Results 131 to 140 of about 149,463 (272)
On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification
ABSTRACT Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor‐based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time‐series ...
Rishona Daniels +4 more
wiley +1 more source
Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen +4 more
wiley +1 more source
Graph Feature Refinement and Fusion in Transformer for Structural Damage Detection
Structural damage detection is of significance for maintaining the structural health. Currently, data-driven deep learning approaches have emerged as a highly promising research field.
Tianjie Hu, Kejian Ma, Jianchun Xiao
doaj +1 more source
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park +19 more
wiley +1 more source
Large language models are transforming microbiome research by enabling advanced sequence profiling, functional prediction, and association mining across complex datasets. They automate microbial classification and disease‐state recognition, improving cross‐study integration and clinical diagnostics.
Jieqi Xing +4 more
wiley +1 more source
A Relationship-Aware Feature Update Method for Enhanced Graph-Based Neural Networks
This paper presents a novel feature update method that leverages the relationships among batch elements, addressing scenarios both with and without an external graph.
Conggui Huang
doaj +1 more source
Automatic Determination of Quasicrystalline Patterns from Microscopy Images
This work introduces a user‐friendly machine learning tool to automatically extract and visualize quasicrystalline tiling patterns from atomically resolved microscopy images. It uses feature clustering, nearest‐neighbor analysis, and support vector machines. The method is broadly applicable to various quasicrystalline systems and is released as part of
Tano Kim Kender +2 more
wiley +1 more source
Cross-device fault diagnosis method based on graph convolution and multi-sensor fusion
ObjectiveTo address the problems of difficulty in obtaining labeled fault data for mechanical equipment and low diagnosis accuracy caused by different probability distributions of cross-device data in actual production, a cross-device fault diagnosis ...
SUN Yuanshuai +3 more
doaj +2 more sources
A novel convolutional neural network architecture enables rapid, unsupervised analysis of IR spectroscopic data from DRIFTS and IRRAS. By combining synthetic data generation with parallel convolutional layers and advanced regularization, the model accurately resolves spectral features of adsorbed CO, offering real‐time insights into ceria surface ...
Mehrdad Jalali +5 more
wiley +1 more source
Robust graph structure learning to improve multi-omics cancer subtype classification
Background Classifying cancer patients into consistent subtypes at the multi-omics level remains a significant challenge in advancing precision medicine.
Mengke Guo, Xiucai Ye, Tetsuya Sakurai
doaj +1 more source

