Constrained Convolutional Neural Networks for Weakly Supervised Segmentation
Deepak Pathak+2 more
openalex +2 more sources
This review highlights advanced methods for detecting and managing over‐humidification in polymer electrolyte fuel cells (PEFCs), emphasizing innovative sensors, simulation techniques, and imaging methods. By addressing the impact of water management on fuel‐cell performance and durability, this study outlines practical and sustainable solutions for ...
Maximilian Käfer+2 more
wiley +1 more source
Taking a look at your speech: identifying diagnostic status and negative symptoms of psychosis using convolutional neural networks. [PDF]
Melshin G+5 more
europepmc +1 more source
Machine Learning for Organic Fluorescent Materials
Organic fluorescent materials (OFMs) have demonstrated significant potential in diverse applications. Conventional approaches for studying OFMs face significant limitations in fluorescence spectroscopy and computational methods. Machine learning (ML) has revolutionized materials chemistry, offering superior predictive accuracy and efficiency over ...
Jiamin Zhong+7 more
wiley +1 more source
Brain-guided convolutional neural networks reveal task-specific representations in scene processing. [PDF]
Hansen BC+5 more
europepmc +1 more source
Abstract Crystallization is pivotal in the chemical and pharmaceutical industry, affecting particle stability, and drug release. Crystal size distribution (CSD), a critical attribute of the final dosage form, is determined by the molecular structure of the crystallizing entity.
Silabrata Pahari+3 more
wiley +1 more source
An efficient method for early Alzheimer's disease detection based on MRI images using deep convolutional neural networks. [PDF]
Dardouri S.
europepmc +1 more source
Domain‐Aware Implicit Network for Arbitrary‐Scale Remote Sensing Image Super‐Resolution
Although existing arbitrary‐scale image super‐resolution methods are flexible to reconstruct images with arbitrary scales, the characteristic of training distribution is neglected that there exists domain shift between samples of various scales. In this work, a Domain‐Aware Implicit Network (DAIN) is proposed to handle it from the perspective of domain
Xiaoxuan Ren+6 more
wiley +1 more source
Multi-convolutional neural networks for cotton disease detection using synergistic deep learning paradigm. [PDF]
Aslam A+5 more
europepmc +1 more source
Machine‐Learning‐Based, Feature‐Rich Prediction of Alumina Microstructure from Hardness
Herein, high‐performance generative adversarial network (GAN), named ‘Microstructure‐GAN’, is demonstrated. After training, the high‐fidelity, feature‐rich micrographs can be predicted for an arbitrary target hardness. Microstructure details such as small pores and grain boundaries can be observed at the nanometer scale in the predicted 1000 ...
Xiao Geng+10 more
wiley +1 more source