The aims of research is diagnose and indicate the role of fairness in the work of accounting, which is important in reducing the unacceptable practices to manage earnings by economic units, As well as the interpretation and indicate Ethical inside in the accounting and accountants in particular practices in the area of ​​earnings management to reduce the negative effects of the practice and display the results mentioned acts according to what is supposed to be for the purposes of service users, Researchers have identified that one hypothesis, that "Fairness of accountants in of their work accounting lead to a reduction of earnings management practices". The results suggest that the accounting practices that lead to the manipu

The research dealt with the effect of the tax examiner's efficiency in detecting tax evasion, as the research problem dealt with tax evasion, especially in Iraq, for many reasons and factors, including those related to tax administration represented in administrative corruption and the complexity of procedures in the tax accounting process and failure to achieve justice in the tax treatment, including the taxpayer himself, as he tries to evade for reasons Related to the level of tax awareness, loss of confidence in the tax administration, and reasons related to the state's inability to manage the services file well and its ability to achieve the set goals, This reflected negatively on the emergence of some of the consequences of

It is not long time since globalization has started along with knowledge economy and, communication media and technical information control on the world. As a result serious events appeared like failure in businesses, financial corruption and shortage of administration and control.

In spite of the great efforts held by international organizations and foundations to find organizational frames that adopt independence, que

Three-dimensional (3D) image and medical image processing, which are considered big data analysis, have attracted significant attention during the last few years. To this end, efficient 3D object recognition techniques could be beneficial to such image and medical image processing. However, to date, most of the proposed methods for 3D object recognition experience major challenges in terms of high computational complexity. This is attributed to the fact that the computational complexity and execution time are increased when the dimensions of the object are increased, which is the case in 3D object recognition. Therefore, finding an efficient method for obtaining high recognition accuracy with low computational complexity is essentia

Software testing is a vital part of the software development life cycle. In many cases, the system under test has more than one input making the testing efforts for every exhaustive combination impossible (i.e. the time of execution of the test case can be outrageously long). Combinatorial testing offers an alternative to exhaustive testing via considering the interaction of input values for every t-way combination between parameters. Combinatorial testing can be divided into three types which are uniform strength interaction, variable strength interaction and input-output based relation (IOR). IOR combinatorial testing only tests for the important combinations selected by the tester. Most of the researches in combinatorial testing appli

Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer. The most common abnormalities that may indicate breast cancer are masses and calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram images. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. The incidence of breast cancer in women has increased significantly in recent years.
This paper proposes a computer aided diagnostic system for the extracti

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv