Islamic shrines architecture is one of the Islamic Architectural building types. It is called (Thresholds); (Atabat) in Arabic. Thoroughbred throughout the Islamic world from the east to west. In addition, it carries the style features and peculiarities of uniqueness from the rest of the types of Islamic architecture. By virtue of the particularity of its function as tomb for important person in Islamic history. Islamic shrines architecture has grown and evolved in started from the small shrines, and then taken to widen with the evolution of time in line with the value of the event and the rituals associated with it. Some of them to became centers of large cities of the largest in many parts of the Islamic world.

The present study included the microscopic and molecular identification of Entamoeba histolytica by using specific primers to detect four virulence factors possessed by Entamoeba histolytica. Virulence factors included Active Cysteine proteinase, Galactose/N-acetyl-D-galactose-lectin, Amoeba pore C and Phospholipase. Titanium dioxide nanoparticles (TiO₂NPs) were synthesized from Pseudomonas aeruginosa which producing Pyocyanin pigment as a reducing agent to form it. After that we studied the ability ofTiO₂NPs to inhibit virulence factors production and curing the genes responsible for encoding them by using four different dose 2 ,3, 4, 6 mg/Kg and administered by intraperitoneal injection

The research dealt with the risks of reinsurance and its impact on the financial performance of the National Insurance Company by focusing on reinsurance in the marine insurance branch. Negative impact on the financial performance of the NICs and the Marine Insurance Branch. The research sample resulted in a main hypothesis that reflects this relationship. Lee in the analysis of financial information reports National Insurance Company of the branch of marine insurance for the period of 2010 until 2017, and the use of (retention) for measuring the re-insurance operations index, and (insurance financial surplus rate) to measure financial performance. For the purpose of obtaining the results, a number of statistical methods were used accord

Capital Adequacy is on of the most importance of financial position element because its role is enhance against varions risks of Banking procese . Which this risks is a result of the nature of its operations , investment , Financing tools in addition , in order to  Faced any clients withdraws un expected .

So This study aim to measure The Capital Adequacy according to basel II and explin the different risk in  the Islamic Bank as aresult to nature and different investment and financing tools as participation speculation , murabah and natures of its assets and liabilities related to commercial banks .

Find out we concludes that the Islamic Banks needs to increase the ca

Objective of the research This study aimed to manufacture an innovative device that enables the player to walk after the operation and improves functional efficiency through improvement in the range of motion as well as improvement in the size of the muscles working on the knee joint Imposing research There are statistically significant differences between the pre and posttests of the experimental and control groups, there are Statistically significant differences between the post-tests between the experimental group and the control group in favor of the experimental group of the research sample. The researchers used the experimental approach by designing the control and experimental groups with a test (pre-post) for the suitabili

Early diagnosis and clinical decision-making depend on accurate brain tumor classification using magnetic resonance imaging (MRI). However, traditional deep learning methods usually rely on centralized medical data, which raises privacy concerns and limits the use of distributed clinical data. This research proposes a privacy-preserving federated learning framework for MRI image-based binary brain tumor classification using a decentralized ResNet-18 architecture that enables collaborative training without sharing raw patient data. To reflect realistic clinical conditions, the framework integrates heterogeneous multi-source datasets in different image formats (PNG and JPG) and evaluates performance under both IID and non-IID settings

This research aims to analyze and simulate biochemical real test data for uncovering the relationships among the tests, and how each of them impacts others. The data were acquired from Iraqi private biochemical laboratory. However, these data have many dimensions with a high rate of null values, and big patient numbers. Then, several experiments have been applied on these data beginning with unsupervised techniques such as hierarchical clustering, and k-means, but the results were not clear. Then the preprocessing step performed, to make the dataset analyzable by supervised techniques such as Linear Discriminant Analysis (LDA), Classification And Regression Tree (CART), Logistic Regression (LR), K-Nearest Neighbor (K-NN), Naïve Bays (NB

Nowadays, people's expression on the Internet is no longer limited to text, especially with the rise of the short video boom, leading to the emergence of a large number of modal data such as text, pictures, audio, and video. Compared to single mode data ,the multi-modal data always contains massive information. The mining process of multi-modal information can help computers to better understand human emotional characteristics. However, because the multi-modal data show obvious dynamic time series features, it is necessary to solve the dynamic correlation problem within a single mode and between different modes in the same application scene during the fusion process. To solve this problem, in this paper, a feature extraction framework of

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD