Overfitting of artificial-neural-network-based nonlinear equalizer for multilevel signals in optical communication systems

2020 ◽  
Author(s):  
Kai Ikuta ◽  
Yuta Otsuka ◽  
Moriya Nakamura
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optical Communication ◽  
Communication Systems ◽  
Optical Communication Systems ◽  
Artificial Neural

scholarly journals Model and method for diagnosing computer incidents in information and communication systems based on deep machine learning

2019 ◽  
pp. 32-42
Author(s):  
Albert Malikov ◽  
Vladimir Avramenko ◽  
Igor Saenko
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Information Security ◽  
Communication System ◽  
Communication Systems ◽  
Security Breach ◽  
Main Requirement ◽  
Information And Communication ◽  
Artificial Neural ◽  
Hidden Layer

Introduction: Models and methods for diagnosing computer incidents recorded in information and communication systems are the most important components in mathematical support of information security systems. The main requirement for the diagnostics is prompt identification of security violation characteristics. This problem is complicated due to the amount and variability of the initial data on information security violation. Purpose: Development of a model for diagnosing a computer incident, along with a method which would allow you to quickly determine the characteristics of a security violation. Results: Security breach characteristics important for making a decision about responding to an identified computer incident can be determined via deep artificial neural networks. A structural feature of the proposed deep artificial neural network is combining the coding part of the autoencoder and a multilayer perceptron. In addition, the method implements a parallel mode of processing information events which have occurred in the information and communication system before the incident was detected, by using a separate proposed artificial neural network for each secondary characteristic of the security breach. The method of determining the values of these secondary characteristics allows you to greatly improve the diagnostics efficiency, having acceptable values of precision and recall for the security violation characteristics to determine. The dependence has been studied of the completeness and classification accuracy on the number of neurons in the hidden layer. A sufficient number of neurons in the hidden layer for achieving the required training efficiency is experimentally determined. Practical relevance: The developed model and method can be implemented using standard software and hardware (servers) of an information and communication system. Their combined use with the existing models and methods of monitoring and diagnostics can significantly improve the efficiency of an information security system.

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
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