Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem.  In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a

 Data Driven Requirement Engineering (DDRE) represents a vision for a shift from the static traditional methods of doing requirements engineering to dynamic data-driven user-centered methods. Data available and the increasingly complex requirements of system software whose functions can adapt to changing needs to gain the trust of its users, an approach is needed in a continuous software engineering process. This need drives the emergence of new challenges in the discipline of requirements engineering to meet the required changes. The problem in this study was the method in data discrepancies which resulted in the needs elicitation process being hampered and in the end software development found discrepancies and could not meet the need

Software Defined Networking (SDN) with centralized control provides a global view and achieves efficient network resources management. However, using centralized controllers has several limitations related to scalability and performance, especially with the exponential growth of 5G communication. This paper proposes a novel traffic scheduling algorithm to avoid congestion in the control plane. The Packet-In messages received from different 5G devices are classified into two classes: critical and non-critical 5G communication by adopting Dual-Spike Neural Networks (DSNN) classifier and implementing it on a Virtualized Network Function (VNF). Dual spikes identify each class to increase the reliability of the classification

Nowadays, university education stands in front of both students who feel they are weak and teachers who are addicted to using traditional and dependent teaching. This has led to have negative repercussions on the learner from different aspects, including the mental aspect and the academic achievement process. Therefore, the present research is concerned with finding a new teaching method that adopts the motivation by the fear of failure technique. Thus, the study aims to examine the effect of adopting this method on students’ academic achievement. To achieve this aim, an experimental method was used, and an achievement test was built for the curriculum material of level two students. The pretest test was applied on 17 male and female s

The γ- mixing ratios of γ- transitions from levels of 56Fe populated in reaction are calculated using least square fitting program for the first time in the case of pure and mixed transitions the results obtained have been compound with γ Values determined by other methods .The comparison shows that the agreement is good this confirmed the valilety of this method in calculating of values for such γ- transitions key word: γ- transition ,Multipole mixing ratios ,Least square fitting method.

The expansion of building blocks at the expense of agricultural land is one of the main problems causing climate change within the urban area of a city. The research came to determine these indicators, as a study was conducted on the expansion of the building blocks in three municipalities in the city of Baghdad for a period of four decades extended in the form of time cycles for the period (1981-2021) and using ArcMap GIS 10.7 technology. Then, the impact of this expansion on temperature rates was evaluated, as they are the most important climatic elements due to their significant effect on the rest of the elements. The results showed a clear, direct relationship between the increase in urban expansion rates and the corresponding r

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

Abstract

            This research was to provide a definition of quality, dimensions and concepts, whether traditional or modern concept, as well as review the dimensions of quality in higher education and vision and mission with the overall objectives of the Statistics Department.

         After reviewing quality goals and purposes achieved as well as the mechanisms used to achieve them. and use standard Six-Sigma as one of the methodologies used in quality with the historical roots of using this methodology and methods applied and their definitions t