يعيش المجتمع العالمي أزمات بنيوية ونسقيه عميقة واختلالات وظيفية أسهمت في تنامي مظاهر الصراع المختلفة واشتداد تفجر الأزمات الهوياتية وتزايد الامراض الاجتماعية المقضة للعيش المشترك والمربكة لمظاهر التعايش والتآلف مع لجوء إنسان العصر الحالي الى التقوقع والاحتماء والانزواء تحت هويات ضيقة ونحل صغيره في مشهد يظهر مدى تراضي شبكة العالقات الاجتماعية على أداء وظائفها وأصبح عندها الاغتراب والقلق واللاتضامن والع

Due to the impact of industrial companies on the ransformations and developments witnessed by the world in general nd Iraq in particular, especially in the industrial and economic Jonrnal spects, the liberalization of internal and external trade, and the idoption of the open market policy in Iraq, which led to the intensification of competition between industrial companies and heir products and services, and the increase and diversity of the Onumber of alternatives Available to the public on the one hand, and he diversity and change of their desires and needs on the other hand, which was reflected in the reality of those companies and heir products or services, especially in light of the great challenges facing companies and the deteriorat

Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.

Nowadays, still images are used everywhere in the digital world. The shortages of storage capacity and transmission bandwidth make efficient compression solutions essential. A revolutionary mathematics tool, wavelet transform, has already shown its power in image processing. The major topic of this paper, is improve the compresses of still images by Multiwavelet based on estimation the high Multiwavelet coefficients in high frequencies sub band  by interpolation instead of sending all Multiwavelet coefficients. When comparing the proposed approach with other compression methods Good result obtained

     The searching process using a binary codebook of combined Block Truncation Coding (BTC) method and Vector Quantization (VQ), i.e. a full codebook search for each input image vector to find the best matched code word in the codebook, requires a long time.   Therefore, in this paper, after designing a small binary codebook, we adopted a new method by rotating each binary code word in this codebook into 900 to 2700 step 900 directions. Then, we systematized each code word depending on its angle  to involve four types of binary code books (i.e. Pour when , Flat when  , Vertical when, or Zigzag). The proposed scheme was used for decreasing the time of the coding procedure, with very small distortion per block, by designing s

 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification

The denoising of a natural image corrupted by Gaussian noise is a problem in signal or image processing.  Much work has been done in the field of wavelet thresholding but most of it was focused on statistical modeling of wavelet coefficients and the optimal choice of thresholds.  This paper describes a new method for the suppression of noise in image by fusing the stationary wavelet denoising technique with adaptive wiener filter. The wiener filter is applied to the reconstructed image for the approximation coefficients only, while the thresholding technique is applied to the details coefficients of the transform, then get the final denoised image is obtained by combining the two results. The proposed method was applied by usin