Each book has a specific style in which its author walks on it from its beginning to its end, and the Holy Qur’an is a book that compiled many methods that were indicative of its miracle, and that it is one unit even though it has been astrologer for twenty-three years.
There is no doubt that knowledge of the Qur’anic methods is one of the pillars of the approach that deals with any of the Qur’an, and the multiplicity of Qur’anic methods is a fact that has many causes. It has been expressed by the Qur’anic discharge and the conjugation of verses to bring them to different methods, and on multiple forms such as nominal, actual, singular Qur’an, presentation, delay, deletion, mention, abbreviation and redundancy. The Qur'ani

     Fuzzy C-means (FCM) is a clustering method used for collecting similar data elements within the group according to specific measurements. Tabu is a heuristic algorithm. In this paper, Probabilistic Tabu Search for FCM implemented to find a global clustering based on the minimum value of the Fuzzy objective function. The experiments designed for different networks, and cluster’s number the results show the best performance based on the comparison that is done between the values of the objective function in the case of using standard FCM and Tabu-FCM, for the average of ten runs.

      In current generation of technology, a robust security system is required based on biometric trait such as human gait, which is a smooth biometric feature to understand humans via their taking walks pattern. In this paper, a person is recognized based on his gait's style that is captured from a video motion previously recorded with a digital camera. The video package is handled via more than one phase after splitting it into a successive image (called frames), which are passes through a preprocessing step earlier than classification procedure operation. The pre-processing steps encompass converting each image into a gray image, cast off all undesirable components and ridding it from noise, discover differen

Median filter is adopted to match the noise statistics of the degradation seeking good quality smoothing images. Two methods are suggested in this paper(Pentagonal-Hexagonal mask and Scan Window Mask), the study involved modified median filter for improving noise suppression, the modification is considered toward more reliable results. Modification median filter (Pentagonal-Hexagonal mask) was found gave better results (qualitatively and quantitatively ) than classical median filters and another suggested method (Scan Window Mask), but this will be on the account of the time required. But sometimes when the noise is line type the cross 3x3 filter preferred to another one Pentagonal-Hexagonal with few variation. Scan Window Mask gave bett

Texture is an important characteristic for the analysis of many types of images because it provides a rich source of information about the image. Also it provides a key to understand basic mechanisms that underlie human visual perception. In this paper four statistical feature of texture (Contrast, Correlation, Homogeneity and Energy) was calculated from gray level Co-occurrence matrix (GLCM) of equal blocks (30×30) from both tumor tissue and normal tissue of three samples of CT-scan image of patients with lung cancer. It was found that the contrast feature is the best to differentiate between textures, while the correlation is not suitable for comparison, the energy and homogeneity features for tumor tissue always greater than its valu

In this paper, we introduce a DCT based steganographic method for gray scale images. The embedding approach is designed to reach efficient tradeoff among the three conflicting goals; maximizing the amount of hidden message, minimizing distortion between the cover image and stego-image,and maximizing the robustness of embedding. The main idea of the method is to create a safe embedding area in the middle and high frequency region of the DCT domain using a magnitude modulation technique. The magnitude modulation is applied using uniform quantization with magnitude Adder/Subtractor modules. The conducted test results indicated that the proposed method satisfy high capacity, high preservation of perceptual and statistical properties of the steg

Fractal image compression gives some desirable properties like fast decoding image, and very good rate-distortion curves, but suffers from a high encoding time. In fractal image compression a partitioning of the image into ranges is required. In this work, we introduced good partitioning process by means of merge approach, since some ranges are connected to the others. This paper presents a method to reduce the encoding time of this technique by reducing the number of range blocks based on the computing the statistical measures between them . Experimental results on standard images show that the proposed method yields minimize (decrease) the encoding time and remain the quality results passable visually.

      The image caption is the process of adding an explicit, coherent description to the contents of the image. This is done by using the latest deep learning techniques, which include computer vision and natural language processing, to understand the contents of the image and give it an appropriate caption. Multiple datasets suitable for many applications have been proposed. The biggest challenge for researchers with natural language processing is that the datasets are incompatible with all languages. The researchers worked on translating the most famous English data sets with Google Translate to understand the content of the images in their mother tongue. In this paper, the proposed review aims to enhance the understanding o

Although the number of stomach tumor patients reduced obviously during last decades in western countries, but this illness is still one of the main causes of death in developing countries. The aim of this research is to detect the area of a tumor in a stomach images based on fuzzy clustering. The proposed methodology consists of three stages. The stomach images are divided into four quarters and then features elicited from each quarter in the first stage by utilizing seven moments invariant. Fuzzy C-Mean clustering (FCM) was employed in the second stage for each quarter to collect the features of each quarter into clusters. Manhattan distance was calculated in the third stage among all clusters' centers in all quarters to disclosure of t