Results 31 to 40 of about 4,393,923 (290)

Toward More Efficient WMSN Data Search Combined FJLT Dimension Expansion With PCA Dimension Reduction

IEEE Access, 2020
With the rapid development of 5G technology, the scales and dimensions of the data that are processed by Wireless Multimedia Sensor Network (WMSN) applications will be larger than ever before.
Chenkai Xiao, Wenhao Shao, Ruliang Xiao
doaj   +1 more source

On dimension reduction in Gaussian filters [PDF]

Inverse Problems, 2016
A priori dimension reduction is a widely adopted technique for reducing the computational complexity of stationary inverse problems. In this setting, the solution of an inverse problem is parameterized by a low-dimensional basis that is often obtained from the truncated Karhunen-Loeve expansion of the prior distribution.
Hakkarainen, Janne, Solonen, Antti, Cui, Tiangang, Marzouk, Youssef M   +3 more
openaire   +5 more sources

PLS dimension reduction for classification of microarray data [PDF]

, 2004
PLS dimension reduction is known to give good prediction accuracy in the context of classification with high-dimensional microarray data. In this paper, PLS is compared with some of the best state-of-the-art classification methods.
Boulesteix, Anne-Laure
core   +1 more source

Improving Wind Power Forecasts: Combination through Multivariate Dimension Reduction Techniques

Energies, 2021
Wind energy and wind power forecast errors have a direct impact on operational decision problems involved in the integration of this form of energy into the electricity system.
Marta Poncela-Blanco, Pilar Poncela
doaj   +1 more source

Multiple phenotype association tests based on sliced inverse regression

BMC Bioinformatics
Background Joint analysis of multiple phenotypes in studies of biological systems such as Genome-Wide Association Studies is critical to revealing the functional interactions between various traits and genetic variants, but growth of data in ...
Wenyuan Sun, Kyongson Jon, Wensheng Zhu, the Alzheimer’s Disease Neuroimaging Initiative   +3 more
doaj   +1 more source

Cauchy-Lanczos Algorithm for Effective Dimension Reduction

IEEE Access
The aim of dimension reduction techniques is to eliminate unnecessary information from extensive datasets, thereby enhancing the effectiveness of data analysis.
Xuansheng Wang, Linzhong Xia, Changwei Lv   +2 more
doaj   +1 more source

Central Nervous System Tumors Among Infants in Canada: A Report From CYP‐C

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Central nervous system (CNS) tumors in infants are rare, pose unique clinical challenges, and lack large‐scale evidence‐based data to guide management. This study seeks to describe CNS tumors in Canadian infants and to compare their outcomes with those of older children.
Samuel Sassine, Hallie Coltin, Sylvia Cheng, Lucie Lafay‐Cousin, Vanan Issai Magimairajan, Craig Erker, Stéphanie Vairy, Lynette Bowes, Adam Fleming, Donna Johnston, Sarah McKillop, Roona Sinha, Julie Bennett, Christina Coleman, Laura Wheaton, Shayna Zelcer, Samuele Renzi, Thai Hoa Tran   +17 more
wiley   +1 more source

Dimension reduction using global and local pattern information-based maximum margin criterion

, 2016
Dimension reduction is an important research area in pattern recognition when dealing with high-dimensional data. In this paper, a novel supervised dimension reduction approach is introduced for classification. Advantages of using not only global pattern
Sakarya, Ufuk
core   +1 more source

Impact of Radiation Therapy on Physical and Psychosocial Health of Adolescents and Young Adults: A Joint Report From the Children's Oncology Group AYA and Radiation Oncology Committees

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Rates of cancer among adolescents and young adults (AYA), age 15–39 years, are increasing. Consequently, radiation oncologists are treating more AYAs who have diagnoses spanning both pediatric and adult practices. Compared to pediatric and older adult patients, AYAs face a unique set of challenges.
Hesham Elhalawani, Rachel K. Peterson, Avani D. Rao, Laurie J. McKenzie, Ahmed Magdy Elmotayam, Leslie Chang, Michael Roth, Susan L. McGovern   +7 more
wiley   +1 more source

A Review on Nature-Inspired Algorithms for Cancer Disease Prediction and Classification

Mathematics, 2023
In the era of healthcare and its related research fields, the dimensionality problem of high-dimensional data is a massive challenge as it is crucial to identify significant genes while conducting research on diseases like cancer.
Abrar Yaqoob, Rabia Musheer Aziz, Navneet Kumar Verma, Praveen Lalwani, Akshara Makrariya, Pavan Kumar   +5 more
doaj   +1 more source