Establishing structure–property correlations and classification of base oils using statistical techniques and artificial neural networks

2004 ◽  
Vol 506 (1) ◽  
pp. 57-69 ◽  
Author(s):  
G.S Kapur ◽  
M.I.S Sastry ◽  
A.K Jaiswal ◽  
A.S Sarpal
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Techniques ◽  
Structure Property ◽  
Base Oils ◽  
Artificial Neural ◽  
Property Correlations

Classification of ground-truth fire debris samples using artificial neural networks

2021 ◽  
Vol 23 ◽  
pp. 100313
Author(s):  
Nicholas A. Thurn ◽  
Taylor Wood ◽  
Mary R. Williams ◽  
Michael E. Sigman
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ground Truth ◽  
Fire Debris ◽  
Artificial Neural

scholarly journals Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules

Pharmaceutics ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1101
Author(s):  
Elena M. Tosca ◽  
Roberta Bartolucci ◽  
Paolo Magni
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Molecular Descriptor ◽  
Biological Properties ◽  
Training Dataset ◽  
Structure Property ◽  
Feed Forward Neural Network ◽  
Qspr Model ◽  
Artificial Neural ◽  
Intrinsic Solubility

Machine learning (ML) approaches are receiving increasing attention from pharmaceutical companies and regulatory agencies, given their ability to mine knowledge from available data. In drug discovery, for example, they are employed in quantitative structure–property relationship (QSPR) models to predict biological properties from the chemical structure of a drug molecule. In this paper, following the Second Solubility Challenge (SC-2), a QSPR model based on artificial neural networks (ANNs) was built to predict the intrinsic solubility (logS0) of the 100-compound low-variance tight set and the 32-compound high-variance loose set provided by SC-2 as test datasets. First, a training dataset of 270 drug-like molecules with logS0 value experimentally determined was gathered from the literature. Then, a standard three-layer feed-forward neural network was defined by using 10 ChemGPS physico-chemical descriptors as input features. The developed ANN showed adequate predictive performances on both of the SC-2 test datasets. Benefits and limitations of ML approaches have been highlighted and discussed, starting from this case-study. The main findings confirmed that ML approaches are an attractive and promising tool to predict logS0; however, many aspects, such as data quality, molecular descriptor computation and selection, and assessment of applicability domain, are crucial but often neglected, and should be carefully considered to improve predictions based on ML.

Easy classification of traditional Minas cheeses using artificial neural networks and discriminant analysis

2017 ◽  
Vol 70 (4) ◽  
pp. 492-498 ◽  
Author(s):  
Leandro S Santos ◽  
Roberta M D Cardozo ◽  
Natália Moreiria Nunes ◽  
Andréia B Inácio ◽  
Ana Clarissa dos S Pires ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Artificial Neural

Evaluation of artificial neural networks in the classification of primary oesophageal dysmotility

2006 ◽  
Vol 41 (3) ◽  
pp. 257-263 ◽  
Author(s):  
Robespierre Santos ◽  
Horst G. Haack ◽  
Des Maddalena ◽  
Ross D. Hansen ◽  
John E. Kellow
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural ◽  
Oesophageal Dysmotility

scholarly journals The Application of Artificial Neural Networks in Predicting Children’s Giftedness

2016 ◽  
Vol 19 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Nina Pavlin-Bernardić ◽  
◽  
Silvija Ravić ◽  
Ivan Pavao Matić ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
School Readiness ◽  
Multilayer Perceptron ◽  
Gifted Students ◽  
Test Sample ◽  
Hyperbolic Tangent ◽  
Readiness Assessment ◽  
Artificial Neural

Artificial neural networks have a wide use in the prediction and classification of different variables, but their application in the area of educational psychology is still relatively rare. The aim of this study was to examine the accuracy of artificial neural networks in predicting students’ general giftedness. The participants were 221 fourth grade students from one Croatian elementary school. The input variables for artificial neural networks were teachers’ and peers’ nominations, school grades, earlier school readiness assessment and parents’ education. The output variable was the result on the Standard Progressive Matrices (Raven, 1994), according to which students were classified as gifted or non-gifted. We tested two artificial neural networks’ algorithms: multilayer perceptron and radial basis function. Within each algorithm, a number of different types of activation functions were tested. 80% of the sample was used for training the network and the remaining 20% to test the network. For a criterion according to which students were classified as gifted if their result on the Standard Progressive Matrices was in the 95th centile or above, the best model was obtained by the hyperbolic tangent multilayer perceptron, which had a high accuracy of 100% of correctly classified non-gifted students and 75% correctly classified gifted students in the test sample. When the criterion was the 90th centile or above, the best model was also obtained by the hyperbolic tangent multilayer perceptron, but the accuracy was lower: 94.7% in the classification of non-gifted students and 66.7% in the classification of gifted students. The study has shown artificial neural networks’ potential in this area, which should be further explored. Keywords: gifted students, identification of gifted students, artificial neural networks

Classification of ASAS multiangle and multispectral measurements using artificial neural networks

1996 ◽  
Vol 57 (2) ◽  
pp. 79-87 ◽  
Author(s):  
Abdelgadir A. Abuelgasim ◽  
Sucharita Gopal ◽  
James R. Irons ◽  
Alan H. Strahler
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural
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