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Diffusion tensor imaging (DTI) has recently been added to several large-scale studies of Alzheimer's disease (AD), such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), to investigate white matter (WM) abnormalities not detectable on standard anatomical MRI. Disease effects can be widespread, and the profile of WM abnormalities across tracts is still not fully understood. Here we analyzed...
In this paper, we propose an automated Euler's time-step adjustment scheme for diffeomorphic image registration using stationary velocity fields (SVFs). The proposed variational problem aims at bounding the inverse consistency error by adaptively adjusting the number of Euler's step required to realize the time integration. This particular formulation allows us to gain computationally since only relevant...
Statistical shape models generally characterize shape variations linearly by principal component analysis (PCA), which assumes that the non-rigid shape parameters are drawn from a Gaussian distribution. This practical assumption is often not valid. Instead, we propose a constrained local model based on independent component analysis (ICA) and use kernel density estimation (KDE) for non-parametrically...
Topological signal processing, especially persistent homology, is a growing field of study for analyzing sets of data points that has been heretofore applied to unlabeled data. In this work, we consider the case of labeled data and examine the topology of the decision boundary separating different labeled classes. Specifically, we propose a novel approach to construct simplicial complexes of decision...
In this paper, we propose an accurate approximation framework for separable edge-preserving filtering. Naïve implementation of edge-preserving filtering, such as bilateral filtering and non-local means filtering, consumes enormous computational costs. Separable implementation of such filters is an efficient approximation method for real-time filtering. The accuracy of the conventional separable representation,...
Constructing accurate models that represent the underlying structure of Big Data is a costly process that usually constitutes a compromise between computation time and model accuracy. Methods addressing these issues often employ parallelisation to handle processing. Many of these methods target the Support Vector Machine (SVM) and provide a significant speed up over batch approaches. However, the...
In this paper, we consider multi-sensor classification when there is a large number of unlabeled samples. The problem is formulated under the multi-view learning framework and a Consensus-based Multi-View Maximum Entropy Discrimination (CMV-MED) algorithm is proposed. By iteratively maximizing the stochastic agreement between multiple classifiers on the unlabeled dataset, the algorithm simultaneously...
This paper studies a new method for identifying the new words, Objective to identify new words better. Method is first to extract the positive and negative samples from training corpus which was handled by segmentation and POS Tagging according to the dictionary, then combining with all kinds of words classification which was gotten from training corpus, and gaining the new word support vector through...
Many indoor localisation systems based on existent radio communication networks use the received signal strength (RSS) as measured feature. The accuracy of such systems is directly related to the amount of labelled data, gathered during a calibration phase. This paper presents a new algorithm based on previous works from the same authors, where an explicit calibration phase is avoided applying unsupervised...
Intrusion detection system (IDS) is of paramount importance in the present network and system security. Intrusion detection can successfully prevent many attempts to crash network and hamper web services by intruders and hackers.
Over recent years, the world has experienced a huge growth in the volume of shared web texts. Its users generate daily a huge volume of comments and reviews related to different aspects of their lives. In general, opinion mining/sentiment analysis refers to the task of identifying positive and negative opinions, emotions and evaluations related to an article, news, products, services, etc [1]. Arabic...
Video surveillance systems require both accurate and efficient operations for biometric classification tasks. Recent research has shown that modelling video data on manifold space leads to significant improvement on classification accuracy. However, processing manifold points directly often requires computationally expensive operations since manifolds are non-Euclidean. In this work, we tackle this...
This paper introduces a newly developed automatic classification system for wedge tightness inside the generator by applying support vector machine (SVM) classifier. The automatic classifying system for wedge tightness of the generator consists of 4 parts including data collection, preprocessing, feature extraction, and classification. Machine learning algorithm called SVM is used with the linear...
With the rapid maturity of internet and web technology over the last decades, the number of Indonesian online news articles is growing rapidly on the web at a pace we never experienced before. In this paper, we introduce a combination of rule-based and machine learning approach to find the sentences that have tropical disease information in them, such as the incidence date and the number of casualty,...
Introduction: The ECG Bayesian filtering framework has been shown to be a promising method to extract the foetal electrocardiogram (FECG) from abdominal recordings. This framework requires an estimation of the ECG morphology, which is obtained by approximating an average beat with a number of Gaussian kernels. This approximation results in a high dimensional nonlinear optimization problem (finding...
The diagnosis of the arythema disease is a real difficulty in dermatology. It causes redness induced in the lower level of the skin by hyperemia of the capillaries. It can harm several skin damages, inflammations. In this paper, we have put our efforts to design a diagnostic approach based on Support Vector Machine (SVM) with linear kernel by classifying the erythemato-squamous disease. SVM being...
The behavior of many physical and biological processes and systems can be described satisfactorily by fractional order models. A new method, termed fractional linear prediction (FLP) based on fractional calculus, is used to model ictal and seizure-free EEG signals. Through numerical simulations it is demonstrated that, the EEG signal can be modeled accurately, by using a few integrals of fractional...
In this paper, we develop a novel framework for action recognition in videos. The framework is based on automatically learning the discriminative trajectory groups that are relevant to an action. Different from previous approaches, our method does not require complex computation for graph matching or complex latent models to localize the parts. We model a video as a structured bag of trajectory groups...
In this paper, we present multiple parallelized support vector machines (MPSVMs), which aims to deal with the situation when multiple SVMs are required to be performed concurrently. The proposed MPSVM is based on an optimization procedure for nonnegative quadratic programming (NQP), called multiplicative updates. By using graphical processing units (GPUs) to parallelize the numerical procedure of...
This paper addresses the problem of adaptive chemical detection, using the Receptor Density Algorithm (RDA), an immune inspired anomaly detection algorithm. Our approach is to first detect when and if something has changed in the environment and then adapt the RDA to this change. Statistical hypothesis testing is used to determine whether there has been concept drift in consecutive time windows of...
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