scholarly journals Classification of drug‐induced hERG potassium‐channel block from electrocardiographic T‐wave features using artificial neural networks

2019 ◽  
Vol 24 (6) ◽  
Author(s):  
Micaela Morettini ◽  
Chiara Peroni ◽  
Agnese Sbrollini ◽  
Ilaria Marcantoni ◽  
Laura Burattini
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Potassium Channel ◽  
Channel Block ◽  
Drug Induced ◽  
T Wave ◽  
Artificial Neural

scholarly journals Hepatotoxicity Modeling Using Counter-Propagation Artificial Neural Networks: Handling an Imbalanced Classification Problem

Molecules ◽  
2020 ◽  
Vol 25 (3) ◽  
pp. 481 ◽  
Author(s):  
Benjamin Bajželj ◽  
Viktor Drgan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Liver Injury ◽  
In Vivo Studies ◽  
Training Procedure ◽  
Imbalanced Dataset ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Artificial Neural

Drug-induced liver injury is a major concern in the drug development process. Expensive and time-consuming in vitro and in vivo studies do not reflect the complexity of the phenomenon. Complementary to wet lab methods are in silico approaches, which present a cost-efficient method for toxicity prediction. The aim of our study was to explore the capabilities of counter-propagation artificial neural networks (CPANNs) for the classification of an imbalanced dataset related to idiosyncratic drug-induced liver injury and to develop a model for prediction of the hepatotoxic potential of drugs. Genetic algorithm optimization of CPANN models was used to build models for the classification of drugs into hepatotoxic and non-hepatotoxic class using molecular descriptors. For the classification of an imbalanced dataset, we modified the classical CPANN training algorithm by integrating random subsampling into the training procedure of CPANN to improve the classification ability of CPANN. According to the number of models accepted by internal validation and according to the prediction statistics on the external set, we concluded that using an imbalanced set with balanced subsampling in each learning epoch is a better approach compared to using a fixed balanced set in the case of the counter-propagation artificial neural network learning methodology.

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

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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