. The features of the PSD values of vibration signals were extracted using statistical and vibration parameters. The extracted features were used as inputs to the KNN and... 

New methods based on local spatio-temporal features have exhibited significant performance in action recognition. In these methods, feature selection plays an important role to achieve a superior performance. Actions are represented by local spatio-temporal features extracted from action videos. Action representations are then classified by applying a classifier (such as k-nearest neighbor or SVM). In this paper, we have proposed two feature weighting methods to better discriminate similar actions. We have proposed a definition of feature discrimination power to be used in the feature selection process. Our proposed weighting schemes have greatly improved the final categorization accuracy on... 

In this letter, we introduce a semi-supervised manifold regularization framework for human pose estimation. We utilize the unlabeled data to compensate for the complexities in the input space and model the underlying manifold by a nearest neighbor graph. We argue that the optimal graph is a subgraph of the k nearest neighbors (k-NN) graph. Then, we estimate distances in the output space to approximate this subgraph. In addition, we use the underlying manifold of the points in the output space to introduce a novel regularization term which captures the correlation among the output dimensions. The modified graph and the proposed regularization term are utilized for a smooth regression over... 

The rapid development of scientific fields in this modern era has raised the concern for prospective scholars to find a proper research field to conduct their future studies. Thus, having a vision of future could be helpful to pick the right path for doing research and ensuring that it is worth investing in. In this study, we use article keywords of computer science journals and conferences, assigned by INSPEC controlled indexing, to construct a temporal scientific knowledge network. By observing keyword networks snapshots over time, we can utilize the link prediction methods to foresee the future structures of these networks. We use two different approaches for this link prediction problem.... 

Lung cancer is one of the most common diseases for human beings everywhere throughout the world. Early identification of this disease is the main conceivable approach to enhance the possibility of patients’ survival. In this paper, a k-Nearest-Neighbors technique, for which a genetic algorithm is applied for the efficient feature selection to reduce the dataset dimensions and enhance the classifier pace, is employed for diagnosing the stage of patients’ disease. To improve the accuracy of the proposed algorithm, the best value for k is determined using an experimental procedure. The implementation of the proposed approach on a lung cancer database reveals 100% accuracy. This implies that one... 

A new approach for person identification based on hand geometry is presented. After preprocessing hand features are extracted from a photograph taken while user has placed his/her hand (either left or right) on the platform of a document scanner with no limits or fixation. Different pattern recognition techniques like Gaussian mixture modeling (GMM), Radial basis function neural networks (RBF), Multi layer perceptron (MLP), k-Nearest Neighbor (k-NN), Bayes method and mahalanobis/Hamming distance have been used in classification section. Experimental results show a rate of success above 90%. © 2005 IEEE  

Blind source separation (BSS) methods have received a great deal of attention in electroencephalogram (EEG) artifact elimination as they are routine and standard signal processing tools to remove artifacts and reserve desired neural information. On the other hand, a classifier should follow BSS methods to automatically identify artifactual sources and remove them in the following steps. In addition, removing all detected artifactual components leads to loss of information since some desired information related to neural activity leaks to these sources. So, an approach should be employed to detect and suppress the artifacts and reserve neural activity. This study introduces a novel method... 

In this paper, a robust algorithm for disease type determination in brain magnetic resonance image (MRI) is presented. The proposed method classifies MRI into normal or one of the seven different diseases. At first two-level two-dimensional discrete wavelet transform (2D DWT) of input image is calculated. Our analysis show that the wavelet coefficients of detail sub-bands can be modeled by generalized autoregressive conditional heteroscedasticity (GARCH) statistical model. The parameters of GARCH model are considered as the primary feature vector. After feature vector normalization, principal component analysis (PCA) and linear discriminant analysis (LDA) are used to extract the proper... 

