USE OF GENETIC ALGORITHM IN DEEP NEURAL NETWORKS CONFIGURATION FOR THE PURPOSES OF COMPUTER ATTACKS CLASSIFICATION

The task of multiclass network classification of computer attacks is given. The applicability of deep neural network technology in problem solving has been considered. Deep neural network architecture was chosen based on the strategy of combining a set of convolution and recurrence LSTM layers. Op-timization of neural network parameters based on genetic algorithm is proposed. The presented results of modeling show the possibility of solving the network classification problem in real time.

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Optimizing Deep Neural Network Architecture with Enhanced Genetic Algorithm

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) ◽

10.1109/icmla.2019.00222 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ajay Shrestha ◽

Ausif Mahmood

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Network Architecture ◽

Deep Neural Network ◽

Neural Network Architecture

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Deep Neural Network Architecture for Low-Dimensional Embedding and Classification of Cosmic Ray Images Obtained from CMOS Cameras

10.1007/978-3-030-92307-5_36 ◽

2021 ◽

pp. 307-316

Author(s):

Tomasz Hachaj ◽

Marcin Piekarczyk ◽

Łukasz Bibrzycki

Keyword(s):

Neural Network ◽

Network Architecture ◽

Deep Neural Network ◽

Cosmic Ray ◽

Neural Network Architecture ◽

Low Dimensional

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Classification of lexical stress patterns using deep neural network architecture

2014 IEEE Spoken Language Technology Workshop (SLT) ◽

10.1109/slt.2014.7078621 ◽

2014 ◽

Cited By ~ 4

Author(s):

Mostafa Ali Shahin ◽

Beena Ahmed ◽

Kirrie J. Ballard

Keyword(s):

Neural Network ◽

Network Architecture ◽

Deep Neural Network ◽

Lexical Stress ◽

Neural Network Architecture ◽

Stress Patterns

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Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

Journal of Fluid Mechanics ◽

10.1017/jfm.2016.615 ◽

2016 ◽

Vol 807 ◽

pp. 155-166 ◽

Cited By ~ 274

Author(s):

Julia Ling ◽

Andrew Kurzawski ◽

Jeremy Templeton

Keyword(s):

Neural Network ◽

Neural Networks ◽

Reynolds Stress ◽

Network Architecture ◽

Eddy Viscosity ◽

Deep Neural Networks ◽

Test Cases ◽

Neural Network Architecture ◽

Stress Anisotropy ◽

Anisotropy Tensor

There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data. A novel neural network architecture is proposed which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropy tensor. It is demonstrated that this neural network architecture provides improved prediction accuracy compared with a generic neural network architecture that does not embed this invariance property. The Reynolds stress anisotropy predictions of this invariant neural network are propagated through to the velocity field for two test cases. For both test cases, significant improvement versus baseline RANS linear eddy viscosity and nonlinear eddy viscosity models is demonstrated.

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Inclusion of Multiple Cycling of the Potential into Deep Neural Network Classification of Voltammetric Reaction Mechanisms

Faraday Discussions ◽

10.1039/d1fd00050k ◽

2021 ◽

Author(s):

Luke Gundry ◽

Gareth Kennedy ◽

Alan Bond ◽

Jie Zhang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Reaction Mechanisms ◽

Deep Neural Network ◽

Deep Neural Networks ◽

Neural Network Classification ◽

Initial Cycle

The use of Deep Neural Networks (DNNs) for the classification of electrochemical mechanisms based on training with simulations of the initial cycle of potential have been reported. In this paper,...

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