Keywords : Artificial Intelligent techniques

Hybrid intelligent watermark System

Fardos Adnan Abdalkader; Shahbaa I. Khaleel; Nada N. Saleem

AL-Rafidain Journal of Computer Sciences and Mathematics, 2010, Volume 7, Issue 2, Pages 125-138
DOI: 10.33899/csmj.2010.163902

As a result of the development in data transfer technology a cross multimedia and internet, it has became possible to access and copy these information in unauthentical manner . This leads to penetrate digital multimedia security problem.
In this research a hybrid method is designed to protect product from unauthentication access using watermark technique with digital images, these images represent the important part in information systems and many applications. The method indicates hiding the watermark in both spatial and frequency domains using Artificial Intelligent techniques, such as neural networks and genetic algorithms by dividing the watermark depending on the important information contents. The basic important part hides in frequency domain and the second part in spatial domain using Discreet Cosine Transform DCT and Least Significant Bit LSB.
The method efficiency is measured using Peak Signal –to-Noise Ratio PSNR and Normalized Correlation Coefficient NC , Also many attacks is used to measured the watermark robustness and feasibility.

Image Compression Based on Artificial Intelligent Techniques

Shahbaa I. Khaleel; Baydaa I. Khaleel; Alaa I. khaleel

AL-Rafidain Journal of Computer Sciences and Mathematics, 2009, Volume 6, Issue 3, Pages 75-109
DOI: 10.33899/csmj.2009.163839

This research present four methods to compress digital images using clustering based on artificial intelligent techniques that include neural network, fuzzy logic and hybrid between them. To enhance the performance of the compression system, the first method was developed in two types (k-means 1 dimension run length encoding km1D, k-means 2 dimension run length encoding km2D) by applying traditional clustering algorithm k-means on color and gray level images and then apply compression algorithm RLE in one and two dimension by zigzag scanning to obtain compressed image. The second method (fuzzy c-mean 1dimension run length encoding fcm1D, fuzzy c-mean 2dimension run length encoding fcm2D) used fuzzy c-mean to apply clustering operation and then compression. The third method (kohonen 1 dimension run length encoding Koh1D, kohonen 2dimension run length encoding Koh2D) used kohonen neural network for clustering image and then used RLE. The fourth developed method (fuzzy kohonen 1dimension run length encoding fKoh1D, fuzzy kohonen 2dimension run length encoding fKoh2D) based on hybrid kohonen neural network and fuzzy logic i.e fuzzy kohonen network which is recognized as the best method among the four methods. The four compression methods that are implemented in this research are efficient when applied on gray level and color images.

Face Recognition using Artificial Intelligent Techniques

Laheeb Mohammad Ibrahim; Ibrahim A. Saleh

AL-Rafidain Journal of Computer Sciences and Mathematics, 2009, Volume 6, Issue 2, Pages 211-227
DOI: 10.33899/csmj.2009.163809

Face recognition is considered one of the visual tasks which humans can do almost effortlessly while for computers it is a difficult and challenging task. This research deals with the problem of face recognition. A novel approach is presented for both face feature extraction and recognition, first, we introduce Principal Component Analysis (PCA) for face feature extraction, Generalized Regression Artificial Neural network for face recognition. The performance of the whole system was done after training with 120 color images (40 human faces with 3 poses) and testing using 40 color images. The images were taken from Collection of Facial Images: Faces95 by Computer Vision Science Research Projects. Experimental results for proposed human face recognition confirm that the proposed method lends itself to good extraction and classification accuracy relative to existing techniques.

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.