this paper is based on a combination of the principal component analysis (pca), eigenface and support vector machines. using n-fold method and with respect to the value of n, any person’s face images are divided into two sections. as a result, vectors of training features and test features are obtain ed. classification precision and accuracy was examined with three different types of kernel and...

ABSTRACT: The present research presents an analytical methodology based on High Performance Liquid Chromatography (HPLC) and Principal Component Analyses (PCA) for simultaneous quantification differentiation of organic acids sugars in commercial fruit juice samples (orange, grape, apple tangerine). In addition to the development method that generated suitable validation paramters quantitative a...

The fact that the Photoplethysmograph (PPG) signal caries respiratory information in addition to arterial blood oxygen saturation attracted the researchers to extract the respiratory information from it. In this current work, we present an efficient algorithm, based on the multi scale principal component analysis (MSPCA) technique to extract the respiratory activity from the PPG signals. MSPCA ...

The green organs, especially the leaves, of many Compositae plants possess characteristic aromas. To exploit the utility value of these germplasm resources, the constituents, mainly volatile compounds, in the leaves of 14 scented plant materials were qualitatively and quantitatively compared via gas chromatography-mass spectrometry (GC-MS). A total of 213 constituents were detected and tentativ...

Principal Component Analysis (PCA) is one of the most valuable results oriented techniques of applied linear algebra. The minimum effort of PCA provides a roadmap for reducing a complex data set to a lower dimension to reveal the sometimes hidden, simplified structure that often underlie it. Bioelectrical signals express the electrical functionality of different organs in the human body. The El...

The facial expression recognition is an ocular task that can be performed without human discomfort, is really a speedily growing on the computer research field. There are many applications and programs uses facial expression to evaluate human character, judgment, feelings, and viewpoint The process of rrecognizing facial expression is a hard task due to the several circumstances such as facial ...

A popular approach for dimensionality reduction and data analysis is principal component analysis (PCA). A limiting factor with PCA is that it does not inform us on which of the original features are important. There is a recent interest in sparse PCA (SPCA). By applying an L1 regularizer to PCA, a sparse transformation is achieved. However, true feature selection may not be achieved as non-spa...

p2x 7 antagonist activity for a set of 49 molecules of the p2x 7 receptor antagonists, derivatives of purine, was modeled with the aid of chemometric and artificial intelligence techniques. the activity of these compounds was estimated by means of combination of principal component analysis (pca), as a well-known data reduction method, genetic algorithm (ga), as a variable selection technique, ...

Nowadays we are living in the information age with the fast development of computational technologies and modern facilities. Larger data sets are produced by experiments and computer simulations. In contrast to conventional scientific approaches where simple models are built to fit the data, automated procedures are urged to obtain insights into the core messages carried by the large volume of ...

Side Channel Analysis (SCA) are of great concern since they have shown their efficiency in retrieving sensitive information from secure devices. In this paper we introduce First Principal Components Analysis (FPCA) which consists in evaluating the relevance of a partitioning using the projection on the first principal directions as a distinguisher. Indeed, FPCA is a novel application of the Pri...