Factors Affecting Retailer’s Adopti on of Mobile Payment Systems: A SEM-Neural Network Modeling Approach

2018 ◽  
Vol 103 (3) ◽  
pp. 2529-2551 ◽  
Author(s):  
Ali Nawaz Khan ◽  
Ahsan Ali
Keyword(s):  
Neural Network ◽  
Network Modeling ◽  
Mobile Payment ◽  
Neural Network Modeling ◽  
Payment Systems ◽  
Factors Affecting ◽  
Modeling Approach

A hybrid econometric—neural network modeling approach for sales forecasting

1996 ◽  
Vol 43 (2-3) ◽  
pp. 175-192 ◽  
Author(s):  
James T. Luxhøj ◽  
Jens O. Riis ◽  
Brian Stensballe
Keyword(s):  
Neural Network ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Modeling Approach

Mechanisms of developmental regression in autism and the broader phenotype: A neural network modeling approach.

2011 ◽  
Vol 118 (4) ◽  
pp. 637-654 ◽  
Author(s):  
Michael S. C. Thomas ◽  
Victoria C. P. Knowland ◽  
Annette Karmiloff-Smith
Keyword(s):  
Neural Network ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Modeling Approach ◽  
Developmental Regression ◽  
Broader Phenotype

Photocatalytic degradation of textile reactive dye using artificial neural network modeling approach

2015 ◽  
Vol 57 (30) ◽  
pp. 14132-14144 ◽  
Author(s):  
G.G. Lenzi ◽  
R.F. Evangelista ◽  
E.R. Duarte ◽  
L.M.S. Colpini ◽  
A.C. Fornari ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Photocatalytic Degradation ◽  
Network Modeling ◽  
Reactive Dye ◽  
Neural Network Modeling ◽  
Artificial Neural Network Modeling ◽  
Modeling Approach ◽  
Artificial Neural

scholarly journals Modeling Height–Diameter Relationship for Poplar Plantations Using Combined-Optimization Multiple Hidden Layer Back Propagation Neural Network

Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 442
Author(s):  
Jianbo Shen ◽  
Zongda Hu ◽  
Ram P. Sharma ◽  
Gongming Wang ◽  
Xiang Meng ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Transfer Functions ◽  
Network Modeling ◽  
Back Propagation ◽  
Mixed Effects ◽  
Neural Network Modeling ◽  
Neural Network Approach ◽  
Modeling Approach ◽  
Neural Network Structure

Relationship of total height and diameter at breast height (hereafter diameter) of the trees is generally nonlinear, and therefore has complex characteristics, which can be accurately described by the height-diameter model developed using the back propagation (BP) neural network approach. The multiple hidden layered-BP neural network has several hidden layers and neurons, and is therefore considered more appropriate modeling approach compared to the single hidden layered-BP neural network approach. However, the former approach is not widely applied for tree height prediction due to absence of the effective optimization method, but it can be done using the BP neural network modeling approach. The poplar (Populus spp. L.) plantation data acquired from Guangdong province of China were used for evaluating the BP neural network modeling approach and compared its results with those obtained from the traditional regression modeling and mixed-effects modeling approaches. We determined the best BP neural network structure with two hidden layers and five neurons in each layer, and logistic sigmoid transfer functions. Relative to the Mitscherlich height-diameter model that had the highest fitting precision among the six traditional height-diameter models evaluated, coefficient of determination (R2) of the neural network height-diameter model increased by 10.3%, root mean squares error (RMSE) and mean absolute error (MAE) decreased by 12% and 13.5%, respectively. The BP neural network height-diameter model also appeared more accurate than the mixed-effects height-diameter model. Our study proposes the method of determining the optimal numbers of hidden layers, neurons of each layer, and transfer functions in the BP neural network structure. This method can be useful for other modeling studies of similar or different types, such as tree crown modeling, height, and diameter increments modeling, and so on.

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