Community detection is useful for better understanding the structure of complex networks. It aids in the extraction of the required information from such networks and has a vital role in different fields that range from healthcare to regional geography, economics, human interactions, and mobility. The method for detecting the structure of communities involves the partitioning of complex networks into groups of nodes, with extensive connections within community and sparse connections with other communities. In the literature, two main measures, namely the Modularity (Q) and Normalized Mutual Information (NMI) have been used for evaluating the validation and quality of the detected community structures. Although many optimization algorithms have been implemented to unfold the structures of communities, the influence of NMI on the Q, and vice versa, between a detected partition and the correct partition in signed and unsigned networks is unclear. For this reason, in this paper, we investigate the correlation between Q and NMI in signed and unsigned networks. The results show that there is no direct relationship between Q and NMI in both types of networks.
Transportability refers to the ease with which people, goods, or services may be transferred. When transportability is high, distance becomes less of a limitation for activities. Transportation networks are frequently represented by a set of locations and a set of links that indicate the connections between those places which is usually called network topology. Hence, each transmission network has a unique topology that distinguishes its structure. The most essential components of such a framework are the network architecture and the connection level. This research aims to demonstrate the efficiency of the road network in the Al-Karrada area which is located in the Baghdad city. The analysis based on a quantitative evaluation using graph th
... Show MoreThe continuous increases in the size of current telecommunication infrastructures have led to the many challenges that existing algorithms face in underlying optimization. The unrealistic assumptions and low efficiency of the traditional algorithms make them unable to solve large real-life problems at reasonable times.
The use of approximate optimization techniques, such as adaptive metaheuristic algorithms, has become more prevalent in a diverse research area. In this paper, we proposed the use of a self-adaptive differential evolution (jDE) algorithm to solve the radio network planning (RNP) problem in the context of the upcoming generation 5G. The experimental results prove the jDE with best vecto
Benthic invertebrates diversity on the Al-Dalmage the Lake in the middle of Iraq, in addition to the some chemical properties was the aim of the present study, for a period from January to the December 2013. Six stations of the lake were chosen for monthly samples collection. The result revealed that the water temperature ranged from (9-34) ◦C. According to the values of salinity (1.5-14.6) ‰ the lake was classified as Brackish, while the pH values were neutral tends to near alkaline and ranged from (7.2- 9). The study showed that the lake were well oxygenated due to the dissolved oxygen values which ranged from 7-12 mg/l. The lake considered as very hard according to the total hardness values which range from (825-6980) CaCo3 mg/l.
... Show MoreOne of the most interested problems that recently attracts many research investigations in Protein-protein interactions (PPI) networks is complex detection problem. Detecting natural divisions in such complex networks is proved to be extremely NP-hard problem wherein, recently, the field of Evolutionary Algorithms (EAs) reveals positive results. The contribution of this work is to introduce a heuristic operator, called protein-complex attraction and repulsion, which is especially tailored for the complex detection problem and to enable the EA to improve its detection ability. The proposed heuristic operator is designed to fine-grain the structure of a complex by dividing it into two more complexes, each being distinguished with a core pr
... Show MoreWith the spread use of internet, especially the web of social media, an unusual quantity of information is found that includes a number of study fields such as psychology, entertainment, sociology, business, news, politics, and other cultural fields of nations. Data mining methodologies that deal with social media allows producing enjoyable scene on the human behaviour and interaction. This paper demonstrates the application and precision of sentiment analysis using traditional feedforward and two of recurrent neural networks (gated recurrent unit (GRU) and long short term memory (LSTM)) to find the differences between them. In order to test the system’s performance, a set of tests is applied on two public datasets. The firs
... Show MoreEstablishing complete and reliable coverage for a long time-span is a crucial issue in densely surveillance wireless sensor networks (WSNs). Many scheduling algorithms have been proposed to model the problem as a maximum disjoint set covers (DSC) problem. The goal of DSC based algorithms is to schedule sensors into several disjoint subsets. One subset is assigned to be active, whereas, all remaining subsets are set to sleep. An extension to the maximum disjoint set covers problem has also been addressed in literature to allow for more advance sensors to adjust their sensing range. The problem, then, is extended to finding maximum number of overlapped set covers. Unlike all related works which concern with the disc sensing model, the cont
... Show MoreSoftware-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr
... Show Moreيهدف البحث الى تقديم استراتيجية مقترحة لشركة نفط الشمال ، وأخذت الاستراتيجية المقترحة بنظر الاعتبار الظروف البيئية المحيطة واعتمدت في صياغتها على اسس وخطوات علمية تتسم بالشمولية والواقعية ، اذ انها غطت الانشطة الرئيسية في الشركة (نشاط الانتاج والاستكشاف , نشاط التكرير والتصفية , التصدير ونقل النفط , نشاط البحث والتطوير , النشاط المالي , تقنية المعلومات , الموارد البشرية ) وقد اعتمد نموذج (David) في التحليل البيئي
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