Results 81 to 90 of about 3,655 (256)
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 +3 more
wiley +1 more source
Enhancing Performance of Evolutionary Strategies with Symmetric Sampling (Furthermore, Weight Decay)
Evolutionary Strategies (ESs) are optimization metaheuristics largely adopted in Evolutionary Computation (EC). Since their introduction in the early 70s, researchers in the field have attempted to improve the efficacy of these algorithms.
Paolo Pagliuca
doaj +1 more source
The search for reliable and efficient global optimization algorithms for solving phase stability and phase equilibrium problems in applied thermodynamics is an ongoing area of research.
Seif-Eddeen K. Fateen +1 more
doaj +1 more source
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 +5 more
wiley +1 more source
Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source
Complex optimization problems, especially those encountered in real-life scenarios, pose significant challenges due to their multifaceted nature and the involvement of numerous variables.
Ravneil Nand +2 more
doaj +1 more source
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia +2 more
wiley +1 more source
A laser pointer‐guided robotic grasping method for arbitrary objects based on promptable segment anything model and force‐closure analysis is presented. Grasp generation methods based on force‐closure analysis can calculate the optimal grasps for objects through their appearances. However, the limited visual perception ability makes robots difficult to
Yan Liu +5 more
wiley +1 more source
Fuzzy Echo State Network-Based Fault Diagnosis of Remote-Controlled Robotic Arms
This paper presents a novel fault diagnosis technique for remote-controlled robotic arm systems, utilizing deep fuzzy echo state networks (DFESNs) and applies the covariance matrix adaptation evolution strategy (CMA-ES) to optimize the hyperparameters of
Shurong Peng +4 more
doaj +1 more source
It is a fact that slippage causes tracking errors in both longitudinal and lateral directions which results to have less travel distance in tracking a reference trajectory. Less travel distance means having energy loss of the battery and carrying loads less than planned.
Gokhan Bayar +2 more
wiley +1 more source

