Results 231 to 240 of about 560,645 (271)

Image authenticity implementing Principal Component Analysis (PCA)

2013 10th International Conference and Expo on Emerging Technologies for a Smarter World (CEWIT), 2013
The paper addresses the application of finding key features within an image utilizing the process termed the Principal Components Analysis (PCA). Understanding this technique is critical for researchers within biometric fields and the larger cyber security field. Research, found in ASEE 2011 Conference Proceedings, titled “Edge Detectors in Engineering
Suzanna Schmeelk, John Schmeelk
openaire   +1 more source

Principal Component Analysis (PCA) in Ichnology

2021
I observed the ichnofabric variable on 600 ichnofabric units in the Samarinda area of Kutai Basin. Five ichnofabric variables are bioturbation index (BI), biodiversity (ID), number of behaviors (NB), penetration depth (PD), and burrow diameter (DM) that perform as a semi-quantitative form. It must process the data with principal component analysis (PCA)
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Principal Component Analysis (PCA)

2019
Principal component analysis (PCA) is an essential algorithm in machine learning. It is a mathematical method for evaluating the principal components of a dataset. The principal components are a set of vectors in high-dimensional space that capture the variance (i.e., spread) or variability of the feature space.
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Identification of the isomers using principal component analysis (PCA) method

AIP Conference Proceedings, 2016
In this work, we have carried out a detailed statistical analysis for experimental data of mass spectra from xylene isomers. Principle Component Analysis (PCA) was used to identify the isomers which cannot be distinguished using conventional statistical methods for interpretation of their mass spectra.
Kepceoglu, Abdullah, Gundogdu, Yasemin, Ledingham, Kenneth William David, Kilic, Hamdi Sukur   +3 more
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The use of principal component analysis (PCA) to characterize beef

Meat Science, 2000
Principal component analysis was performed to study the relationships between chemical, physical and sensory variables (n=18) measured on longissimus thoracis et lumborum of 79 young bulls from the following ethnic groups: hypertrophied Piemontese, normal Piemontese, Friesian, crossbred hypertrophied Piemontese×Friesian, Belgian Blue and White.
DESTEFANIS, Gianluigi, BARGE, Maria Teresa, BRUGIAPAGLIA, Alberto, TASSONE, Sonia   +3 more
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Single Class Discrimination Using Principal Component Analysis (SCD‐PCA)

Quantitative Structure-Activity Relationships, 1991
AbstractSingle Class Discrimination using Principal Component Analysis (SCD‐PCA) has been developed to discriminate an embedded data class. The embedded class is defined as the active class and the diffuse class, or classes as the inactives. Two basic methods are described.
Valerie S. Rose, John Wood, Halliday J. H. Macfie   +2 more
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Principal Component Analysis

ACM Computing Surveys, 2022
Cesar H Comin, Filipi N Silva
exaly  

Investigation of correlation between chemical composition and properties of biodiesel using principal component analysis (PCA) and artificial neural network (ANN)

Renewable Energy, 2021
Rezwanul Haque, Wijitha Senadeera, Teuku Meurah Indra Mahlia   +2 more
exaly  

Principal Component Analysis (PCA).

La Tunisie medicale, 2022
Kamel, Ben Salem, Ahmed, Ben Abdelaziz
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Principal Component Analysis (PCA)

2013
Daniel V. Guebel, Néstor V. Torres
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