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Community Detection in Modular Complex Networks Using an Improved Particle Swarm Optimization Algorithm
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     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 local improvement operator to effectively discover community structure in the modular complex networks when employing the modularity density metric as a single-objective function. The framework of the proposed algorithm consists of three main steps: an initialization strategy, a movement strategy based on perturbation genetic operators, and an improvement operator. The key idea behind the improvement operator is to determine and reassign the complex network nodes that are located in the wrong communities if the majority of their topological links do not belong to their current communities, making it appear that these nodes belong to another community. The performance of the proposed algorithm has been tested and evaluated when applied to publicly-available modular complex networks generated using a flexible and simple benchmark generator. The experimental results showed the effectiveness of the suggested method in discovering community structure over modular networks of different complexities and sizes.

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Publication Date
Mon Sep 13 2021
Journal Name
Al-khwarizmi Engineering Journal
Optimization of Cutting Parameters for Milling Process of (4032) Al-Alloy using Taguchi-Based Grey Relational Analysis
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The objective of this work is to study the influence of end milling cutting process parameters, tool material and geometry on multi-response outputs for 4032 Al-alloy. This can be done by proposing an approach that combines Taguchi method with grey relational analysis. Three cutting parameters have been selected (spindle speed, feed rate and cut depth) with three levels for each parameter. Three tools with different materials and geometry have been also used to design the experimental tests and runs based on matrix L9. The end milling process with several output characteristics is solved using a grey relational analysis. The results of analysis of variance (ANOVA) showed that the major influencing parameters on multi-objective response w

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Publication Date
Thu Apr 01 2021
Journal Name
Journal Of Engineering Science And Technology
Applying box-behnken design with statistical optimization for removal vat orange dye from aqueous solution using kaolin
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Publication Date
Tue Jan 01 2019
Journal Name
International Journal Of Electrochemical Science
Effect of Electrolysis Parameters on the Specific Surface Area of Nickel Powder: Optimization using Box-Behnken Design
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Publication Date
Tue Sep 01 2020
Journal Name
Journal Of Engineering
Optimization and Modeling the Performance of a Mediator-less Microbial Fuel Cell using Butler-Volmer-Monod Model
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In this study, a one-dimensional model represented by Butler-Volmer-Monod (BVM) model was proposed to compute the anode overpotential and current density in a mediator-less MFC system. The system was fueled with various organic loadings of real field petroleum refinery oily sludge to optimize the favorable organic loading for biomass to operate the suggested system. The increase in each organic loading showed higher resistance to electrons transport to the anode represented by ohmic loss. On the contrary, both activation and mass transfer losses exhibited a noticeable decrement upon the increased organic loadings. However, current density was improved throughout all increased loads achieving a maximum current density of 5.2 A/m3

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Publication Date
Thu Feb 28 2019
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
The Use Of Artificial Neural Networks In Developing The Role Of Auditor In Discovering Fundamental Errors: An Applied Research In General Company for Electrical Industries and Nasr General Company for Mechanical Industries
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Artificial neural networks usage, as a developed technique, increased in many fields such as Auditing business. Contemporary auditor should cope with the challenges of the technology evolution in the business environment by using computerized techniques such as Artificial neural networks, This research is the first work made in the field of modern techniques of the artificial neural networks in the field of auditing; it is made by using thesample of neural networks as a sample of the artificial multi-layer Back Propagation neural networks in the field of detecting fundamental mistakes of the financial statements when making auditing. The research objectives at offering a methodology for the application of theartificial neural networks wi

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Publication Date
Sat Nov 11 2023
Journal Name
Journal Of Dental Research, Dental Clinics, Dental Prospects
Investigating the effect of Er,Cr:YSGG laser agitation of sodium hypochlorite on the removal of mature biofilm in the complex root canal systems using atomic force microscopy
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Background. Endodontic infections caused by remaining biofilm following disinfection with chemical fluids encourage secondary bacterial infection; hence, employing laser pulses to activate the fluids is advised to improve microbial biofilm clearance. This study investigated the performance of Er,Cr:YSGG laser in photon-induced photoacoustic streaming (PIPS) agitation of 5.25% sodium hypochlorite (NaOCl) to enhance the removal of mature Enterococcus faecalis (E. faecalis) biofilms in complex root canal systems. Methods. The mesial roots of the lower first and second molars were separated and inoculated with E. faecalis bacteria for 30 days. The roots were irrigated with 5.25% NaOCl, some of them were agitated with passive ultrasonic

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Publication Date
Wed Apr 01 2020
Journal Name
Plant Archives
Land cover change detection using satellite images based on modified spectral angle mapper method
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This research depends on the relationship between the reflected spectrum, the nature of each target, area and the percentage of its presence with other targets in the unity of the target area. The changes occur in Land cover have been detected for different years using satellite images based on the Modified Spectral Angle Mapper (MSAM) processing, where Landsat satellite images are utilized using two software programming (MATLAB 7.11 and ERDAS imagine 2014). The proposed supervised classification method (MSAM) using a MATLAB program with supervised classification method (Maximum likelihood Classifier) by ERDAS imagine have been used to get farthest precise results and detect environmental changes for periods. Despite using two classificatio

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Publication Date
Fri Jun 01 2018
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Detection of Increasing of Tropospheric NO2 over some Iraqi Cities by using Satellite Data
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This paper focus on study the variations of monthly tropospheric NO2 concentrations over three Iraqi cities Baghdad (33.3° N, 44.4° E), Basrah (30.56° N, 47.8° E) and Erbil (36.3° N, 44.06° E). Monthly NO2 retrievals from the Ozone Monitoring Instrument (OMI) onboard Aura satellite during the period from October 2004 to March 2013 have been used.  The results show a high monthly and annual NO2 concentrations at Baghdad than Basra and Erbil may be attribute to high densely populations and a high economic activity. During the whole period, Baghdad, Basrah and Erbil were exhibited an average of NO2 (8.1±2.5), (3.7±1.3) and (3.3±1.7) in unit 1015 molecules

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Publication Date
Tue Sep 01 2020
Journal Name
Baghdad Science Journal
Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection
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            In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the

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Publication Date
Tue Sep 01 2020
Journal Name
Baghdad Science Journal
Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection
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In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection

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