In this research a study process to calculate the factor accumulation of gamma rays for aluminum and exporters cobalt 60 Mika Atktron volts and actively radiation Vdrh 1.406 Mika Bq been studying the effect of the angle of reimbursement and the distance between the shield and detector In measurements factor accumulation Adhrt results in line with the theoretical results published

In this paper, the goal of proposed method is to protect data against different types of attacks by unauthorized parties. The basic idea of proposed method is generating a private key from a specific features of digital color image such as color (Red, Green and Blue); the generating process of private key from colors of digital color image performed via the computing process of color frequencies for blue color of an image then computing the maximum frequency of blue color, multiplying it by its number and adding process will performed to produce a generated key. After that the private key is generated, must be converting it into the binary representation form. The generated key is extracted from blue color of keyed image then we selects a c

   Chaotic features of nuclear energy spectrum in ⁶⁸Ge nucleus are investigated by nuclear shell model. The energies are calculated through doing shell model calculations employing the OXBASH computer code with effective interaction of F5PVH. The ⁶⁸Ge nucleus is supposed to have an inert core of ⁵⁶Ni with 12 nucleons (4 protons and 8 neutrons) move in the f5p-model space ( and ). The nuclear level density of considered classes of states is seen to have a Gaussian form, which is in accord with the prediction of other theoretical studies. The statistical fluctuations of the energy spectrum (the level spacing P(s) and the Dyson-Mehta (or statistics) are well described by the Gaussian orthogonal ens

Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select

Aim: The purpose of this study was to analyze the patterns of facial fractures in children and to compare them between preschool- and school-aged children. Materials and methods: This retrospective observational study included 57 children with facial fractures. The variables analyzed were the age of the patients—divided into a preschool-aged group (0–5 years) and a school-aged group (6–12 years)—gender, cause of trauma, the facial bones involved, the pattern of fracture, the modality of treatment used, the time between injury and treatment, and the postoperative complications. Results: The incidence of facial fractures in children ≤12 years was 30.2%. The patients consisted of 40 (70.2%) males and 17 (29.8%) females, and most pati

KE Sharquie, AA Noaimi, HG Mahmood, SM Al-Ogaily, Journal of Cosmetics, Dermatological Sciences and Applications, 2015 - Cited by 6

KE Sharquie, AA Noaimi, Pigmentary Disorders, 2014 - Cited by 5

HM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023

Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.