Results 121 to 130 of about 86,959 (305)

Experimental Characterization of Mycelium‐Based Composites Under Multiple Loading Conditions

Advanced Engineering Materials, EarlyView.
This study examines the mechanical response of mycelium‐based composites under compression, shear, and tension using mechanical testing and imaging methods. The comparison between unpressed and hot‐pressed specimens shows that hot pressing is associated with higher compression and shear stiffnesses.
Shaghayegh Elahi, Kimiya Tajik, Harvey Shaw, Matti Isakov, Sami Pajunen, Lauri Rautkari   +5 more
wiley   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Calibrating Random Forests

2008 Seventh International Conference on Machine Learning and Applications, 2008
When using the output of classifiers to calculate the expected utility of different alternatives in decision situations, the correctness of predicted class probabilities may be of crucial importance. However, even very accurate classifiers may output class probabilities of rather poor quality.
openaire   +3 more sources

Random Relational Rules

, 2006
Exhaustive search in relational learning is generally infeasible, therefore some form of heuristic search is usually employed, such as in FOIL[1]. On the other hand, so-called stochastic discrimination provides a framework for combining arbitrary numbers
Pfahringer, Bernhard, Anderson, Grant
core  

Random forests classification.

, 2016
Random forests classification.
Wei Li (7081), Jun Wu (4002), Maogui Hu (319021), Lianfa Li (1922281), Douglas Houston (2502763)   +4 more
core   +1 more source

DNA‐Templated 2D Heterostructures as Phototriggered Dynamic Nanohybrids: From Releasing Molecular Loads to Controlling Enzyme Biocatalytic Function

Advanced Functional Materials, EarlyView.
DNA strands are employed both as dynamic linkers and nanoscale templates for the integration of Ag2S nanoparticles on MoS2, which in turn imparted photothermal responsiveness; this feature permits the selective cargo (fluorophore, quantum dots or an enzyme) release from the MoS2 surface in response to local heat induced by light irradiation.
Kai Chen, Haosen Miao, Weiying Hong, Matteo Palma   +3 more
wiley   +1 more source

Random Uniform Forests

, 2015
Random Uniform Forests are a variant of Breiman's Random Forests (tm) (Breiman, 2001) and Extremely randomized trees (Geurts et al., 2006). Random Uniform Forests are designed for classification, regression and unsupervised learning.
Ciss, Saïp
core   +1 more source

Baker’s Cyst Classification Using Random Forests [PDF]

Annals of computer science and information systems, 2018
Adam Ciszkiewicz, Grzegorz Milewski, Jacek Lorkowski   +2 more
doaj   +1 more source

Biodiversity shapes tree species aggregations in tropical forests

, 2008
Spatial patterns of conspecific trees are considered as the consequences of biological interactions and environmental influences. They also reflect species interactions in plant communities.
Chang-Fu Hsieh, Wei-Chun Chao, Shan-Huah Wu   +2 more
core  

Variance reduction in purely random forests

, 2012
International audienceRandom forests, introduced by Leo Breiman in 2001, are a very effective statistical method. The complex mechanism of the method makes theoretical analysis difficult.
Genuer, Robin
core   +1 more source