A robust fingerprint recognition system based on marginal statistics of 2D wavelet transform is introduced which significantly improves the accuracy of previous wavelet based approaches due to 1) a better selection of features extracted from the wavelet transform, and 2) a more accurate distance measure to find the similarity between fingerprints. A combination of Jain and Poincare algorithms is employed to locate the fingerprint reference point. The main part of the fingerprint image is chosen as a rectangle with the reference point at its center. The image is then divided into nonoverlapping sub-images, the wavelet transform is applied to the bi-level sub-images, and Generalized Gaussian... 

A new face recognition method is proposed in this paper. The proposed method is based on fuzzy regularized linear discriminant analysis (FR-LDA) and combines the regularized linear discriminant analysis (R-LDA) and the fuzzy set theory. R-LDA is based on a new regularized Fisher's discriminant criterion, which is particularly robust against the small sample size problem compared to the traditional one used in LDA. In the proposed method, we calculate the membership degree matrix by Fuzzy K-nearest neighbor (FKNN) and then incorporate the membership degree into the definition of the between-class and within-class scatter matrices and get the fuzzy between-class and within-class scatter... 

In this paper, we introduce a novel method for human activity recognition that benefits from the structure and sequential properties of the test data as well as the training data. In the training phase, we obtain a fraction of data labels at constant time intervals and use them in a semi-supervised graph-based method for recognizing the user's activities. We use label propagation on a k-nearest neighbor graph to calculate the probability of association of the unlabeled data to each class in this phase. Then we use these probabilities to train an HMM in a way that each of its hidden states corresponds to one class of activity. These probabilities are used to learn the transition probabilities... 

The k-nearest neighbor (k-NN) is a widely used classification technique and has significant applications in various domains. The most challenging issues in the k-nearest neighbor algorithm are high dimensional data, the reasonable accuracy of results and suitable computation time. Nowadays, using parallel processing and deploying many-core platforms like GPUs is considered as one of the popular approaches to improving these issues. In this paper, we present a novel and accurate parallel implementation of k-NN based on Mahalanobis distance metric in GPU platform. We design and implement k-NN for GPU architecture and utilize mathematic and algorithmic techniques to eliminate repetitive... 

In this study, a new feature-based steganalytic method is presented and four classification methods: Fisher Linear Discriminant, Gaussian naïve Bayes, Multilayer perceptron, and k nearest neighbor, are compared for steganalysis of suspicious images. The method exploits statistics of the histogram, wavelet statistics, amplitudes of local extrema from the 1D and 2D adjacency histograms, center of mass of the histogram characteristic function and co-occurrence matrices for feature extraction process. In order to reduce the proposed features dimension and select the best subset, genetic algorithm is used and the results are compared through principle component analysis and linear discriminant... 

Herniation in the lumbar area is one of the most common diseases which results in lower back pain (LBP) causing discomfort and inconvenience in the patients' daily lives. A computer aided diagnosis (CAD) system can be of immense benefit as it generates diagnostic results within a short time while increasing precision of diagnosis and eliminating human errors. We have proposed a new method for automatic diagnosis of lumbar disc herniation based on clinical MRI data. We use T2-W sagittal and myelograph images. The presented method has been applied on 30 clinical cases, each containing 7 discs (210 lumbar discs) for the herniation diagnosis. We employ Otsu thresholding method to extract the... 

Background: Convolutional neural networks (CNN) have enabled significant progress in speech recognition, image classification, automotive software engineering, and neuroscience. This impressive progress is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data. Method: In this paper, we focus on the automated detection of autism spectrum disorder (ASD) using CNN with a brain imaging dataset. We detected ASD patients using most common resting-state functional magnetic resonance imaging (fMRI) data from a multi-site dataset named the Autism Brain Imaging Exchange (ABIDE). The proposed approach was able to classify ASD... 

The building sector is a major source of energy consumption and greenhouse gas emissions in urban regions. Several studies have explored energy consumption prediction, and the value of the knowledge extracted is directly related to the quality of the data used. The massive growth in the scale of data affects data quality and poses a challenge to traditional data mining methods, as these methods have difficulties coping with such large amounts of data. Expanded algorithms need to be utilized to improve prediction performance considering the ever-increasing large data sets. In this paper, a preprocessing method to remove noisy features is coupled with predication methods to improve the...