Results 141 to 150 of about 95,260 (290)
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
The Doctrines of the Great Educators [PDF]
A. V. Judges, Robert R. Rusk
openalex +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
Quadrotor unmanned aerial vehicle control is critical to maintain flight safety and efficiency, especially when facing external disturbances and model uncertainties. This article presents a robust reinforcement learning control scheme to deal with these challenges.
Yu Cai +3 more
wiley +1 more source
The model leverages patient time‐space information for pattern feature representations. Encoders extract first and second‐order features, aggregated with categorical embeddings and dense features. Task‐specific and shared experts use gated networks, with a dispatch layer routing information for diabetes risk evaluation and blood glucose prediction ...
Yingshuai Wang +8 more
wiley +1 more source
Art. XV. Appréciation de la Doctrine Phrénologique ou des localisations des Facultés Intellectuelles et morales, au Moyen de lʼAnatomic Comparée. [PDF]
Jules M. Lafargue
openalex +1 more source

