In this paper three techniques for image compression are implemented. The proposed techniques consist of three dimension (3-D) two level discrete wavelet transform (DWT), 3-D two level discrete multi-wavelet transform (DMWT) and 3-D two level hybrid (wavelet-multiwavelet transform) technique. Daubechies and Haar are used in discrete wavelet transform and Critically Sampled preprocessing is used in discrete multi-wavelet transform. The aim is to maintain to increase the compression ratio (CR) with respect to increase the level of the transformation in case of 3-D transformation, so, the compression ratio is measured for each level. To get a good compression, the image data properties, were measured, such as, image entropy (He), percent r

Ferritin is a key organizer of protected deregulation, particularly below risky hyperferritinemia, by straight immune-suppressive and pro-inflammatory things. , We conclude that there is a significant association between levels of ferritin and the harshness of COVID-19. In this paper we introduce a semi- parametric method for prediction by making a combination between NN and regression models. So, two methodologies are adopted, Neural Network (NN) and regression model in design the model; the data were collected from مستشفى دار التمريض الخاص for period 11/7/2021- 23/7/2021, we have 100 person, With COVID 12 Female & 38 Male out of 50, while 26 Female & 24 Male non COVID out of 50. The input variables of the NN m

<span>Deepfakes have become possible using artificial intelligence techniques, replacing one person’s face with another person’s face (primarily a public figure), making the latter do or say things he would not have done. Therefore, contributing to a solution for video credibility has become a critical goal that we will address in this paper. Our work exploits the visible artifacts (blur inconsistencies) which are generated by the manipulation process. We analyze focus quality and its ability to detect these artifacts. Focus measure operators in this paper include image Laplacian and image gradient groups, which are very fast to compute and do not need a large dataset for training. The results showed that i) the Laplacian

Background: e cerebellum is divided into two hemispheres and contains a narrow midline zone called thevermis. A set of large folds are conventionally used to divide the overall structure into ten smaller "lobules". evermis receives fibres from the trunk and proximal portions of limbs, But the question is that does the cerebellum have the same measurementvalues in males and females of the same age?Material and method: e present study used 80 sectional brain MRI images (40: males, 40: females); 35-50 years old as indices of size for thevermian structures of the Cerebellum. is middle age group was taken because as known generally it could be neither an age of growth as inthe young nor of atrophy as in old individuals. e aim rega

The last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of

Optimizing system performance in dynamic and heterogeneous environments and the efficient management of computational tasks are crucial. This paper therefore looks at task scheduling and resource allocation algorithms in some depth. The work evaluates five algorithms: Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Firefly Algorithm (FA) and Simulated Annealing (SA) across various workloads achieved by varying the task-to-node ratio. The paper identifies Finish Time and Deadline as two key performance metrics for gauging the efficacy of an algorithm, and a comprehensive investigation of the behaviors of these algorithms across different workloads was carried out. Results from the experiment