… as random forests. In this paper, we show that a carefully designed neural network with
random forest … In fact, this architecture is more powerful than random forests, because the back-…

… , that is, random forest, extreme learning machine, probabilistic neural network and support
vector machine, is presented to find the most efficient one. Random forest has been proven …

… As in the brain, the basic processing elements of an artificial neural network are neurons (units
or nodes). In a neural network, units are placed as layers, and are connected in such a …

… as random forests. In this article, we show that a carefully designed neural network with
random forest … In fact, this architecture is more powerful than random forests, because the back-…

… been recently introduced as a general alternative to neural networks (NN), were investigated
[21… [25] demonstrated that using random forests/ferns with an appropriate node test reduces …

… developed classifier named Forest Deep Neural Network (fDNN), to integrate the deep …
forest can be simply built through bagging trees 20 . In this paper, we only employ random forests …

… In the following paper we are comparing the machine learning classifiers Random Forest
(RF) (Breiman, 2001), ArtificialNeural-Network (ANN) (Rosenblatt, 1958; Rumelhart et al., 1986)…

… had significantly higher classification accuracy than the random forest method for the … simple
random forest method can achieve good identification results, however the neural network …

… In this paper, artificial neural network (ANN) and random forest (RF) algorithms were utilized
to predict the ultimate axial bearing capacity of driven piles. An unprecedented database …

… The research significance of this study is to predict the mechanical strength of the geopolymer
concrete by the use of artificial neural networks and random forest algorithm machine …