Print ISSN: 1812-125X

Online ISSN: 2664-2530

Keywords : Neural Networks


Optimal classification of remote sensing data using directed and non-directed instruction of neural networks

Kanar Mohammed Sami Mustafa; Lubna Thanoon Al-Kahli

JOURNAL OF EDUCATION AND SCIENCE, 2011, Volume 24, Issue 1, Pages 136-149
DOI: 10.33899/edusj.1999.51456

Abstract
The study of multi classification of data has become one of the important issues which geographical studies focus on especially those which take their data from satellites.
Two ways of classification has been taken into consideration, each one completes the other. The two ways are used together to get the benefits of both and to obtain the full advantage. The data of remote sensing were chosen to be tested.
The system suggested is a software package which consists of neural networks of supervised learning and neural networks of unsupervised learning to classify the land of (Al-Mosul Dam) in Mosul city.
The best use of remotely sensed data is by using the methods of supervised and unsupervised classification consequently which improve the primary input data in the classification. Thus, a high degree of accuracy and efficiency in the classification are obtained.
It is worth mentioning that software package used in the integration consists of the nets: the supervised ART-II neural network and the unsupervised Kohonen neural network.

Diagnosis of pulmonary diseases using neural networks

Eman Fathi Ahmed; Yahya Ismael Ibrahim

JOURNAL OF EDUCATION AND SCIENCE, 2010, Volume 23, Issue 4, Pages 142-156
DOI: 10.33899/edusj.2010.58452

Abstract
The main purpose of this research is to give high accuracy result in pulmonary diseases diagnosis and attaining real medications that corresponds with the decisions of the pulmonary disease specialist. The neural network (perception network) which has ability of giving stable results in medical fields, was used for this purpose. Thirty samples were taken from infected patients with pulmonary diseases (Asthma, tuberculosis) and the network was trained of the symptoms of these diseases and samples. Good diagnostics results were attained corresponding with the symptoms of diseases.

Comparative study between artificial neural networks ( recognition of printed English numbers)

manahil Abdul Karim Yusuf

JOURNAL OF EDUCATION AND SCIENCE, 2010, Volume 23, Issue 2, Pages 73-90
DOI: 10.33899/edusj.2010.58252

ABSTRACT
This study is aimed to make a comparison between three artificial neural networks, these networks differ from each other in architecture and the method of adaptive the weights. In this research four ANN are used to recognized English number, these ANN are Adaline, Backpropagation, Hopfield, and Kohen ANN. By doing the comparison, we found that, the ability of a network differentiation does not depend on the complexity of the network architecture, the training algorithm or the number of layers, but it depends on the learning rule and increase in the number of the patterns that are used to train the network.

Management of relational databases of herbaceous plants using neural networks and fazzy logic

Anhar Mohammed; Suhair Abd Dawood

JOURNAL OF EDUCATION AND SCIENCE, 2008, Volume 21, Issue 1, Pages 149-160
DOI: 10.33899/edusj.2008.51260

The research aim is to design a system that help the user which want agriculture some of types of plantago with adequate way without losing in effort, Time & money through designing of general computer database about this plantago & understanding its behavior and sensitivity to light and temperature by connecting database with fuzzy logic, so number from fuzzy logical bases are shaping to know the growth ratio for the seeds of a number from the kinds of the planted plantago, the membership functions connecting within the every one of its variables.
We connect Backpropagation Neural Network with Fuzzy Logic to appoint the Type (classifying) of Plantago which growths .
The database is designed by using(Microsoft Access)and is programmed by using Visual Basic Ver. 6 and works With Windows Me.