scholarly journals Optimization of a Depiction Procedure for an Artificial Intelligence-Based Network Protection System Using a Genetic Algorithm

2021 ◽  
Vol 11 (5) ◽  
pp. 2012
Author(s):  
Petr Dolezel ◽  
Filip Holik ◽  
Jan Merta ◽  
Dominik Stursa
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Network Traffic ◽  
Decision Procedure ◽  
Flow Characteristics ◽  
Incoming Flow ◽  
Remote Work ◽  
Network Protection ◽  
Efficient Data

The current demand for remote work, remote teaching and video conferencing has brought a surge not only in network traffic, but unfortunately, in the number of attacks as well. Having reliable, safe and secure functionality of various network services has never been more important. Another serious phenomenon that is apparent these days and that must not be discounted is the growing use of artificial intelligence techniques for carrying out network attacks. To combat these attacks, effective protection methods must also utilize artificial intelligence. Hence, we are introducing a specific neural network-based decision procedure that can be considered for application in any flow characteristic-based network-traffic-handling controller. This decision procedure is based on a convolutional neural network that processes the incoming flow characteristics and provides a decision; the procedure can be understood as a firewall rule. The main advantage of this decision procedure is its depiction process, which has the ability to transform the incoming flow characteristics into a graphical structure. Graphical structures are regarded as very efficient data structures for processing by convolutional neural networks. This article’s main contribution consists of the development and improvement of the depiction process using a genetic algorithm. The results presented at the end of the article show that the decision procedure using an optimized depiction process brings significant improvements in comparison to previous experiments.

Image Recognition Technology Based on Artificial Intelligence

2013 ◽  
Vol 347-350 ◽  
pp. 3537-3540
Author(s):  
Hai Yun Lin ◽  
Yu Jiao Wang ◽  
Jian Chun Cai
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Semantic Feature ◽  
Current Image ◽  
Semantic Feature Extraction

In respect of the classification of current image retrieval technology and the existing issues, the paper put forward a method designed for image semantic feature extraction based on artificial intelligence. The new method has solved the tough problem of image semantic feature extraction, by fusing fuzzy logic, genetic algorithm and artificial neural network altogether, which greatly improved the efficiency and accuracy of image retrieval.

scholarly journals Deriving Rules for Forecasting Air Carrier Financial Stress and Insolvency: A Genetic Algorithm Approach

2010 ◽  
Author(s):  
Sergio Davalos ◽  
Richard Gritta ◽  
Bahram Adrangi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Local Optima ◽  
Air Carrier ◽  
Genetic Algorithm Approach ◽  
Genetic Algorithm Model

Statistical and artificial intelligence methods have successfully classified organizational solvency, but are limited in terms of generalization, knowledge on how a conclusion was reached, convergence to a local optima, or inconsistent results. Issues such as dimensionality reduction and feature selection can also affect a model's performance. This research explores the use of the genetic algorithm that has the advantages of the artificial neural network but without its limitations. The genetic algorithm model resulted in a set of easy to understand, if-then rules that were used to assess U.S. air carrier solvency with a 94% accuracy.

scholarly journals Survey on computational intelligence based image encryption techniques

2020 ◽  
Vol 19 (3) ◽  
pp. 1428
Author(s):  
Thirumalaimuthu Ramanathan ◽  
Md. Jakir Hossen ◽  
Md. Shohel Sayeed ◽  
Joseph Emerson Raja
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Image Encryption ◽  
Computational Intelligence ◽  
Visual Cryptography ◽  
Machine Learning Algorithms ◽  
Artificial Intelligence Techniques ◽  
Cryptographic Keys

Image encryption is an important area in visual cryptography that helps in protecting images when shared through internet. There is lot of cryptography algorithms applied for many years in encrypting images. In the recent years, artificial intelligence techniques are combined with cryptography algorithms to support image encryption. Some of the benefits that artificial intelligence techniques can provide are prediction of possible attacks on cryptosystem using machine learning algorithms, generation of cryptographic keys using optimization algorithms, etc. Computational intelligence algorithms are popular in enhancing security for image encryption. The main computational intelligence algorithms used in image encryption are neural network, fuzzy logic and genetic algorithm. In this paper, a review is done on computational intelligence-based image encryption methods that have been proposed in the recent years and the comparison is made on those methods based on their performance on image encryption.

