An artificial neural network model for serial speech production and speech error simulation

2013 ◽  
Vol 134 (5) ◽  
pp. 4166-4166
Author(s):  
Erin Rusaw
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Speech Production ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Speech Error ◽  
Artificial Neural ◽  
Error Simulation

Artificial Neural Network Model for Prediction of Latent Heat Flux over Bay of Bengal

2019 ◽  
Vol 7 (5) ◽  
pp. 901-905
Author(s):  
Kanchan. V. Shende ◽  
Vishal S. Shirsat ◽  
M. R. Ramesh Kumar ◽  
K.V. Kale
Keyword(s):  
Neural Network ◽  
Heat Flux ◽  
Artificial Neural Network ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Network Model ◽  
Neural Network Model ◽  
Bay Of Bengal ◽  
Artificial Neural Network Model ◽  
Artificial Neural

scholarly journals A feedforward artificial neural network model for classification and detection of type 2 diabetes

2021 ◽  
Vol 1734 ◽  
pp. 012026
Author(s):  
Enoch A. Frimpong ◽  
Ariyo Oluwasanmi ◽  
Edward Y. Baagyere ◽  
Qin Zhiguang
Keyword(s):  
Neural Network ◽  
Type 2 Diabetes ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Artificial Neural

scholarly journals Using Artificial Neural Networks to Predict Influences of Heterogeneity on Rock Strength at Different Strain Rates

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3042
Author(s):  
Sheng Jiang ◽  
Mansour Sharafisafa ◽  
Luming Shen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Strain Rate ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Rock Strength ◽  
Strain Rates ◽  
Filling Material ◽  
Artificial Neural

Pre-existing cracks and associated filling materials cause the significant heterogeneity of natural rocks and rock masses. The induced heterogeneity changes the rock properties. This paper targets the gap in the existing literature regarding the adopting of artificial neural network approaches to efficiently and accurately predict the influences of heterogeneity on the strength of 3D-printed rocks at different strain rates. Herein, rock heterogeneity is reflected by different pre-existing crack and filling material configurations, quantitatively defined by the crack number, initial crack orientation with loading axis, crack tip distance, and crack offset distance. The artificial neural network model can be trained, validated, and tested by finite 42 quasi-static and 42 dynamic Brazilian disc experimental tests to establish the relationship between the rock strength and heterogeneous parameters at different strain rates. The artificial neural network architecture, including the hidden layer number and transfer functions, is optimized by the corresponding parametric study. Once trained, the proposed artificial neural network model generates an excellent prediction accuracy for influences of high dimensional heterogeneous parameters and strain rate on rock strength. The sensitivity analysis indicates that strain rate is the most important physical quantity affecting the strength of heterogeneous rock.

Artificial neural network model for photosynthetic pigments identification using multi wavelength chromatographic data

2016 ◽  
Author(s):  
K. R. Prilianti ◽  
S. Hariyanto ◽  
F. D. D. Natali ◽  
Indriatmoko ◽  
M. A. S. Adhiwibawa ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Photosynthetic Pigments ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Chromatographic Data ◽  
Multi Wavelength ◽  
Artificial Neural

Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer

2016 ◽  
Vol 55 (8) ◽  
pp. 1239-1248 ◽  
Author(s):  
Xiaoran Duan ◽  
Yongli Yang ◽  
Shanjuan Tan ◽  
Sihua Wang ◽  
Xiaolei Feng ◽  
...  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Artificial Neural ◽  
Diagnosis Of Lung Cancer
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