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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
Sat Dec 01 2018
Journal Name
Applied Soft Computing
A new evolutionary algorithm with locally assisted heuristic for complex detection in protein interaction networks
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Publication Date
Thu Dec 05 2019
Journal Name
Advances In Intelligent Systems And Computing
An Enhanced Evolutionary Algorithm for Detecting Complexes in Protein Interaction Networks with Heuristic Biological Operator
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Publication Date
Sat Dec 31 2022
Journal Name
International Journal Of Intelligent Engineering And Systems
Using Three-Dimensional Logistic Equations and Glowworm Swarm Optimization Algorithm to Generate S-Box
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Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
A Parallel Adaptive Genetic Algorithm for Job Shop Scheduling Problem
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Publication Date
Sun Jan 20 2019
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
A parallel Numerical Algorithm For Solving Some Fractional Integral Equations
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In this study, He's parallel numerical algorithm by neural network is applied to type of integration of fractional equations is Abel’s integral equations of the 1st and 2nd kinds. Using a Levenberge – Marquaradt training algorithm as a tool to train the network. To show the efficiency of the method, some type of Abel’s integral equations is solved as numerical examples. Numerical results show that the new method is very efficient problems with high accuracy.

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Publication Date
Tue Feb 01 2022
Journal Name
Baghdad Science Journal
Retina Based Glowworm Swarm Optimization for Random Cryptographic Key Generation
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The biometric-based keys generation represents the utilization of the extracted features from the human anatomical (physiological) traits like a fingerprint, retina, etc. or behavioral traits like a signature. The retina biometric has inherent robustness, therefore, it is capable of generating random keys with a higher security level compared to the other biometric traits. In this paper, an effective system to generate secure, robust and unique random keys based on retina features has been proposed for cryptographic applications. The retina features are extracted by using the algorithm of glowworm swarm optimization (GSO) that provides promising results through the experiments using the standard retina databases. Additionally, in order t

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Publication Date
Mon Aug 26 2019
Journal Name
Iraqi Journal Of Science
Heuristic Modularity for Complex Identification in Protein-Protein Interaction Networks
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     Due to the significant role in understanding cellular processes, the decomposition of Protein-Protein Interaction (PPI) networks into essential building blocks, or complexes, has received much attention for functional bioinformatics research in recent years. One of the well-known bi-clustering descriptors for identifying communities and complexes in complex networks, such as PPI networks, is modularity function.   The contribution of this paper is to introduce heuristic optimization models that can collaborate with the modularity function to improve its detection ability. The definitions of the formulated heuristics are based on nodes and different levels of their neighbor properties.  The modulari

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Publication Date
Sun Oct 01 2017
Journal Name
Applied Energy
Hourly yield prediction of a double-slope solar still hybrid with rubber scrapers in low-latitude areas based on the particle swarm optimization technique
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Publication Date
Thu Feb 01 2024
Journal Name
Journal Of Engineering
Identifying Failure Factors in the Implementation of Residential Complex Projects in Iraq
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Residential complexes have witnessed a great demand in most countries worldwide, as they are one of the main infrastructure elements, in addition to achieving a developed urban landscape. However, complex residential projects in developing countries face various factors that could be improved in their implementation, especially in Iraq. Sixty-two experts in residential complex projects were interviewed and surveyed to verify these projects' failure factors,. Fifty-one factors were the main failure factors, divided into four main components (leadership, management system, external forces, and project resources). The Relatively Important Index (RII) is used to determine the relative importance factors and obtain the top tw

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Publication Date
Wed Aug 30 2023
Journal Name
Iraqi Journal Of Science
Epidemiological Complex Networks: A Survey
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     In this review paper, several research studies were surveyed to assist future researchers to identify available techniques in the field of infectious disease modeling across complex networks. Infectious disease modelling is becoming increasingly important because of the microbes and viruses that threaten people’s lives and societies in all respects. It has long been a focus of research in many domains, including mathematical biology, physics, computer science, engineering, economics, and the social sciences, to properly represent and analyze spreading processes. This survey first presents a brief overview of previous literature and some graphs and equations to clarify the modeling in complex networks, the detection of soc

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