Keywords : Artificial Neural Network


Hybridization of the Artificial Immune Network Using the Backpropagation Neural Network

Omar Saber Qasim; Israa Rustum Mohammed

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 4, Pages 103-114
DOI: 10.33899/csmj.2013.163559

In this research building style simulation developed is applied in the field of pattern recognition medical patients osteoporosis through a process of integrating and hybridization between artificial immune network and back propagation neural network, where the focus was on the qualities positive and overcome the negative qualities possessed by each of these two technologies by building technology improved, have proven technical hybrid it with better results and high efficiency in the classification of cases patients osteoporosis compared with both artificial immune network (AIN) and back propagation neural network (BP).
 

Modifying Explicit Finite Difference Method by Using Radial Basis Function Neural Network

Omar S. Kasim

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 2, Pages 171-186
DOI: 10.33899/csmj.2013.163484

  In this research, we use artificial neural networks, specifically radial basis function neural network (RBFNN) to improve the performance and work of the explicit finite differences method (EFDM), where it was compared, the modified method with an explicit finite differences method through solving the Murray equation and showing by comparing results with the exact solution that the improved method by using  (RBFNN) is the best and most accurate by giving less error rate through root mean square error (RMSE) from the classical method (EFDM).
 

Estimate Programmatic Effort using the Traditional COCOMO Model and Neural Networks

Jamal Salah Al-Din Sayed Majeed; Isra Zuhair Majeed Qabaa

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 1, Pages 351-364
DOI: 10.33899/csmj.2013.163464

Estimation models in software engineering are used to predict some important and future features for software project such as effort estimation for developing software projects. Failures  of  software  are  mainly  due  to  the faulty  project  management  practices. software project effort estimation is an important step in the process of software management of large projects. Continuous changing in software project makes effort estimation more challenging. The main objective of this paper is find a model to get a more accurate estimation. In this paper we used the Intermediate COCOMO model which is categorized as the best of traditional Techniques in Algorithmic effort estimation methods. also we used an Artificial approaches which is presented in (FFNN,CNN,ENN,RBFN) because of the Ability of ANN(Artificial Neural Network) to  model a  complex  set  of  relationship  between  the  dependent  variable (effort)  and  the  independent  variables  (cost  drivers)which makes  it  as  a  potential  tool    for  estimation. This  paper  presents a  performance analysis of ANNs used in effort estimation. We create and simulate this networks by MATLAB11 NNTool depending on NASA aerospace dataset which contains a features of 60 software project and its actual effort. the result of estimation in  this paper shows that the neural networks in general enhance the performance of traditional COCOMO and we proved that the ENN was the best network between neural networks and the CNN was the next best network and the COCOMO have the worst between the used methods.
 

Building an Intelligent System to Distinguish Russian Printed Letters using Artificial Neural Networks

Jamal S. Majeed; Sura R. Sherif; Osama Y. Mohamed

AL-Rafidain Journal of Computer Sciences and Mathematics, 2011, Volume 8, Issue 2, Pages 125-141
DOI: 10.33899/csmj.2011.163655

In this research, an intelligent computer system is designed for recognizing printed Russian letters by extracting features of the letter by finding the Eigen values which then used for training and testing the artificial neural network used in this work namely, Elman NN. This network is used as a tool for decision making. Data is entered using a flatbed scanner which results in high extensity, fineness and homogeneous BMP extension images. The programs are implemented by Matlab language, the software include image enhancement techniques, image segmentation, resize the segmented image and  features extraction dependent on Eigen values .These values are then used to train and test the Elman Neural Network. In this work the pass  ratio of recognition up to 90 % .
 

An Algorithm for the Best Way Connection among Cell-Phone Towers Using Feedforward Neural Network

Kais I. Ibraheem; Thamir Abdul Hafed Jarjis; Yahya Q. Ibrahim

AL-Rafidain Journal of Computer Sciences and Mathematics, 2009, Volume 6, Issue 3, Pages 197-209
DOI: 10.33899/csmj.2009.163833

A new approach for mobile radio towers is presented in this paper. The use of feed-forward artificial neural network makes it possible to overcome some important disadvantages of previous random distribution of the towers.
Our sample implementation is based upon the coordinates of a virtual cell phone towers distributed in Mosul city. The results show that the proposed algorithm is sufficiently accurate for use in planning mobile towers distribution system.
 

Artificial Intelligent Techniques with Watermarking

Nada N. Saleem; Baydaa I. Khaleel; Shahbaa I. Khaleel

AL-Rafidain Journal of Computer Sciences and Mathematics, 2009, Volume 6, Issue 2, Pages 229-266
DOI: 10.33899/csmj.2009.163810

This research presents three robust blind watermarking algorithms in the discrete wavelet transform and spatial domain based on neural network and fuzzy logic artificial intelligent techniques. To enhance the performance of the watermarking system the first algorithm is developed by combining Radial Basis Function (RBF) neural network with Discrete Wavelet Transform (DWT) using (DWT-RBFW) algorithm for embedding and extracting of watermark. The second developed (RBFW) algorithm used RBF neural network for embedding and extracting of watermark based on intensity of whole image. The third developed (FL-EXPW) watermarking method is based on fuzzy logic and expert system techniques and it’s the best algorithm among the three methods. The developed watermarking algorithms are robust against various attacks signal processing operations such as additive noise and jpeg compression, and geometric transformations.
 

Efficiency of Artificial Neural Networks (Percepton Network) in the Diagnosis of Thyroid Diseases

Suher A. Dawood; Laheeb M. Ibrahim; Nabil D. Kharofa

AL-Rafidain Journal of Computer Sciences and Mathematics, 2006, Volume 3, Issue 1, Pages 11-22
DOI: 10.33899/csmj.2006.164042

Thyroid gland software which was obtained through research is considered an effective system to diagnosed thyroid gland automatically. This is done by a built  complementary database which is flexible and easy at work with data patients concerning those patients under observation at Hazim Al-Hafith Hospital for Oncology & Nuclear  Medicine in Mosul. The activity of Thyroid gland software was tested on information about 200 Patients, and information about them was stred in Thyroid database, after that we diagnosed The Thyroid Gland Disease by using an artificial neural network (Perceptron) that is able to recognize Thyroid Gland Disease in good recognized ratio and with a ratio close to the doctor diagnosis depending on (sign & symptoms) which may enables the doctors in depending on it the right diagnosis for the disease.