The research addresses the role of the digital economy in the growth of the Iraqi economy during the period from 2010 to 2022. The research is based on the hypothesis that the digital economy has become one of the primary growth drivers worldwide and has a close relationship with economic development. Therefore, the digital transformation in Iraq can accelerate bridging developmental gaps with other countries.

It has become evident that the Iraqi economy suffers from structural imbalances for various reasons, hindering economic growth. These reasons include political and economic factors, as well as the absence of a well-thought-out policy to promote the agricultural sector, which is considered one of the fundamental sectors capa

Football is a game derived from the integration of football and tennis skills and some of the laws of volleyball and yard measurements, and since it is a newly created game must be studied and cover all aspects related to them to reach them to the highest levels. It is known that each game or sports activity should have Physical capabilities and motor skills, which is important to determine the level of technical performance where the abilities of the game contribute to mastering skills. The significance of the research in determining the standard levels of physical and motor abilities of the football players to help young professionals and the sponsors to increase the efficiency of these capabilities and thus raise the level of per

Today, data security is a major problem concerning organizations and indi- viduals. The confidentiality of information is associated with using reliable and robust encryption algorithms in systems. Cyber-attacks on data and systems have become prevalent and sophisticated, and are increasing rapidly; hence, the need for developing robust encryption algorithms is crucial nowadays. This paper proposes a new encryption algorithm using dynamic symmetric key cryptography to encrypt text files. It utilizes a secret key for encryption and decryption processes, where the key’s length is varied depending on the text size. This presented approach gives a trade-off between speed and security, making it suitable for various applications, such as secur

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an

The problem of Bi-level programming is to reduce or maximize the function of the target by having another target function within the constraints. This problem has received a great deal of attention in the programming community due to the proliferation of applications and the use of evolutionary algorithms in addressing this kind of problem. Two non-linear bi-level programming methods are used in this paper. The goal is to achieve the optimal solution through the simulation method using the Monte Carlo method using different small and large sample sizes. The research reached the Branch Bound algorithm was preferred in solving the problem of non-linear two-level programming this is because the results were better.

There is a great deal of systems dealing with image processing that are being used and developed on a daily basis. Those systems need the deployment of some basic operations such as detecting the Regions of Interest and matching those regions, in addition to the description of their properties. Those operations play a significant role in decision making which is necessary for the next operations depending on the assigned task. In order to accomplish those tasks, various algorithms have been introduced throughout years. One of the most popular algorithms is the Scale Invariant Feature Transform (SIFT). The efficiency of this algorithm is its performance in the process of detection and property description, and that is due to the fact that