Results 191 to 200 of about 108,134 (338)

Machine Learning‐Enhanced Random Matrix Theory Design for Human Immunodeficiency Virus Vaccine Development

Advanced Intelligent Discovery, EarlyView.
This study integrates random matrix theory (RMT) and principal component analysis (PCA) to improve the identification of correlated regions in HIV protein sequences for vaccine design. PCA validation enhances the reliability of RMT‐derived correlations, particularly in small‐sample, high‐dimensional datasets, enabling more accurate detection of ...
Mariyam Siddiqah, Muhammad Zulqarnain Zeb, Muhammad Husnain Zeb, Babar Shabbir   +3 more
wiley   +1 more source

Multipath Credibility Selection for Robust UWB Angle-of-Arrival Estimation in Narrow Underground Corridors. [PDF]

Sensors (Basel)
Li J, Yu B, Cui S, Yang M, Zhao J, Su R, Tian R.   +6 more
europepmc   +1 more source

Advancing Efficient Error Reduction in DNA Data Storage Systems with Deep Learning‐Based Denoising Models

Advanced Intelligent Discovery, EarlyView.
Deep learning‐based denoising models are applied to DNA data storage systems to enhance error reduction and data fidelity. By integrating DnCNN with DNA sequence encoding methods, the study demonstrates significant improvements in image quality and correction of substitution errors, revealing a promising path toward robust and efficient DNA‐based ...
Seongjun Seo, Anshula Tandon, Nguyen Kim Uyen, Quoc Nam Le, Jee‐Hyong Lee, Sung Ha Park   +5 more
wiley   +1 more source

Independent Increments and Group Sequential Tests. [PDF]

Stat Med
Tsiatis AA, Davidian M.
europepmc   +1 more source

Admissible Estimator of the Eigenvalues of Variance-Covariance Matrix for Multivariate Normal Distributions--Detailed Proof-- [PDF]

, 2009
Yo Sheena, Akimichi Takemura
openalex   +1 more source

Cross-Validated Loss-Based Covariance Matrix Estimator Selection in High Dimensions

, 2022
Philippe Boileau, Nima S. Hejazi, Mark J. van der Laan, Sandrine Dudoit   +3 more
openalex   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

On robust spectrum sensing using M-estimators of covariance matrix

, 2020
Zhedong Liu, Abla Kammoun, Mohamed‐Slim Alouini   +2 more
openalex   +2 more sources

Admissible estimator of the eigenvalues of the variance–covariance matrix for multivariate normal distributions [PDF]

, 2011
Yo Sheena, Akimichi Takemura
openalex   +1 more source

RAMS: Residual‐Based Adversarial‐Gradient Moving Sample Method for Scientific Machine Learning in Solving Partial Differential Equations

Advanced Intelligent Discovery, EarlyView.
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang, Min Zhu, Wei Xiong, Si‐Wei Liu, Lu Lu   +4 more
wiley   +1 more source