A SELF-ADAPTABLE DISTRIBUTED CBR VERSION OF THE EQUIVOX SYSTEM

Three dimensional (3D) voxel phantoms are numerical representations of human bodies, used by physicians in very different contexts. In the controlled context of hospitals, where from 2 to 10 subjects may arrive per day, phantoms are used to verify computations before therapeutic exposure to radiation of cancerous tumors. In addition, 3D phantoms are used to diagnose the gravity of accidental exposure to radiation. In such cases, there may be from 10 to more than 1000 subjects to be diagnosed simultaneously. In all of these cases, computation accuracy depends on a single such representation. In this paper, we present EquiVox which is a tool composed of several distributed functions and enables to create, as quickly and as accurately as possible, 3D numerical phantoms that fit anyone, whatever the context. It is based on a multi-agent system. Agents are convenient for this kind of structure, they can interact together and they may have individual capacities. In EquiVox, the phantoms adaptation is a key phase based on artificial neural network (ANN) interpolations. Thus, ANNs must be trained regularly in order to take into account newly capitalized subjects and to increase interpolation accuracy. However, ANN training is a time-consuming process. Consequently, we have built Equivox to optimize this process. Thus, in this paper, we present our architecture, based on agents and ANN, and we put the stress on the adaptation module. We propose, next, some experimentations in order to show the efficiency of the EquiVox architecture.

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A novel Artificial Neural Network training method combined with Quantum Computational Multi-Agent System theory

International Journal of Intelligent Systems Technologies and Applications ◽

10.1504/ijista.2009.022687 ◽

2009 ◽

Vol 6 (1/2) ◽

pp. 50

Author(s):

Xiangping Meng ◽

Jianzhong Wang ◽

Yuzhen Pi ◽

Quande Yuan

Keyword(s):

Neural Network ◽

System Theory ◽

Artificial Neural Network ◽

Multi Agent System ◽

Training Method ◽

Neural Network Training ◽

Agent System ◽

Network Training ◽

Multi Agent ◽

Artificial Neural Network Training

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Artificial neural network for solving multi-parameter optimization problems

Journal of Physics Conference Series ◽

10.1088/1742-6596/2092/1/012013 ◽

2021 ◽

Vol 2092 (1) ◽

pp. 012013

Author(s):

Krivorotko Olga ◽

Liu Shuang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Optimization Problems ◽

Three Dimensional ◽

Test Functions ◽

Backpropagation Method ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Quality Of Approximation ◽

And Function

Abstract An artificial neural network (ANN) is a mathematical or computational model that simulates the structure and function of biological neural networks used to evaluate or approximate functions at given points. After developing the training algorithm, the resulting model will be used to solve image recognition problems, control problems, optimization, etc. In the process of ANN training, the algorithm of backpropagation is used in the case of convex optimization functions. The article is analyzed test functions for experiments and also study the effect of the number of ANN layers on the quality of approximation in cases one-, two- and three-dimensional. The backpropagation method is improved during the experiments with the help of adaptive gradient, as a result of which more accurate approximations of the functions are obtained. This article also presents the numerical results of test functions.

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A System to Detect and Block SQL Injection with the help of Multi Agent System using Artificial Neural Network

International Journal of Computer Applications ◽

10.5120/12411-9073 ◽

2013 ◽

Vol 71 (12) ◽

pp. 21-26

Author(s):

Rohit Garde ◽

D R Anekar ◽

Niraj Kulkarni ◽

Mayur Ghadge

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Multi Agent System ◽

Agent System ◽

Sql Injection ◽

Multi Agent ◽

Artificial Neural

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An Artificial Neural Network Based Solution Scheme for Periodic Computational Homogenization of Electrostatic Problems

Mathematical and Computational Applications ◽

10.3390/mca24020040 ◽

2019 ◽

Vol 24 (2) ◽

pp. 40 ◽

Cited By ~ 2

Author(s):

Felix Selim Göküzüm ◽

Lu Trong Khiem Nguyen ◽

Marc-André Keip

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Trial Function ◽

A Priori ◽

Three Dimensional ◽

Internal Parameters ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Core Idea ◽

Electrostatic Problems

The present work addresses a solution algorithm for homogenization problems based on an artificial neural network (ANN) discretization. The core idea is the construction of trial functions through ANNs that fulfill a priori the periodic boundary conditions of the microscopic problem. A global potential serves as an objective function, which by construction of the trial function can be optimized without constraints. The aim of the new approach is to reduce the number of unknowns as ANNs are able to fit complicated functions with a relatively small number of internal parameters. We investigate the viability of the scheme on the basis of one-, two- and three-dimensional microstructure problems. Further, global and piecewise-defined approaches for constructing the trial function are discussed and compared to finite element (FE) and fast Fourier transform (FFT) based simulations.

