Abstract

This research aims to improve the provided health service level inside Baghdad hospitals and the Yarmouk educational, as well as to shed light on the reality of the health service and the quality within the major operations room in both hospitals, as the operations room represent the research community, as was the use of some quality tools Pareto and Ishikawa diagram to measure and assess the level of quality provided, and include research problem to find out what are the problems and obstacles facing the process of improving quality in both hospitals, and whether there are scientifically accurate method to assess the quality of health service in Baghdad's Yarmouk hospital and educational . Where the researcher h

Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is eff

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi

The research included five sections containing the first section on the introduction of the research and its importance and was addressed to the importance of the game of gymnastic and skilled parallel effectiveness and the importance of learning, but the problem of research that there is a difference in learning this skill and difficulty in learning may be one of the most important reasons are fear and fear of falling and injury, And a lack of sense of the movement of the movement is one of the obstacles in the completion of the skill and the goal of research to design a device that helps in learning the skill of descending Almtor facing with half a cycle according to the typical locomotor track on the parallel device of the technical men'

The unemployment is considered from the most danger problems that our society face them in current time & in the near future , because it makes prodigality for element of human being , particularly age of youth who have ability to work & producing , that resulted in negative effects forecast to dire consequences social and economical dangers . In the same time as will be stated in our explanation in the following in our research , because the unemployment has ability to help to prepare good environment to grow crime , actions of violence that mostly are main cause to decrease living level of majority of citizens & in increasing numbers who became under poverty , the unemployment is economical problem as it is psycholo

It has aimed by this research at presenting new interpretations to diversification phenomenon and its possible effects on firm value. These interpretations are culminated by the disclosure of the distinct role of resource-based view in the study of relationship between diversification strategy and firm value. The study has conducted on a sample consisted of the largest American companies listed in Fortune 500 along with seven years period
(2005-2011). This first local attempt, which has based on panel dat--a analysis for interpreting an expected variance of firm value due to diversification strategy, may helps with a clear vision to solve the contradiction and confusion surrounded the true linkages between t

Deep Learning Techniques For Skull Stripping of Brain MR Images

Today’s modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. It is used for analysis of human organs to replace surgery. For brain tumor detection, image segmentation is required. For this purpose, the brain is partitioned into two distinct regions. This is considered to be one of the most important but difficult part of the process of detecting brain tumor. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Earlier, a variety of algorithms were developed for segmentation of MRI images by usin

In this paper, a simple fast lossless image compression method is introduced for compressing medical images, it is based on integrates multiresolution coding along with polynomial approximation of linear based to decompose image signal followed by efficient coding. The test results indicate that the suggested method can lead to promising performance due to flexibility in overcoming the limitations or restrictions of the model order length and extra overhead information required compared to traditional predictive coding techniques.

Multilevel models are among the most important models widely used in the application and analysis of data that are characterized by the fact that observations take a hierarchical form, In our research we examined the multilevel logistic regression model (intercept random and slope random model) , here the importance of the research highlights that the usual regression models calculate the total variance of the model and its inability to read variance and variations between levels ,however in the case of multi-level regression models, the calculation of  the total variance is inaccurate and therefore these models calculate the variations for each level of the model, Where the research aims to estimate the parameters of this m