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Parallel Particle Swarm Optimization Algorithm for Identifying Complex Communities in Biological Networks
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    Identification of complex communities in biological networks is a critical and ongoing challenge since lots of network-related problems correspond to the subgraph isomorphism problem known in the literature as NP-hard. Several optimization algorithms have been dedicated and applied to solve this problem. The main challenge regarding the application of optimization algorithms, specifically to handle large-scale complex networks, is their relatively long execution time. Thus, this paper proposes a parallel extension of the PSO algorithm to detect communities in complex biological networks. The main contribution of this study is summarized in three- fold; Firstly, a modified PSO algorithm with a local search operator is proposed to detect complex biological communities with high quality. Secondly, the variability in the capability of PSO to extract community structure in biological networks is studied when different types of crossover operators are used. Finally, to reduce the computational time needed to solve this problem, especially when detecting complex communities in large-scale biological networks, we have implemented parallel computing to execute the algorithm. The performance of the proposed algorithm was tested and evaluated on two real biological networks. The experimental results showed the effective performance of the proposed algorithm when using single-point crossover operator, and its superiority over other counterpart algorithms. Moreover, the use of parallel computing in the proposed algorithm representation has greatly reduced the computational time required for its execution.

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Publication Date
Mon Jun 01 2015
Journal Name
Journal Of Engineering
Artificial Neural Networks Modeling of Total Dissolved Solid in the Selected Locations on Tigris River, Iraq
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The study aims to predict Total Dissolved Solids (TDS) as a water quality indicator parameter at spatial and temporal distribution of the Tigris River, Iraq by using Artificial Neural Network (ANN) model. This study was conducted on this river between Mosul and Amarah in Iraq on five positions stretching along the river for the period from 2001to 2011. In the ANNs model calibration, a computer program of multiple linear regressions is used to obtain a set of coefficient for a linear model. The input parameters of the ANNs model were the discharge of the Tigris River, the year, the month and the distance of the sampling stations from upstream of the river. The sensitivity analysis indicated that the distance and discharge

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Publication Date
Tue Dec 01 2009
Journal Name
2009 7th International Conference On Information, Communications And Signal Processing (icics)
The effect of traffic load on using XCAST based routing protocol in wireless ad hoc networks
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Publication Date
Thu May 18 2023
Journal Name
Journal Of Engineering
A Comparative Study of Single-Constraint Routing in Wireless Mesh Networks Using Different Dynamic Programming Algorithms
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Finding the shortest route in wireless mesh networks is an important aspect. Many techniques are used to solve this problem like dynamic programming, evolutionary algorithms, weighted-sum techniques, and others. In this paper, we use dynamic programming techniques to find the shortest path in wireless mesh networks due to their generality, reduction of complexity and facilitation of numerical computation, simplicity in incorporating constraints, and their onformity to the stochastic nature of some problems. The routing problem is a multi-objective optimization problem with some constraints such as path capacity and end-to-end delay. Single-constraint routing problems and solutions using Dijkstra, Bellman-Ford, and Floyd-Warshall algorith

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Publication Date
Wed Jan 01 2025
Journal Name
Lecture Notes In Networks And Systems
Automated Detection of Dubas Bug Infestation in Palm Trees Using Deep Learning with Residual Neural Networks
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Automated detection of Dubas palm infestation by image processing techniques has practical significance as it can improve agricultural efficiency, increase crop yield and quality, protect the environment, and provide data-driven insights. It also reduces the human effort required for pest control and enhances sustainability. In this study, we aimed to automate the detection of Dubas bug infestation in palm trees using deep learning with transfer learning residual neural networks. Based on four models: InceptionResNetV2, ResNet18, ResNet50, and ResNet101, the data used in this study were obtained by drone photography, many images were taken, and then the infected area was extracted. Using two types of data, 185 infected images and 185 health

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Publication Date
Sun Jan 01 2023
Journal Name
1st International Conference On Achieving The Sustainable Development Goals
Empowerment and its impact on affordable housing sustainable planning : A case study of Al-Sultan housing complex in Al-Najaf
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The past decades of the last century and until now have witnessed many crises in the housing sector, and these crises were the result of many problems that led to a weak quality and quantity of housing stock, especially for lowincome people, and Goal 11 of the Sustainable Development Goals states, "Make cities and human settlements inclusive, safe, and capable of enduring and being sustainable, we have prepared this research that attempts to answer the following question: Is the current empowerment in the housing sector real and efficient support for people with limited income? The research assumes that empowerment in the housing sector has an important and effective role in reducing housing problems, the research aims to reach the most imp

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Publication Date
Wed May 31 2023
Journal Name
Iraqi Geological Journal
A Survey of Infill Well Location Optimization Techniques
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The maximization of the net present value of the investment in oil field improvements is greatly aided by the optimization of well location, which plays a significant role in the production of oil. However, using of optimization methods in well placement developments is exceedingly difficult since the well placement optimization scenario involves a large number of choice variables, objective functions, and restrictions. In addition, a wide variety of computational approaches, both traditional and unconventional, have been applied in order to maximize the efficiency of well installation operations. This research demonstrates how optimization approaches used in well placement have progressed since the last time they were examined. Fol

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Publication Date
Wed Dec 15 2021
Journal Name
Journal Of Physics: Conference Series
Pretreated Fishbone as Low Cost-Adsorbent for Cationic Dye Adsorption from Aqueous Solutions: Equilibrium, Optimization, Kinetic and Thermodynamic Study
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The present study investigated the use of pretreated fish bone (PTFB) as a new surface, natural waste and low-cost adsorbent for the adsorption of Methyl green (MG, as model toxic basic dye) from aqueous solutions. The functional groups and surface morphology of the untreated fish bone (FB) and pretreated fish bone were characterized using Fourier transform infrared (FTIR), scanning electron microscopy (SEM) and Energy dispersive X-ray spectroscopy (EDS),respectively. The effect of operating parameters including contact time, pH, adsorbent dose, temperature, and inorganic salt was evaluated. Langmuir, Freundlich and Temkin adsorption isotherm models were studied and the results showed that the adsorption of basic dye followed Freundlich iso

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Publication Date
Fri Jul 12 2024
Journal Name
Biomass Conversion And Biorefinery
Chitosan-triphosphate/food grade algae/montmorillonite clay for adsorptive removal of methyl violet 2B dye removal: Behnken design optimization
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Publication Date
Thu Oct 30 2025
Journal Name
International Journal Of Environmental Analytical Chemistry
Integrated biomass algae @ cross-linked pyromellitic dianhydride chitosan biocomposite for methyl violet 2B adsorption: modelling and experiment design optimization
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A new adsorbent was developed by integrating algae biomass (AG) into a chitosan (CN) matrix, followed by structural enhancement via crosslinking with pyromellitic dianhydride (PMDA) through a hydrothermal synthesis approach. This process resulted in the formation of a robust AG@CN-PMDA composite with improved physicochemical characteristics suitable for advanced adsorption applications. The AG@CN-PMDA composite was evaluated for its efficiency in removal of the cationic dye methyl violet 2B (MV 2B) from aqueous solution. The adsorption process was refined through the Box-Behnken design (RSM-BBD), evaluating three essential parameters: adsorbent dosage (A: 0.02–0.1 g/100 mL), pH (B: 4–10), and time (C: 5–20 min). The ideal conditions f

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Publication Date
Fri Jan 24 2025
Journal Name
Journal Of The American Oil Chemists' Society
Development of natural deep eutectic solvent‐assisted liquid–liquid extraction method for soap removal from biodiesel: Optimization and kinetics
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Abstract<p>The soap content in biodiesel is an important challenge during the production and purification processing of biodiesel. Natural deep eutectic solvents (NADES) have recently attracted considerable interest as an environmentally suitable substitute for traditional solvents in the biodiesel industry. This work investigates the soap removal from the contaminated biodiesel using NADES. Eight choline chloride‐based deep eutectic solvents (DESs) were screened using the conductor‐like screening model for real solvents (COSMO‐RS) to identify the most suitable solvent for soap removal and were validated experimentally. The effect of NADES molar ratio, NADES:biodiesel ratio, mixing speed and extraction ti</p> ... Show More
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