Finding communities of connected individuals in complex networks is challenging, yet crucial for understanding different real-world societies and their interactions. Recently attention has turned to discover the dynamics of such communities. However, detecting accurate community structures that evolve over time adds additional challenges. Almost all the state-of-the-art algorithms are designed based on seemingly the same principle while treating the problem as a coupled optimization model to simultaneously identify community structures and their evolution over time. Unlike all these studies, the current work aims to individually consider this three measures, i.e. intra-community score, inter-community score, and evolution of community over time. Here, we adopt a new perspective towards detecting the evolution of community structures. The proposed method realizes the decomposition of the problem into three essential components; searching in: intra-community connections, inter-community connections, and community evolution. A multi-objective optimization problem is defined to account for the different intra and inter community structures. Further, we formulate the community evolution problem as a Hidden Markov Model in an attempt to dexterously track the most likely sequence of communities. Then the new model, called Hidden Markov Model-based Multi-Objective evolutionary algorithm for Dynamic Community Detection (HMM-MODCD), uses a multi-objective evolutionary algorithm and Viterbi algorithm for formulating objective functions and providing temporal smoothness over time for clustering dynamic networks. The performance of the proposed algorithm is evaluated on synthetic and real-world dynamic networks and compared against several state-of-the-art algorithms. The results clearly demonstrate the effectiveness of the proposed algorithm to outperform other algorithms.
The current research aims to identify the impact of the (Landa) model on acquiring grammatical concepts among students of the College of Administration and Economics, University of Baghdad, and to achieve the research goal, the researcher has set the following hypotheses: There are no statistically significant differences at the level of significance (0.05) between the average degrees Students of the experimental group who studied the Arabic language according to the (Landa) model and the marks of the students of the control group who studied the same subject in the usual way in the post test, there are no statistically significant differences at the level of significance (0.05) in the average differences between the test scores before and
... Show MoreIt is well known that drilling fluid is a key parameter for optimizing drilling operations, cleaning the hole, and managing the rig hydraulics and margins of surge and swab pressures. Although the experimental works represent valid and reliable results, they are expensive and time consuming. In contrast, continuous and regular determination of the rheological fluid properties can perform its essential functions during good construction. The aim of this study is to develop empirical models to estimate the drilling mud rheological properties of water-based fluids with less need for lab measurements. This study provides two predictive techniques, multiple regression analysis and artificial neural networks, to determine the rheological
... Show More<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver ope
... Show MoreThe present study investigates the relation between the biliteral and triliteral roots which is the introduction to comprehend the nature of the Semitic roots during its early stage of development being unconfirmed to a single pattern. The present research is not meant to decide on the question of the biliteral roots in the Semitic languages, rather it is meant to confirm the predominance of the triliteral roots on these languages which refers, partially, to analogy adopted by the majority of linguists. This tendency is frequently seen in the languages which incline to over generalize the triliteral phenomenon, i. e., to transfer the biliteral roots to the triliteral room, that is, to subject it to the predominant pattern regarding the r
... Show MoreGlobalization has occupied a great deal of studies, research and literature, in addition to being a phenomenon that has imposed itself firmly on the ground. Globalization is considered the main feature of the current moment in today's world. The world is now transforming in an unprecedented way under noticeable titles of successive waves of knowledge and technology.The current research aims to identify the effects of globalization on the variables and their political, social, media and cultural dimensions, as well as culture of consumption and cultural identity.The theoretical framework included two sections: the first is the concept of globalization, its history and its dimensions, and the second is the modernity in contemporary Europea
... Show MoreIn this paper a prey-predator-scavenger food web model is proposed and studied. It is assumed that the model considered the effect of harvesting and all the species are infected by some toxicants released by some other species. The stability analysis of all possible equilibrium points is discussed. The persistence conditions of the system are established. The occurrence of local bifurcation around the equilibrium points is investigated. Numerical simulation is used and the obtained solution curves are drawn to illustrate the results of the model. Finally, the nonexistence of periodic dynamics is discussed analytically as well as numerically.
Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne
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