understanding the performance and role of each formation in a petroleum play is crucial for the efficient and precise exploration and exploitation of trapped hydrocarbons in a sedimentary basin. the lorestan basin is one of the most important hydrocarbon basins of iran, and it includes various oil-prone potential source rocks and reservoir rocks. previous geochemical studies of the basin were n...

We present a learning algorithmic approach to the problem of recognzing the file types of file fragments, with the purpose of applying this to “file carving”, the reconstruction of partially erased files on disk into whole files. We do so through the use of 257 calculated features of an input fragment, applying the Support Vector Machine, Multinomial Naive Bayes, and Linear Discriminant Analysi...

This paper deals with the recognition of off-line handwritten Devnagari characters. Here two sets of feature are computed and two classifiers are combined to get higher accuracy of Devnagari character recognition. Dimension of the features vector of each set is 392. First feature set is computed based on the directional information obtained from the arc tangent of the gradient. Since most of th...

This study presents our entry in this year’s Computers in Cardiology challenge. The challenge is the automated assessment of the electrocardiogram for predicting the onset of paroxysmal atrial fibrillation/flutter (PAF). By considering a large set of features derived from RR intervals, P wave shape and frequency representations of the P wave we compared the performance of a linear discriminant ...

Processing high dimensional features is the key of documents analysis and text classification. Traditional technologies for selecting or extracting rely heavily on the distribution of term features in the set of documents. It generally needs high computation cost to find the significant features. In this paper, we propose a new feature reduction method based on the analysis of discriminant coef...

Many problems in paleontology reduce to finding those features that best discriminate among a set of classes. A clear example is the classification of new specimens. However, these classifications are generally challenging because the number of discriminant features and the number of samples are limited. This has been the fate of LB1, a new specimen found in the Liang Bua Cave of Flores. Severa...

This paper proposes a novel multispectral feature extraction method according to the idea of canonical correlation analysis (CCA). Instead of extracting two groups of features with the same pattern (modality) as usual, the work explores another type of application of CCA that for extracting most correlated features from different face modalities to form effective discriminant vectors for recogn...

Kernel Fisher discriminant analysis (KFD) is an effective method to extract nonlinear discriminant features of input data using the kernel trick. However, conventional KFD algorithms endure the kernel selection problem as well as the singular problem. In order to overcome these limitations, a novel nonlinear feature extraction method called adaptive quasiconformal kernel Fisher discriminant ana...

This paper presents a Devnagari Numerical recognition method based on statistical discriminant functions. 17 geometric features based on pixel connectivity, lines, line directions, holes, image area, perimeter, eccentricity, solidity, orientation etc. are used for representing the numerals. Five discriminant functions viz. Linear, Quadratic, Diaglinear, Diagquadratic and Mahalanobis distance ar...