Spiculated Lesion Detection in Digital Mammogram Based on Artificial Neural Network Ensemble

2005 ◽  
pp. 790-795
Author(s):  
Ning Li ◽  
Huajie Zhou ◽  
Jinjiang Ling ◽  
Zhihua Zhou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Lesion Detection ◽  
Digital Mammogram ◽  
Neural Network Ensemble ◽  
Artificial Neural ◽  
Artificial Neural Network Ensemble

Artificial neural network ensemble-based land-cover classifiers using MODIS data

2009 ◽  
Vol 13 (2) ◽  
pp. 570-574 ◽  
Author(s):  
Takashi Yamaguchi ◽  
Kenneth J. Mackin ◽  
Eiji Nunohiro ◽  
Jong Geol Park ◽  
Keitaro Hara ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Land Cover ◽  
Neural Network Ensemble ◽  
Modis Data ◽  
Artificial Neural ◽  
Artificial Neural Network Ensemble

An artificial neural network ensemble to predict disposition and length of stay in children presenting with bronchiolitis

2004 ◽  
Vol 11 (5) ◽  
pp. 259-264 ◽  
Author(s):  
Paul Walsh ◽  
Padraig Cunningham ◽  
Stephen J. Rothenberg ◽  
Sinead O??Doherty ◽  
Hilary Hoey ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Length Of Stay ◽  
Neural Network Ensemble ◽  
Artificial Neural ◽  
Artificial Neural Network Ensemble

An evolutionary artificial neural network ensemble model for estimating hourly direct normal irradiances from meteosat imagery

Energy ◽  
2015 ◽  
Vol 91 ◽  
pp. 264-273 ◽  
Author(s):  
Alvaro Linares-Rodriguez ◽  
Samuel Quesada-Ruiz ◽  
David Pozo-Vazquez ◽  
Joaquin Tovar-Pescador
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ensemble Model ◽  
Neural Network Ensemble ◽  
Artificial Neural ◽  
Artificial Neural Network Ensemble

scholarly journals A novel time difference of arrival localization algorithm using a neural network ensemble model

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881579 ◽  
Author(s):  
Zhenkai Zhang ◽  
Feng Jiang ◽  
Boyuan Li ◽  
Bing Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mobile Station ◽  
Time Difference ◽  
Ensemble Model ◽  
Neural Network Ensemble ◽  
Time Difference Of Arrival ◽  
Generalization Ability ◽  
Artificial Neural ◽  
Artificial Neural Network Ensemble

In a localization system, time difference of arrival technique is widely used to estimate the location of a mobile station. To improve the performance of mobile station location estimation, a novel algorithm-based artificial neural network ensemble and time difference of arrival information is proposed in non-line-of-sight environments. Back propagation neural network is a classic artificial neural network and may be effectively used for mathematical modeling and prediction, and an artificial neural network ensemble has better generalization ability and stability than a single network. First, the parameters, such as the weights and biases of the single neural network are optimized by the ant lion optimization method which is novel and effective. Then four types of different information from the time difference of arrival measurements are respectively used to train the individual neural network. Finally, the weighted average method is improved to combine the outputs of the different individual neural network, where weights are determined by the training errors. The estimation accuracy of the locating system is evaluated through experimental measurements. The simulation results show that the proposed algorithm is efficient in improving the generalization ability and localization precision of the neural network ensemble model.

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