The imams are the keepers of religion and the law to be that they are in the pilgrimage season is necessary to be Hidayat Nur of the pilgrims, and the Imam Moussa al-Kadhim was one of them. And his relationship to begin the pilgrimage since he was born

Imam Ali Bin Al-Hussain Bin Ali (Zain Al-Abedien) (pbuh) is famous in worship, afraid from God , in spite of his origin that is related to prophet Mohammad , he felt  tired of worship until he had arrived to be sick ,he sometimes felt patient and looked yellow when he would be in front of God . It is important to shed light on his worship as describing him the forth Imam of the twelfth ones .What he had said and what he had done was as a guide to whom who want to be won in this life , finally, the research concluded that faith , worship deals with interior feeling lead to worship not by increasing them or by thinking , but it is as interiorly faith.

Praise be to God who has guided us, and we would not have been guided had it not been for the guidance of God Almighty. The life of the Commander of the Faithful, Imam Ali bin Abi Talib, peace be upon him, represented the highest meanings of sacrifice, redemption, courage, heroism, patience, altruism, and truth, which were embodied in his unique personality, whose counterpart is rare, if not unparalleled in history. Except for the person of the Holy Prophet Muhammad (may God bless him and his family and grant them peace). In this research, we have presented pictures from the life of Imam Ali bin Abi Talib (peace be upon him), as we have clarified his life, upbringing, conversion to Islam and his ma

Praise be to Allah and peace and blessings be on the Messengers and all his divine remission Mahdists companions Almentajabin. That this research dealt with elite valid and good done everything in its power to to live this nation on

In the present work, pattern recognition is carried out by the contrast and relative variance of clouds. The K-mean clustering process is then applied to classify the cloud type; also, texture analysis being adopted to extract the textural features and using them in cloud classification process. The test image used in the classification process is the Meteosat-7 image for the D3 region.The K-mean method is adopted as an unsupervised classification. This method depends on the initial chosen seeds of cluster. Since, the initial seeds are chosen randomly, the user supply a set of means, or cluster centers in the n-dimensional space.The K-mean cluster has been applied on two bands (IR2 band) and (water vapour band).The textural analysis is used

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.

Diabetic retinopathy is an eye disease in diabetic patients due to damage to the small blood vessels in the retina due to high and low blood sugar levels. Accurate detection and classification of Diabetic Retinopathy is an important task in computer-aided diagnosis, especially when planning for diabetic retinopathy surgery. Therefore, this study aims to design an automated model based on deep learning, which helps ophthalmologists detect and classify diabetic retinopathy severity through fundus images. In this work, a deep convolutional neural network (CNN) with transfer learning and fine tunes has been proposed by using pre-trained networks known as Residual Network-50 (ResNet-50). The overall framework of the proposed

The availability of different processing levels for satellite images makes it important to measure their suitability for classification tasks. This study investigates the impact of the Landsat data processing level on the accuracy of land cover classification using a support vector machine (SVM) classifier. The classification accuracy values of Landsat 8 (LS8) and Landsat 9 (LS9) data at different processing levels vary notably. For LS9, Collection 2 Level 2 (C2L2) achieved the highest accuracy of (86.55%) with the polynomial kernel of the SVM classifier, surpassing the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) at (85.31%) and Collection 2 Level 1 (C2L1) at (84.93%). The LS8 data exhibits similar behavior. Conv

Accurate emotion categorization is an important and challenging task in computer vision and image processing fields. Facial emotion recognition system implies three important stages: Prep-processing and face area allocation, feature extraction and classification. In this study a new system based on geometric features (distances and angles) set derived from the basic facial components such as eyes, eyebrows and mouth using analytical geometry calculations. For classification stage feed forward neural network classifier is used. For evaluation purpose the Standard database "JAFFE" have been used as test material; it holds face samples for seven basic emotions. The results of conducted tests indicate that the use of suggested distances, angles