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Fundamentals of neural networks
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The article was published on 1993-01-01 and is currently open access. It has received 1921 citations till now. The article focuses on the topics: Time delay neural network & Physical neural network.read more
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Proceedings Article
A symmetric key cryptography using genetic algorithm and error back propagation neural network
Vikas Sagar,Krishan Kumar +1 more
TL;DR: A new symmetric key algorithm based on genetic algorithm (GA) and error back propagation neural network (EBP-NN) is proposed for communication over the public computer networks.
Proceedings ArticleDOI
Dermatology diagnosis with feature selection methods and artificial neural network
TL;DR: This prototype improves expert diagnosis method in term of time efficiency and diagnosis accuracy and the findings show that FCBF method offers the shortest elapsed time and highest result compared to CFS method and the full features with an accuracy of 91.2%.
Journal ArticleDOI
Identification of crystalline structures using mossbauer parameters and artificial neural network
TL;DR: In this paper, Artificial Neural Networks (ANN) have been used to identify crystalline structures from plots of Mossbauer spectral parameters of I.S., Q.S. and polyhedral volume of a coordination site.
Journal ArticleDOI
Predicting the Shear Behavior of Cemented and Uncemented Carbonate Sands Using a Genetic Algorithm-Based Artificial Neural Network
TL;DR: In this article, a new approach is presented based on the integration of Genetic Algorithm (GA) into an Artificial Neural Network (ANN) to predict the shear behavior of carbonate sands.
Journal ArticleDOI
N4: A precise and highly sensitive promoter predictor using neural network fed by nearest neighbors
Amjad Askary,Ali Masoudi-Nejad,Roozbeh Sharafi,Amir Mizbani,Sobhan Naderi Parizi,Malihe Purmasjedi +5 more
TL;DR: A modified artificial neural network fed by nearest neighbors based on DNA duplex stability, named N4, is presented, which can predict the transcription start sites of Escherichia coli with sensitivity and precision both above 94%, better than most of the existed algorithms.