Application of Genetic Algorithm-Polynomial Neural Network for Modelling Heat Transfer and Fluid Flow Characteristics of a Double-Pipe Heat Exchanger

2013 ◽  
Author(s):  
Mehdi Mehrabi ◽  
Tuhid Pashaee ◽  
Mohsen Sharifpur ◽  
Josua P. Meyer
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Genetic Algorithm ◽  
Fluid Flow ◽  
Heat Exchanger ◽  
Flow Characteristics ◽  
Primary Data ◽  
Polynomial Neural Network ◽  
Fluid Flow Characteristics ◽  
Double Pipe

In this paper a genetic algorithm-polynomial neural network approach is used in order to model the effect of important parameters on heat transfer as well as fluid flow characteristics for a double-pipe helical heat exchanger by using numerical-certified results. In this way, overall heat transfer coefficient (Uo), inner and annular pressure drop (ΔPin, ΔPan) are modeled with respect to the variation of inner and annular dean number, inner and annular Prandtl number, and pitch of coil which are defined as input (design) variables. The numerical-certified data was randomly divided into test and train sections which the former is used for benchmark. The GA-PNN structure was instructed by 75 percent of the numerical-validated data. 25 percent of the primary data which had been considered for testing procedure were entered into GA-PNN proposed models and results were compared by statistical criteria.

scholarly journals Types of classifier in artificial intelligence

2015 ◽  
Vol 15 (1) ◽  
pp. 6436-6443
Author(s):  
Hadis Askarifard
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Supervised Classification ◽  
Machine Intelligence ◽  
Fuzzy Logics ◽  
Information Recovery ◽  
Correct Information

Artificial intelligence or machine intelligence should be considered as the vast domain of junction of many knowledge, sciences and old and new technics. Today, classification of documents is adopted extensively in information recovery for organizing documents. In the method of document supervised classification some correct information about documents that previously have been classified are available for us and based on these information we classify these documents. Thus, we will examine methods such as: expert systems, artificial neural network, Genetic algorithm and fuzzy logics and so on. In this project we examine documents thematically and then using existing algorithms we predict a theme for a new document.

Based on Artificial Neural Network Simulation of Alloy Finite Element

2013 ◽  
Vol 710 ◽  
pp. 739-742
Author(s):  
Shu Zhang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fem Analysis ◽  
Algorithm Optimization ◽  
Neural Network Simulation ◽  
Artificial Neurons ◽  
Artificial Neural

Artificial neural networks are composed of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. First modal analyses of microstructure defects are performed in ANSYS. Second the genetic algorithm is implemented in MATLAB to Calculate the Value of b and p. The last, The FEM analysis results are imported in ANSYS about the Stress distribution. The result presented in this paper is obtained using the Genetic Algorithm Optimization Toolbox.

Optimization of Micro Compressor Blades Using CFD Analysis and Artificial Intelligence

2003 ◽  
Author(s):  
Akay A. Islek
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Design Method ◽  
Navier Stokes ◽  
Cfd Analysis ◽  
Compressor Blades ◽  
Artificial Neural Network Ann ◽  
Robust Design Method

This paper describes a robust design method for turbo machinery components. The method which uses a Navier Stokes (NS) solver, an Artificial Neural Network (ANN) and a Genetic Algorithm (GA) is applied to optimize radial micro compressor blades. Higher efficiency, less divergence from the design mass flow and smoother Mach number distributions are considered as objectives of the optimization.

Sign in / Sign up

Export Citation Format

Share Document