Results 151 to 160 of about 851,900 (211)

A spatial distribution - Principal component analysis (SD-PCA) model to assess pollution of heavy metals in soil.

Science of the Total Environment, 2022
With the rapid development of urbanization, heavy metal pollution of soil has received great attention. Over-enrichment of heavy metals in soil may endanger human health.
Jiawei Liu, Hou-qing Kang, Wendong Tao, Hanyu Li, D. He, Lixia Ma, Haojie Tang, Siqi Wu, Kexin Yang, Xuxiang Li   +9 more
semanticscholar   +1 more source

PCA-Pruner: Filter pruning by principal component analysis

Journal of Intelligent & Fuzzy Systems, 2022
Deep Convolutional Neural Networks (CNNs) have been widely used in various domains due to their outstanding performance. However, they simultaneously bring enormous computational overhead, making it difficult to deploy to mobile and edge devices. Therefore, researchers use network compression techniques such as quantization, knowledge distillation and ...
Zhang, Wei, Wang, Zhiming
openaire   +1 more source

Denoising Aggregation of Graph Neural Networks by Using Principal Component Analysis

IEEE Transactions on Industrial Informatics, 2023
To avoid the overfitting phenomenon that appeared in performing graph neural networks (GNNs) on test examples, the feature encoding scheme of GNNs usually introduces the dropout procedure.
Wei Dong, M. Woźniak, Junsheng Wu, Weigang Li, Zongwen Bai   +4 more
semanticscholar   +1 more source

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)
openaire   +1 more source

A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

, 2021
Big databases are increasingly widespread and are therefore hard to understand, in exploratory biomedicine science, big data in health research is highly exciting because data-based analyses can travel quicker than hypothesis-based research.
Basna Mohammed Salih Hasan, A. Abdulazeez   +1 more
semanticscholar   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

Megavariate analysis of environmental QSAR data. Part I – A basic framework founded on principal component analysis (PCA), partial least squares (PLS), and statistical molecular design (SMD)

Molecular diversity, 2006
L. Eriksson, P. Andersson, E. Johansson, M. Tysklind   +3 more
semanticscholar   +1 more source

Identification of PM sources by principal component analysis (PCA) coupled with wind direction data.

Chemosphere, 2006
M. Viana, X. Querol, A. Alastuey, J. Gil, M. Menéndez   +4 more
semanticscholar   +1 more source