Simulation of structural features on mechanochemical synthesis of Al2O3–TiB2 nanocomposite by optimized artificial neural network

2012 ◽  
Vol 23 (2) ◽  
pp. 220-227 ◽  
Author(s):  
Ali Ghafari Nazari ◽  
Masoud Mozafari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mechanochemical Synthesis ◽  
Structural Features ◽  
Artificial Neural

scholarly journals Visible and Near-Infrared Multispectral Features in Conjunction with Artificial Neural Network and Partial Least Squares for Predicting Biochemical and Micro-Structural Features of Beef Muscles

Foods ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1254
Author(s):  
Abderrahmane Aït-Kaddour ◽  
Donato Andueza ◽  
Annabelle Dubost ◽  
Jean-Michel Roger ◽  
Jean-François Hocquette ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Structural Features ◽  
Ann Model ◽  
Practical Applications ◽  
Artificial Neural

The objective of this study was to determine the potential of multispectral imaging (MSI) data recorded in the visible and near infrared electromagnetic regions to predict the structural features of intramuscular connective tissue, the proportion of intramuscular fat (IMF), and some characteristic parameters of muscle fibers involved in beef sensory quality. In order to do this, samples from three muscles (Longissimus thoracis, Semimembranosus and Biceps femoris) of animals belonging to three breeds (Aberdeen Angus, Limousine, and Blonde d’Aquitaine) were used (120 samples). After the acquisition of images by MSI and segmentation of their morphological parameters, a back propagation artificial neural network (ANN) model coupled with partial least squares was applied to predict the muscular parameters cited above. The results presented a high accuracy and are promising (R2 test > 0.90) for practical applications. For example, considering the prediction of IMF, the regression model giving the best ANN model exhibited R2P = 0.99 and RMSEP = 0.103 g × 100 g−1 DM.

scholarly journals Machine learning-based structure-property predictions in silica aerogels

Soft Matter ◽  
2021 ◽  
Author(s):  
Rasul Abdusalamov ◽  
Prakul Pandit ◽  
Barbara Milow ◽  
Mikhail Itskov ◽  
Ameya Rege
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Silica Aerogels ◽  
Structural Features ◽  
Structure Property ◽  
Cluster Aggregation ◽  
Diffusion Limited ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The structural features in silica aerogels are known to be modelled effectively by the diffusion-limited cluster-cluster aggregation (DLCA) approach. In this paper, an artificial neural network (ANN) is developed for...

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document