The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approach

The current study highlighted the violations and the steady depletion of , and agricultural and green areas in and around cities , which is an accurate scientific research problem that affects the future perspectives of these areas, their production and their consequences for the life of cities and their vital surroundings. This research took Baghdad city as a model of this critical phenomenon in order to study the reality of these regions, the size of violations, and to set a future concept and strategy, in addition to the proper treatment that preserves the assets of this great wealth , It was one of the most important conclusions Urban planning disruption of Baghdad city formations ,One of the most important recommendations , Scaling Bag

In this article we  study a single stochastic process model for the evaluate the assets pricing and stock.,On of the models le'vy . depending on the so –called Brownian subordinate as it has been depending on the so-called Normal Inverse Gaussian (NIG). this article aims as the estimate that the parameters of his model using my way (MME,MLE) and then employ those  estimate of the parameters is the study of stock returns and evaluate asset pricing for both the united Bank and Bank of North which their data were taken from the Iraq stock Exchange.

which showed the results to a preference MLE on MME based on the standard of comparison the average square e

Purpose:  The research aims to explore the impact Business Intelligence System (BIS) and Knowledge Conversion Processes (KCP) in the Building Learning Organization (LO) in KOREK Telecom Company in Baghdad city.

Design/methodology/approach: in order to achieve the objectives of the research has been the development of a questionnaire prepared for this purpose and then has tested the search in the telecommunications sector, representatives of one of the telecommunications companies in Baghdad city, has therefore chosen KOREK Telecom company as a sample for research, and the choice was based on the best standard international companies to serve mobile communications in terms o

Four antimony compounds were used in this inves as additives to retard combustion of unsaturated polyester and epoxy resins, namely: 1. Tetraethyl ammonium tribromoethylantimonates (additive I). 2. Tetraethyl ammonium chlorodibromoethylantimonates (additive II). 3. Tetraethyl ammonium trichloroethylantimonates (additive III). 4. Tetraethyl ammonium bromodichloroethylantimonates (additive IV). The effects of these additives on flammability of unsaturated polyester and epoxy resins have been studied by using sheets of the resins with weight percentages of (0.5,1.0,1.5,2.0,2.5&3.0%) of the additives in dimensions of (150 X150X3)mm .Three standard test methods were used to measure the flame retardation which are: (ASTM:D -2863), (ASTM:D-635

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a