Using the Backpropagation Algorithm to Distinguish Arabic Alphabet

In this research, a study of the Arabic alphabet used a multi-layered neural network, which is the backpropagation error. Using the algorithm through the Losing activation function to train the network. The hidden numbers of nodes are 10, the number of cycles is 500, and the error is 0.001, using t...

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Main Author: Samyia Khalid Hasan
Format: Article
Language:Arabic
Published: Salahaddin University-Erbil 2024-02-01
Series:Zanco Journal of Humanity Sciences
Subjects:
Online Access:https://zancojournal.su.edu.krd/index.php/JAHS/article/view/1463
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author Samyia Khalid Hasan
author_facet Samyia Khalid Hasan
author_sort Samyia Khalid Hasan
collection DOAJ
description In this research, a study of the Arabic alphabet used a multi-layered neural network, which is the backpropagation error. Using the algorithm through the Losing activation function to train the network. The hidden numbers of nodes are 10, the number of cycles is 500, and the error is 0.001, using the Matlab R2013a program. The aim of the study It is the use of the network algorithm to recognize the characters, by training the network to recognize the characters in two cases. The first case is inputting the image of the letter into the grid and the second case is identifying the letter that represents the letter drawn in the image. And it was reached that the algorithm used for the network of nervousness to recognize the Arabic alphabet and then show it correctly.
format Article
id doaj-art-dbc95809d1f146fa98e2df17a5e4a7e2
institution Kabale University
issn 2412-396X
language Arabic
publishDate 2024-02-01
publisher Salahaddin University-Erbil
record_format Article
series Zanco Journal of Humanity Sciences
spelling doaj-art-dbc95809d1f146fa98e2df17a5e4a7e22025-08-20T03:43:41ZaraSalahaddin University-ErbilZanco Journal of Humanity Sciences2412-396X2024-02-0128110.21271/zjhs.28.1.6Using the Backpropagation Algorithm to Distinguish Arabic AlphabetSamyia Khalid Hasan0 College of Administration & Economics, Salahaddin University-Erbil In this research, a study of the Arabic alphabet used a multi-layered neural network, which is the backpropagation error. Using the algorithm through the Losing activation function to train the network. The hidden numbers of nodes are 10, the number of cycles is 500, and the error is 0.001, using the Matlab R2013a program. The aim of the study It is the use of the network algorithm to recognize the characters, by training the network to recognize the characters in two cases. The first case is inputting the image of the letter into the grid and the second case is identifying the letter that represents the letter drawn in the image. And it was reached that the algorithm used for the network of nervousness to recognize the Arabic alphabet and then show it correctly. https://zancojournal.su.edu.krd/index.php/JAHS/article/view/1463networks neural artificial, back propagation algorithm, weights, input and output.
spellingShingle Samyia Khalid Hasan
Using the Backpropagation Algorithm to Distinguish Arabic Alphabet
Zanco Journal of Humanity Sciences
networks neural artificial, back propagation algorithm, weights, input and output.
title Using the Backpropagation Algorithm to Distinguish Arabic Alphabet
title_full Using the Backpropagation Algorithm to Distinguish Arabic Alphabet
title_fullStr Using the Backpropagation Algorithm to Distinguish Arabic Alphabet
title_full_unstemmed Using the Backpropagation Algorithm to Distinguish Arabic Alphabet
title_short Using the Backpropagation Algorithm to Distinguish Arabic Alphabet
title_sort using the backpropagation algorithm to distinguish arabic alphabet
topic networks neural artificial, back propagation algorithm, weights, input and output.
url https://zancojournal.su.edu.krd/index.php/JAHS/article/view/1463
work_keys_str_mv AT samyiakhalidhasan usingthebackpropagationalgorithmtodistinguisharabicalphabet