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Artificial neural network (ANN) approach for synthesis and optimization of (3D) three-dimensional periodic phased array antenna

2016 17th International Symposium on Antenna Technology and Applied Electromagnetics (ANTEM) ◽

10.1109/antem.2016.7550118 ◽

2016 ◽

Cited By ~ 3

Author(s):

Hamdi Bilel ◽

Limam Selma ◽

Aguili Taoufik

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Phased Array ◽

Three Dimensional ◽

Array Antenna ◽

Phased Array Antenna ◽

Artificial Neural ◽

Artificial Neural Network Ann

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An artificial neural network based dynamic controller for a robot in a multi-agent system

Neurocomputing ◽

10.1016/j.neucom.2009.08.011 ◽

2009 ◽

Vol 73 (1-3) ◽

pp. 283-294 ◽

Cited By ~ 13

Author(s):

K.G. Jolly ◽

R. Sreerama Kumar ◽

R. Vijayakumar

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Multi Agent System ◽

Agent System ◽

Dynamic Controller ◽

Multi Agent ◽

Artificial Neural

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EQUIVOX: AN EXAMPLE OF ADAPTATION USING AN ARTIFICIAL NEURAL NETWORK ON A CASE-BASED REASONING PLATFORM

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237213500270 ◽

2013 ◽

Vol 25 (02) ◽

pp. 1350027 ◽

Cited By ~ 2

Author(s):

Julien Henriet ◽

Brigitte Chebel-Morello ◽

Michel Salomon ◽

Jad Farah ◽

Rémy Laurent ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Emergency Situation ◽

Case Based Reasoning ◽

Radiological Emergency ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

The One ◽

Numerical Representations ◽

Case Based

In case of a radiological emergency situation involving accidental human exposure, a dosimetry evaluation must be established as soon as possible. In most cases, this evaluation is based on numerical representations and models of victims. Unfortunately, personalized and realistic human representations are often unavailable for the exposed subjects. However, accuracy of treatment depends on the similarity of the phantom to the victim. The EquiVox platform (Research of Equivalent Voxel phantom) developed in this study uses case-based reasoning (CBR) principles to retrieve and adapt, from among a set of existing phantoms, the one to represent the victim. This paper introduces the EquiVox platform and the artificial neural network (ANN) developed to interpolate the victim's 3D lung contours. The results obtained for the choice and construction of the contours are presented and discussed.

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Using an artificial neural network for the evaluation of the parameters controlling PVA/chitosan electrospun nanofibers diameter

e-Polymers ◽

10.1515/epoly-2014-0198 ◽

2015 ◽

Vol 15 (2) ◽

pp. 127-138 ◽

Cited By ~ 29

Author(s):

Mohammad Ali Karimi ◽

Pouran Pourhakkak ◽

Mahdi Adabi ◽

Saman Firoozi ◽

Mohsen Adabi ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mean Squared Error ◽

Three Dimensional ◽

Base Material ◽

Electrospun Nanofibers ◽

Poly Vinyl Alcohol ◽

Nanofiber Diameter ◽

Artificial Neural ◽

Artificial Neural Network Ann

AbstractThe purpose of this study was to investigate the validity of an artificial neural network (ANN) method in the prediction of nanofiber diameter to assess the parameters involved in controlling fiber form and thickness. A mixture of polymers including poly(vinyl alcohol) (PVA) and chitosan (CS) at different ratios was chosen as the nanofiber base material. The various samples of nanofibers were fabricated as training and testing datasets for ANN modeling. Different networks of ANN were designed to achieve the purposes of this study. The best network had three hidden layers with 8, 16 and 5 nodes in each layer, respectively. The mean squared error and correlation coefficient between the observed and the predicted diameter of the fibers in the selected model were equal to 0.09008 and 0.93866, respectively, proving the efficacy of the ANN technique in the prediction process. Finally, three-dimensional graphs of the electrospinning parameters involved and nanofiber diameter were plotted to scrutinize the implications.

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