United nation determined many basic climatic effects which affect the crust of Earth.
And the most important one is the climatic change and its effect on environmental, economic,
social, and political effects. So, the amount of rain which is considered as one of climatic
changes in Iraq should be studied.So, this research explains the factors which affect rain, its
overall average, the variation in the amounts of rain, the amount of yearly rain and variation
in both yearly and monthly rains by using standard variation and yearly fluctuation.As a
result, it is concluded that the number of rainy days doesn't mean an increase in rains amount.
And there's variation in rains amount in all study areas which is contrastive

Givers of foreign Audit about Social Responsibility of Profit Organization. The recent time is charcterstically with big economic Organization activities, because there are many transactions between these Organizations and different financial markets development techniques.

This  encourgage business men to increase their efforts for investment in these markets. Because the Accounting is in general terms it represents a language of these Unions Activities and translate them in to fact numbers, for that there is need for Accounting recording for certain of these Organizations behavior and their harmonization with their Objectives.

In this respect the Audit function comes to che

Abstract

This study aimed to identify the business risks using the approach of the client strategy analysis in order to improve the efficiency and effectiveness of the audit process. A study of business risks and their impact on the efficiency and effectiveness of the audit process has been performed to establish a cognitive framework of the main objective of this study, in which the descriptive analytical method has been adopted. A survey questionnaire has been developed and distributed to the targeted group of audit firms which have profession license from the Auditors Association in the Gaza Strip (63 offices). A hundred questionnaires have been distributed to the study sample of which, a total of 84 where answered and

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

A study of taxonomic quality of soil algae was conducted with some environmental variables in three sites of local gardens (Kadhimiya, Adhamiya and Dora) within the governorate of Baghdad for the period from October 2016 to March 2017. The study identified 28 species belonging to 16 species in which the predominance of blue green algae (18 species) Followed by Bacillarophyta algae (7 species) and three types of Chlorophyta. The study showed an increase in species of Oscillatoria. The results showed no significant differences between sites in temperature, pH and relative humidity, while there were clear differences between sites for salinity and nutrient The study showed a difference of irrigation water quality and use of different fertilize

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

The purpose of this research shed light on the analysis of the relationship between the knowledge gap and the strategic performance  gap and diagnose the level of impact this relationship in building a learning organization, and sought search to achieve a number of goals, cognitive and Applied been tested nature of the relationship and effect between variables in a sample size (62) of the managers of banks civil in Baghdad (Baghdad, Gulf, Assyria, Union, Elaf) and focused research problem in question is bold is whether the analysis of the relationship between the knowledge gap and the performance gap strategic leads to recognize organizations need to shift to organizations educated, either in the side of the field was the pr

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an