Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the  most effective parameter, particularly when Age<49.5. Whereas  Ki67  appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimu

In digital images, protecting sensitive visual information against unauthorized access is considered a critical issue; robust encryption methods are the best solution to preserve such information. This paper introduces a model designed to enhance the performance of the Tiny Encryption Algorithm (TEA) in encrypting images. Two approaches have been suggested for the image cipher process as a preprocessing step before applying the Tiny Encryption Algorithm (TEA). The step mentioned earlier aims to de-correlate and weaken adjacent pixel values as a preparation process before the encryption process. The first approach suggests an Affine transformation for image encryption at two layers, utilizing two different key sets for each layer. Th

          Melanoidins can be diagnosed using the Fourier transform infrared (FTIR) technique. UV/Vis is an effective tool for both qualitative and quantitative analysis of chemical components in melanoidin polymers. The structural and vibrational features of melanoidin synthesized from D-glucose and D-fructose are identical, according to FTIR spectra, with the only difference being the intensity of bands.  Using FTIR spectra, the skeleton of melanoidin is divided into seven major regions. The existence of the C=C, C=N, and C=O groups in all melanoidins formed from fructose and glucose with ammonia is confirmed by the areas ranging from 1600 to 1690 cm^-1, and the band is largely evident as a broad shoulder. Both melan

Assessing the accuracy of classification algorithms is paramount as it provides insights into reliability and effectiveness in solving real-world problems. Accuracy examination is essential in any remote sensing-based classification practice, given that classification maps consistently include misclassified pixels and classification misconceptions. In this study, two imaginary satellites for Duhok province, Iraq, were captured at regular intervals, and the photos were analyzed using spatial analysis tools to provide supervised classifications. Some processes were conducted to enhance the categorization, like smoothing. The classification results indicate that Duhok province is divided into four classes: vegetation cover, buildings,

The present study was carried out to determine the bacterial isolates and study their antimicrobial susceptibility in case of burned wound infections. 70 burn wound swabs were taken from patients, who presented invasive burn wound infection from both sex and average age of 3-58 years, admitted to teaching medical Al- Kendi hospital from October 2007 to June 2008. Pseudomonas aeruginosa was found to be the most common isolate (48.9%) followed by Staphylococcus aureus (24.4%), Citrobacter braakii (13.3%), Enterobacter spp. (11.1%), Coagulase-negative Staphylococci (11.1%), Proteus vulgaris (6.66%), Corynebacterium spp. (6.66%), Micrococcus (6.66%), Proteus mirabilis (4.44%), Enterococcus faecalis (4.44%), E.coli (4.44%), Klebsiella spp. (2.22

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.

In this paper, two new simple, fast and efficient block matching algorithms are introduced, both methods begins blocks matching process from the image center block and moves across the blocks toward image boundaries. With each block, its motion vector is initialized using linear prediction that depending on the motion vectors of its neighbor blocks that are already scanned and their motion vectors are assessed. Also, a hybrid mechanism is introduced, it depends on mixing the proposed two predictive mechanisms with Exhaustive Search (ES) mechanism in order to gain matching accuracy near or similar to ES but with Search Time ST less than 80% of the ES. Also, it offers more control capability to reduce the search errors. The experimental tests

The study aims to identify the extent of the availability of administrative empowerment and the implementation of remote supervision in relation to two variables with respect to the employees of the Saudi Ministry of Education. The study included (456) male and female supervisors. The questionnaire has been administrated to collected data related to the research aims. The results indicated that both the extent of the availability of administrative empowerment and the implementation of remote supervision is of an average degree. There is a significant correlation relationship (p<0.05) between administrative empowerment and remote supervision of the educational supervisors of the Saudi Ministry of Education. The research recommends that