A network (or formally a graph) can be described by a set of nodes and a set of edges connecting these nodes. Networks model many real-world phenomena in various research domains, such as biology, engineering and sociology. Community mining is discovering the groups in a network where individuals group of membership are not explicitly given. Detecting natural divisions in such complex networks is proved to be extremely NP-hard problem that recently enjoyed a considerable interest. Among the proposed methods, the field of evolutionary algorithms (EAs) takes a remarkable interest. To this end, the aim of this paper is to present the general statement of community detection problem in social networks. Then, it visits the problem as an optimization problem where a modularity-based ( ) and normalized mutual information ( ) metrics are formulated to describe the problem. An evolutionary algorithm is then expressed in the light of its characteristic components to tackle the problem. The presentation will highlight the possible alternative that can be adopted in this study for individual representation, fitness evaluations, and crossover and mutation operators. The results point out that adopting as a fitness function carries out more correct solutions than adopting the modularity function . Moreover, the strength of mutation has a background role. When coupled with non elite selection, increasing mutation probability could results in better solutions. However, when elitism is used, increasing mutation probability could bewilder the behavior of EA.
A network (or formally a graph) can be described by a set of nodes and a set of edges connecting these nodes. Networks model many real-world phenomena in various research domains, such as biology, engineering and sociology. Community mining is discovering the groups in a network where individuals group of membership are not explicitly given. Detecting natural divisions in such complex networks is proved to be extremely NP-hard problem that recently enjoyed a considerable interest. Among the proposed methods, the field of evolutionary algorithms (EAs) takes a remarkable interest. To this end, the aim of this paper is to present the general statement of community detection problem in social networks. Then, it visits the problem as an optimization problem where a modularity-based ( ) and normalized mutual information ( ) metrics are formulated to describe the problem. An evolutionary algorithm is then expressed in the light of its characteristic components to tackle the problem. The presentation will highlight the possible alternative that can be adopted in this study for individual representation, fitness evaluations, and crossover and mutation operators. The results point out that adopting as a fitness function carries out more correct solutions than adopting the modularity function . Moreover, the strength of mutation has a background role. When coupled with non elite selection, increasing mutation probability could results in better solutions. However, when elitism is used, increasing mutation probability could bewilder the behavior of EA.
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Objective: Matrix tablet approach is one of the delivery systems intended for poorly water-soluble drugs, like candesartan cilexetil (CC). CC is a class II drug used for the treatment of hypertension. Methods: Matrix tablets from (F1x to F18z) were prepared in the presence of β‑cyclodextrin. Matrix tablet formulation ensures control release of the drug and higher dissolution by β‑cyclodextrin. Fourier transform infrared spectroscopy (FTIR), and differential scanning calorimetry (DSC) were used to study compatibility. Results: The angle of repose determination showed good flow for most of the formulas, besides having good compressibility. Weight variation test for all formulas showed accepted value. Drug content measurement sho
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Objective: Matrix tablet approach is one of the delivery systems intended for poorly water-soluble drugs, like candesartan cilexetil (CC). CC is a class II drug used for the treatment of hypertension. Methods: Matrix tablets from (F1x to F18z) were prepared in the presence of β‑cyclodextrin. Matrix tablet formulation ensures control release of the drug and higher dissolution by β‑cyclodextrin. Fourier transform infrared spectroscopy (FTIR), and differential scanning calorimetry (DSC) were used to study compatibility. Results: The angle of repose determination showed good flow for most of the formulas, besides having good compressibility. Weight variation test for all formulas showed accepted value. Drug content measurement sho
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Polyacetal was synthesized from the reaction of Polyethylene glycol with4- dimethylaminobenzaldehyde.Polymer metal complex was synthesized by the reaction with Ag+; polymer blend with polyvinyl alcohol was synthesized solution casting technique. All synthesized compounds were characterized by FT-IR in addition to the antimicrobial activity. The FT-IR spectra indicate the formation of the polyacetal. The DSC resultsindicatethe thermal stability regarding the synthesized polymer blends. The synthesized polyacetal, its metal complex and PA blend against four types of bacteria (gram+ve) Staphylococcus aureas, Bacillus subtilis and (gram –ve)Klebsiella pneumoniae, Escherichia Coli w
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This article aims to establish and evaluate standards for critical equipment and materials in highway projects in Iraq. Delphi technique has been used to analyze, explore, and discover the main criteria and sub-criteria that affect equipment and materials in highway construction projects in Iraq. To determine the correct response to the criteria presented in this study, a program (IBM, SPSS/V25) was used to assess the main criteria and sub-criteria using the mean score (MS) and standard deviation (SD) technique, as well as to check reliability using Cronbach's alpha factor (α). The experts' qualifications and the extent to which the person is ready to commit are both important factors in panel selection. The design of a
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based search on two variables two main (Administrative empowerment ) and (technical innovation) target detection relationship and influence between the five dimensions (the delegation of authority , personnel training , effective communication, work teams , motivating employees) and
(technical innovation) conducted research in General Company for electrical Industries , and through the sample included the views of managers in the various administrative levels poll .
And adopted a researcher at a major tool for data collection is questionnaire designed to find, as was the contents of the questionnaire analysis according to the Statistical Information System ( Spss), The (55) to identi
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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
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<p> Traditionally, wireless networks and optical fiber Networks are independent of each other. Wireless networks are designed to meet specific service requirements, while dealing with weak physical transmission, and maximize system resources to ensure cost effectiveness and satisfaction for the end user. In optical fiber networks, on the other hand, search efforts instead concentrated on simple low-cost, future-proofness against inheritance and high services and applications through optical transparency. The ultimate goal of providing access to information when needed, was considered significantly. Whatever form it is required, not only increases the requirement sees technology convergence of wireless and optical networks but
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There are large numbers of weakness in the generated keys of security algorithms. This paper includes a new algorithm to generate key of 5120 bits for a new proposed cryptography algorithm for 10 rounds that combine neural networks and chaos theory (1D logistic map). Two methods of neural networks (NN) are employed as Adaline and Hopfield and the results are combined through several sequential operation. Carefully integrating high quality random number generators from neural networks and chaos theory to obtain suitable key for randomness and complexity.