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Publikováno v:
Heliyon, Vol 8, Iss 12, Pp e11847- (2022)
The present study developed Multiple Linear Regression (MLR) and machine learning (ML) models, including Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF), to predict the mean free-flow speed (FFS) using several ge
Externí odkaz:
https://doaj.org/article/d704574e47114f8b93a1a1cc3ac62870
N-hidden layer artificial neural network architecture computer code: geophysical application example
Publikováno v:
Heliyon, Vol 6, Iss 6, Pp e04108- (2020)
We provide a MATLAB computer code for training artificial neural network (ANN) with N+1 layer (N-hidden layer) architecture. Currently, the ANN application to solving geophysical problems have been confined to the 2-layer, i.e. 1-hidden layer, archit
Externí odkaz:
https://doaj.org/article/a8687255c7c0497eaa01505286c37e21
Autor:
Pooja Rani, G.S. Mahapatra
Publikováno v:
Heliyon, Vol 5, Iss 7, Pp e02082- (2019)
This paper presents software fault detection, which is dependent upon the effectiveness of the testing and debugging team. A more skilled testing team can achieve higher rates of debugging success, and thereby removing a larger fraction of faults ide
Externí odkaz:
https://doaj.org/article/af2342b7e01647698e2225f4235a4b20