This research dealt with the analysis of murder crime data in Iraq in its temporal and spatial dimensions, then it focused on building a new model with an algorithm that combines the characteristics associated with time and spatial series so that this model can predict more accurately than other models by comparing them with this model, which we called the Combined Regression model (CR), which consists of merging two models, the time series regression model with the spatial regression model, and making them one model that can analyze data in its temporal and spatial dimensions. Several models were used for comparison with the integrated model, namely Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Random Forest Reg

Tax justice is one of the important principles that any tax system seeks to achieve because it is an important and important pillar of the tax because it has negative repercussions on the tax accounting process on the one hand and the low tax revenues on the other hand. Therefore, countries have sought through their tax systems to use methods and methods In the process of estimating taxable incomes to achieve that justice depending on the awareness of their communities. The method adopted differs from one country to another according to the knowledge and understanding of the society of that state for taxation and its role in economic, social and political life. In Iraq, the General Authority for Taxation Methods of estimating taxable inc

يهدف البحث الحالي الى التعرف إلى درجة تقدير عينة من المديرين في المنظمات العامة لمستوى تطبيق ولتحقيق ، (Covid- نمط القيادة الخدمية من وجهة نظرهم ، وعلاقتها بمؤشر رضا الزبائن في ظل جائحة ( 19 ذلك اتبعت الباحثة المنهج التجريبي بتطبيق استبانة ضمت محورين، الأول: لقياس مستوى القيادة االخدمية وفقا الذي يشمل ( 8) أبعاد هي ( التمكين , الدعم , ( D. Van Dierendonck and I. Nuijten , لمقياس ( 2011 المساءلة, الشجاعة, التسامح , الموثوقية , التواضع , الر

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

Decision making is vital and important activity in field operations research ,engineering ,administration science and economic science with any industrial or service company or organization because the core of management process as well as improve him performance . The research includes decision making process when the objective function is fraction function and solve models fraction programming by using some fraction programming methods and using goal programming method aid programming ( win QSB )and the results explain the effect use the goal programming method in decision making process when the objective function is
fraction .

In this research estimated the parameters of Gumbel distribution Type 1 for Maximum values through the use of two estimation methods:- Moments (MoM) and Modification Moments(MM) Method. the Simulation used for comparison between each of the estimation methods to reach the best method to estimate the parameters where the simulation was to generate random data follow Gumbel distributiondepending on three models of the real values of the parameters for different sample sizes with samples of replicate (R=500).The results of the assessment were put in tables prepared for the purpose of comparison, which made depending on the mean squares error (MSE).

The efficiency evaluation of the railway lines performance is done through a set of indicators and criteria, the most important are transport density, the productivity of enrollee, passenger vehicle production, the productivity of freight wagon, and the productivity of locomotives. This study includes an attempt to calculate the most important of these indicators which transport density index from productivity during the four indicators, using artificial neural network technology. Two neural networks software are used in this study, (Simulnet) and (Neuframe), the results of second program has been adopted. Training results and test to the neural network data used in the study, which are obtained from the international in