Analysis of Arabic letters recognition using artificial neural networks
Dissertant
Thesis advisor
Comitee Members
al-Shaykh, Isam
Kanan, Ghassan Jaddu
al-Qarini, Shihadah
University
Arab Academy for Financial and Banking Sciences
Faculty
The Faculty of Information Systems and Technology
Department
Computer information systems
University Country
Jordan
Degree
Ph.D.
Degree Date
2010
English Abstract
The Analysis of Arabic letter recognition using Artificial Neural Networks had been affected by many network factors; these factors had many changeable parameters in order to reach to the optimum architecture of pattern recognition for Arabic letters.
The presented work aims to enhance the main factor which affected the network architecture which was the network architecture, also the work presented the best value of other factors which gave the best solution in terms of training ratio, training time, simulation time and number of iteration to meet the network goal and the activation function.
In order for the artificial neural network to be useful, it is necessary to adjust the network factors (number of hidden layers, learning rate, number of neuron in each layer, etc) to reach the optimum network architecture appropriately.
The adjustments of network factors are accomplished by comparative results, along with examples of how the system should function.
The basic idea behind the analytical study is to find the best set of factors based on the statistics of training examples that produces correct input/output/relationship.
In this thesis, I obtained the optimal neural network architecture that can reach the goals in minimum number of training iterations and minimum time of training.
From the experimental results, we can reached the goals by adding a hidden layer to the network, and an optimized solution can be achieved if the number of neurons is equal to the power of 2 of the number of inputs (n2) where n represents the number of input layer.
Splitting the number of neurons in the hidden layer to 2 or more hidden layers make the network not optimal by mean of increasing the number of training iterations and increasing the training time.
Main Subjects
Information Technology and Computer Science
Topics
No. of Pages
108
Table of Contents
Table of contents.
Abstract.
Abstract.
Chapter One : dissertation approach and formulation.
Chapter Two : theoritical introduction and background.
Chapter Three : literature review.
Chapter Four : pattern recognition using neural networks.
Chapter Five : methodolgy and result evaluation.
Chapter Six : conclusion.
References.
American Psychological Association (APA)
al-Shaltouni, Ali Mahmud. (2010). Analysis of Arabic letters recognition using artificial neural networks. (Doctoral dissertations Theses and Dissertations Master). Arab Academy for Financial and Banking Sciences, Jordan
https://search.emarefa.net/detail/BIM-307226
Modern Language Association (MLA)
al-Shaltouni, Ali Mahmud. Analysis of Arabic letters recognition using artificial neural networks. (Doctoral dissertations Theses and Dissertations Master). Arab Academy for Financial and Banking Sciences. (2010).
https://search.emarefa.net/detail/BIM-307226
American Medical Association (AMA)
al-Shaltouni, Ali Mahmud. (2010). Analysis of Arabic letters recognition using artificial neural networks. (Doctoral dissertations Theses and Dissertations Master). Arab Academy for Financial and Banking Sciences, Jordan
https://search.emarefa.net/detail/BIM-307226
Language
English
Data Type
Arab Theses
Record ID
BIM-307226
