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In recent years, data-driven approaches have been developed to predict Alzheimer's Disease. The goal of our report is to identify the factors that are most associated with memory loss. Data from ADNI (Alzheimer's Disease Neuro-imaging Initiative) with 8 variables from socioeconomic factors and biomarkers are analyzed, and will be used to propose a predictive model for the onset of AD. On the mathematical...
Steady State Visual Evoked Potential (SSVEP) has been commonly adopted in Brain Computer Interface (BCI) applications. For wearable BCI applications, several aspects of SSVEP-based BCI systems, such as speed, subject variability, and accurate target detection, are under ongoing research investigations. Up to date, Canonical Correlation Analysis (CCA) has been considered the state-of-the-art feature...
Canonical correlation analysis (CCA) has been used for concurrent quality and process monitoring to extract multidimensional correlation structure between process and quality variables. In this paper, a new kernel concurrent CCA (KCCCA) algorithm is proposed for quality-relevant nonlinear process monitoring, which decomposes the original space into five subspaces, including correlation subspace, quality-principal...
The objective of this paper is to address a new method based on trend extraction for isolating faults in the nonstationary and nonlinear processes. Firstly, a concise review of the traditional methods for fault isolation based on Hotelling statistic are introduced, a rigorous analysis of their weaknesses, especially the smearing (coupling) phenomena, is provided, and the possible handling strategies...
Several methods have been explained for blind source separation (BSS) in the literature. Those methods fail when considered for separation of speech signals. This paper mainly focuses on blind speech signal separation from the observations using canonical correlation. The performance of the proposed method is evaluated in terms of signal to interference ratio (SIR) and time domain waveforms of separated...
With progress in the area of computer science, it is achievable to read, process, store and generate information out of the available data. Humongous amount of data is generated, which is of mixed type, including time-series, Boolean, spatial-temporal and alpha-numeric data. This data is generated at a very giant speed and volume, which makes difficult for the traditional clustering algorithms to...
Commercial seaports are a key component in the transport chain and particularly in the supply chain and in the production process. Port performance is a recurring question as each change in ports affects their performance level. These changes occur at different levels: international (technological advances), national (division of traffic between different regions) and local (land use planning). This...
Traumatic brain injury (TBI) and its complications, including intracranial hypertension, are one of the leading causes of mortality. Many proposed algorithms have attempted to overcome the invasiveness of intracranial pressure monitoring with limited clinical applications. In medical practices, changes of intracranial hypertension are perceived manually, by clinical experts, via surgical placement...
More and more medical data are shared, which leads to disclosure of personal privacy information. Therefore, the construction of medical data privacy preserving publishing model is of great value: not only to make a non-correspondence between the released information and personal identity, but also to maintain the data utility after anonymity. However, there is an inherent contradiction between the...
Complex industrial processes are often non-linear and non-Gaussian, while the traditional principal component analysis (PCA) method assumes that the data are Gaussian and linear. In this paper, a novel process monitoring method based on maximum information coefficient-PCA (MIC-PCA) and support vector data description (SVDD) is proposed. First, the covariance matrix is replaced by the MIC matrix which...
Hyperspectral images(HSIs) provide hundreds of narrow spectral bands for the land-covers, thus can provide more powerful discriminative information for the land-cover classification. However, HSIs suffer from the curse of high dimensionality, therefore dimension reduction and feature extraction are essential for the application of HSIs. In this paper, we propose an unsupervised feature extraction...
Unlike dimensionality reduction (DR) tools for single-view data, e.g., principal component analysis (PCA), canonical correlation analysis (CCA) and generalized CCA (GCCA) are able to integrate information from multiple feature spaces of data. This is critical in multi-modal data fusion and analytics, where samples from a single view may not be enough for meaningful DR. In this work, we focus on a...
Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise...
We address the problem of determining the number of signals correlated between two high-dimensional data sets with small sample support. In this setting, conventional techniques based on canonical correlation analysis (CCA) cannot be directly applied since the canonical correlations are significantly overestimated when computed from few samples. To overcome this problem, a principal component analysis...
The success of many joint blind source separation techniques is dependent upon accurate estimation of the common signal subspace order across multiple datasets. This has stimulated the development of techniques to estimate the number of common signals across two datasets, in particular, a method that uses information theoretic criteria using the canonical correlation coefficients in the likelihood...
The effort required for the development of a software system is predicted through the cost of software estimation. Completion of project within time and budget limits is required for accurate cost estimation. Effort and cost estimation can be done through various modes. A new hybrid algorithm which is a combination of concepts of Artificial Bee Colony (ABC) and Local search procedures is used here...
This paper review an important of the student loan consideration criteria by using factor analysis method. The processes include five steps determining factors that influence the consideration; Kaiser-Meyer-Olkin Measure of Sampling Adequacy and Bartlet's test of sphericity, Principal components analysis (PCA), rotation, evaluation, and interpretation. The dataset is a historical data of student loan...
In order to better understand what structural and functional brain components changes are associated with schizophrenia, various investigations have been conducted. Functional Network Connectivity (FNC) generally interpreted as an indirect measure of brain activity, measures the functional component, and Structural Based Morphometry (SBM), an indirect measure of concentration of Gray Matter (GM),...
Timber Purchasing Managers' Index (PMI) has been perceived as the barometer of timber industry evolution. The weight scheme is at the core of constructing the index. In this study, an in-depth analysis on the weight scheme of Timber PMI is conducted. The empirical results reveal that the current weight does not affect the leading function of Timber PMI, but it is not the optimal weighting method....
Parkinson's disease is a pathology that involves characteristic perturbations in patients' voices. This paper describes a proposed method that aims to diagnose persons with Parkinson (PWP) by analyzing on line their voices signals. First, Thresholds signals alterations are determined by the Multi-Dimensional Voice Program (MDVP). Principal Analysis (PCA) is exploited to select the main voice principal...
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