Results 291 to 300 of about 767,862 (371)
Jalaiah Effect: A Story of a Stolen Dance on TikTok and Trans-Platformization of Ignorance
Mariam Betlemidze
openalex +1 more source
Feature selection combined with machine learning and high‐throughput experimentation enables efficient handling of high‐dimensional datasets in emerging photovoltaics. This approach accelerates material discovery, improves process optimization, and strengthens stability prediction, while overcoming challenges in data quality and model scalability to ...
Jiyun Zhang +5 more
wiley +1 more source
Slippery Knowledge: Ignorance, Ecologies, and Environment in Endometriosis Framing. [PDF]
Ford A.
europepmc +1 more source
This study introduces an affordable machine learning platform for simultaneous dengue and zika detection using fluorine‐doped tin oxide thin films modified with gold nanoparticles and DNA aptamers. Designed for low‐cost, hardware‐limited devices (< $25), the model achieves 95.3% accuracy and uses only 9.4 kB of RAM, demonstrating viability for resource‐
Marina Ribeiro Batistuti Sawazaki +3 more
wiley +1 more source
Learning (Not) to Know: Examining How White Ignorance Manifests and Functions in White Adolescents' Racial Identity Narratives. [PDF]
Dull BD, Rogers LO, Ross J.
europepmc +1 more source
Tiesiskā ignorance kā tiesiskās apziņas deformācijas veids un tās samazināšanas iespējas
Ilze Cercene
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To integrate surface analysis into materials discovery workflows, Gaussian process regression is used to accurately predict surface compositions from rapidly acquired volume composition data (obtained by energy‐dispersive X‐ray spectroscopy), drastically reducing the number of required surface measurements on thin‐film materials libraries.
Felix Thelen +2 more
wiley +1 more source
Bonobos point more for ignorant than knowledgeable social partners. [PDF]
Townrow LA, Krupenye C.
europepmc +1 more source
A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai +8 more
wiley +1 more source

