The process of converting gray images or videos to color ones by adding colors to them and transforming them from one-dimension to three-dimension is called colorization. This process is often used to make the image appear more visually appealing. The main problem with the colorization process is the lack of knowledge of the true colors of the objects in the picture when it is captured. For that, there is no a unique solution. In the current work, the colorization of gray images is proposed based on the utilization of the YCbCr color space. Reference image (color image) is selected for transferring the color to a gray image. Both color and gray images are transferred to YCbCr color space. Then, the Y value of the gray image is combined w

The revolution of multimedia has been a driving force behind fast and secured data transmission techniques. The security of image information from unapproved access is imperative. Encryptions technique is used to transfer data, where each kind of data has its own special elements; thus various methods should to be used to conserve distributing the image. This paper produces image encryption improvements based on proposed an approach to generate efficient intelligent session (mask keys) based on investigates from the combination between robust feature for ECC algebra and construction level in Greedy Randomized Adaptive Search Procedure (GRASP) to produce durable symmetric session mask keys consist of ECC points. Symmetric behavior for ECC

Human health can be negatively impacted by exposure to loud noise, which can harm the auditory system. Traffic noise is the leading cause of noise pollution. This paper studies the problem of noise pollution on the roads in Baghdad, Iraq. Due to the increase in vehicle numbers and road network modifications in Baghdad, noise levels became a serious topic to be studied. The aim of the paper was thus to study traffic noise levels and the effect of the traffic stream on noise levels and to formulate a prediction model that identified the guidelines used for designing or developing future roads in the city. Then, the noise levels were measured based on five variables: the functional classification of roads, traffic flow, vehicle speed,

Concentrations and distribution of major, minor, and trace elements were
studied in thirteen sediment samples from Al-Hammar Marsh.
Multivariate statistical techniques such as Principal Component Analysis (PCA)
and Agglomerative Hierarchal Cluster Analysis (AHCA) as well as pollution
analysis such as Enrichment Factor (EF) were used to process the data and identify
the possible sources of elemental constituents in sediment samples.Results of
chemical analysis revealed that Major element mean concentrations were in the
order of Ca> Si> K> Mg> and minor elements were in the order of Al> Fe>S>Cl>
Ti> P>Mn> Sr> N and trace elements were in the order of Cr> Ni> Zr>V>Zn&gt

     Water balance as a technique is considered one of the means that is relied upon in solving significant hydrological problems. The soil and water assessment tool (SWAT) model was used in this study to assess the water balance in the Wadi Al-Mohammadi basin located at the eastern edge of the Western Desert. Digital elevation model, soil data, Land use - Land cover, and climate data represent the most important requirements for the SWAT model's input as a database. The Wadi Al-Mohammadi basin delineation results show the overall drainage area was 2286.8 km² with seven sub-basins. The trend line of climate data indicates a clear increase in the total rainfall, relative humidity, temperature, and solar radiation from 1990-

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

The main targets for using the edge detection techniques in image processing are to reduce the number of features and find the edge of image based-contents. In this paper, comparisons have been demonstrated between classical methods (Canny, Sobel, Roberts, and Prewitt) and Fuzzy Logic Technique to detect the edges of different samples of image's contents and patterns. These methods are tested to detect edges of images that are corrupted with different types of noise such as (Gaussian, and Salt and pepper). The performance indices are mean square error and peak signal to noise ratio (MSE and PSNR). Finally, experimental results show that the proposed Fuzzy rules and membership function provide better results for both noisy and noise-free