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An Organized Committee of Artificial Neural Networks in the Classification of Human Chromosomes

by Sadina Gagula-palalic, Mehmet Can
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 80 - Number 8
Year of Publication: 2013
Authors: Sadina Gagula-palalic, Mehmet Can
10.5120/13884-1789

Sadina Gagula-palalic, Mehmet Can . An Organized Committee of Artificial Neural Networks in the Classification of Human Chromosomes. International Journal of Computer Applications. 80, 8 ( October 2013), 38-41. DOI=10.5120/13884-1789

@article{ 10.5120/13884-1789,
author = { Sadina Gagula-palalic, Mehmet Can },
title = { An Organized Committee of Artificial Neural Networks in the Classification of Human Chromosomes },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 80 },
number = { 8 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 38-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume80/number8/13884-1789/ },
doi = { 10.5120/13884-1789 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:54:02.985637+05:30
%A Sadina Gagula-palalic
%A Mehmet Can
%T An Organized Committee of Artificial Neural Networks in the Classification of Human Chromosomes
%J International Journal of Computer Applications
%@ 0975-8887
%V 80
%N 8
%P 38-41
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Neural networks are organized in committees to improve the correctness of the decisions created by artificial neural networks (ANN's). In the classification of human chromosomes, it is accustomed to use multilayer perceptrons with multiple (22-24) outputs. Because of the huge number of synaptic weights to be tuned, these classifiers cannot go beyond a level of 92% overall correctness. In this study we represent a special organized committee of 462 simple perceptrons to improve the rate of correct classification of 22 types of human chromosomes. Each of these simple perceptrons is trained to distinguish between two types of chromosomes. When a new data is entered, the votes of these 462 simple perceptrons and additional 22 dummy perceptrons create a decision matrix of the size 22×22. By a special assembling of these votes we get a higher rate of correct classification of 22 types of human chromosomes.

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Index Terms

Computer Science
Information Sciences

Keywords

Classification of human chromosomes perceptrons committee machines image profile metaphase

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