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A review of heuristics and metaheuristics for community detection in complex networks: Current usage, emerging development and future directions
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Sensibly highlighting the hidden structures of many real-world networks has attracted growing interest and triggered a vast array of techniques on what is called nowadays community detection (CD) problem. Non-deterministic metaheuristics are proved to competitively transcending the limits of the counterpart deterministic heuristics in solving community detection problem. Despite the increasing interest, most of the existing metaheuristic based community detection (MCD) algorithms reflect one traditional language. Generally, they tend to explicitly project some features of real communities into different definitions of single or multi-objective optimization functions. The design of other operators, however, remains canonical lacking any intense interest to reflect the domain knowledge. Moreover, all the published reviews did not make any direct effort to link heuristic and metaheuristic based community detection approaches, rather, they simply state them separately. The review introduced in this paper attempts to address this issue. Mainly, we review the main heuristic and metaheuristic based community detection algorithms. Then, we introduce two new taxonomies for community detection algorithms: hybrid metaheuristic and hyper heuristic that can serve as common grounds for designing a collection of new and more effective MCD algorithms. To this end, we introduce four new systematic frameworks integrating both heuristic and metaheuristic algorithms, illustrating the possible issues that would fuel the desire for researchers to direct their future interest towards developing more effective community detection instances from the context of these frameworks.

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Publication Date
Thu Jun 25 2020
Journal Name
International Journal Of Drug Delivery Technology
Development and Characterization of Controlled Release Tablets of Candesartan Cilexetil/ β‑Cyclodextrin Inclusion Complex
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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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Publication Date
Thu Jun 25 2020
Journal Name
International Journal Of Drug Delivery Technology
Development and Characterization of Controlled Release Tablets of Candesartan Cilexetil/ β‑Cyclodextrin Inclusion Complex
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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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Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Journal Of Science
An Evolutionary Algorithm with Gene Ontology-Aware Crossover Operator for Protein Complex Detection
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     Evolutionary algorithms (EAs), as global search methods, are proved to be more robust than their counterpart local heuristics for detecting protein complexes in protein-protein interaction (PPI) networks. Typically, the source of robustness of these EAs comes from their components and parameters. These components are solution representation, selection, crossover, and mutation. Unfortunately, almost all EA based complex detection methods suggested in the literature were designed with only canonical or traditional components. Further, topological structure of the protein network is the main information that is used in the design of almost all such components. The main contribution of this paper is to formulate a more robust E

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Publication Date
Thu Feb 01 2024
Journal Name
Journal Of Orthodontic Science
Presurgical management trends and nasoalveolar molding usage for infants with cleft lip and palate in the capital of a developing country
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BACKGROUNDS Nasoalveolar molding (NAM) application is among presurgical management (PSM) techniques used for infants with cleft lip and palate (CLP). It helps to approximate the palatal cleft and to reshape the nasoalveolar complex prior to primary lip repair. This study aimed to explore types of PSM and the dental speciality provision for infants with CLP in Baghdad. The status of NAM usage and surgeons’ perceptions toward NAM usage were assessed. MATERIALS AND METHODS This is a cross-sectional paper-based questionnaire study that collected responses of surgeons perform primary lip and nose repair regarding PSM. The questionnaire was distributed amongst public and private hospitals in Baghdad. Twenty surgeons were enrolled (only those su

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Publication Date
Wed Apr 02 2014
Journal Name
Journal Of Theoretical And Applied Information Technology
TUMOR BRAIN DETECTION THROUGH MR IMAGES: A REVIEW OF LITERATURE
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Today’s modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. It is used for analysis of human organs to replace surgery. For brain tumor detection, image segmentation is required. For this purpose, the brain is partitioned into two distinct regions. This is considered to be one of the most important but difficult part of the process of detecting brain tumor. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Earlier, a variety of algorithms were developed for segmentation of MRI images by usin

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Publication Date
Wed May 04 2022
Journal Name
Int. J. Nonlinear Anal. Appl.
Knee Meniscus Segmentation and Tear Detection Based On Magnitic Resonacis Images: A Review of Literature
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The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when

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Publication Date
Thu Dec 01 2022
Journal Name
Iraqi Journal Of Science
PLAGIARISM DETECTION SYSTEM IN SCIENTIFIC PUBLICATION USING LSTM NETWORKS
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Publication Date
Tue Dec 06 2022
Journal Name
International Journal Of Professional Business Review
A Comparative Study to Reveal Earnings Management in Emerging Markets: Evidence from Tunisia and Iraq
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Purpose: The purpose of the study is to compare and evaluate Earnings Management in Tunisia and Iraq.   Theoretical framework: Earnings Management is an important topic that has been studied by a significant number of researchers, as well as those who are interested in the accounting profession. Earnings Management has gotten a lot of attention from academics, professionals, and other interested parties in recent years (e.g. Kliestik et al., 2020; Rahman et al., 2021; Gamra &Ellouze, 2021)   Design/methodology/approach: The sample includes ten banks listed on the Bourse of Tunisia and Iraq Stock Exchanges for the year 2017. We have used a model of Kothari et al., (2005) as a tool to measure Earnings Management in both mark

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Publication Date
Wed Oct 03 2018
Journal Name
University Of Plymouth
Green networking: analyses of power consumption of real and complex IFFT/FFT used in Next-Generation Networks and optical Orthogonal Frequency Division Multiplexing
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he Orthogonal Frequency Division Multiplexing is a promising technology for the Next Generation Networks. This technique was selected because of the flexibility for the various parameters, high spectral efficiency, and immunity to ISI. The OFDM technique suffers from significant digital signal processing, especially inside the Inverse/ Fast Fourier Transform IFFT/FFT. This part is used to perform the orthogonality/De-orthogonality between the subcarriers which the important part of the OFDM system. Therefore, it is important to understand the parameter effects on the increase or to decrease the FPGA power consumption for the IFFT/FFT. This thesis is focusing on the FPGA power consumption of the IFFT/FFT uses in the OFDM system. This researc

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Publication Date
Wed Jan 01 2020
Journal Name
International Journal Of Computational Intelligence Systems
Evolutionary Feature Optimization for Plant Leaf Disease Detection by Deep Neural Networks
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