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A Tri-Gene Ontology Migration Operator for Improving the Performance of Meta-heuristics in Complex Detection Problems
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      Detecting protein complexes in protein-protein interaction (PPI) networks is a challenging problem in computational biology. To uncover a PPI network into a complex structure, different meta-heuristic algorithms have been proposed in the literature. Unfortunately, many of such methods, including evolutionary algorithms (EAs), are based solely on the topological information of the network rather than on biological information. Despite the effectiveness of EAs over heuristic methods, more inherent biological properties of proteins are rarely investigated and exploited in these approaches. In this paper, we proposed an EA with a new mutation operator for complex detection problems. The proposed mutation operator is formulated under four expressions depending on the type of gene sub-ontology. To demonstrate the performance of the proposed evolutionary based complex detection algorithm, the Saccharomyces Cerevisiae (yeast) PPI network is used in the evaluation. The results reveal that the proposed algorithm achieves more accurate complex structures than the counterpart heuristic algorithms and the canonical evolutionary algorithm based on the topological-aware mutation operator.

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Publication Date
Thu May 18 2023
Journal Name
Journal Of Engineering
Improving Fatigue Life of Bolt Adapter of Prosthetic SACH Foot
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In this research an analysis for improving the fatigue behavior (safety factor of fatigue) of non- articular prosthetic foot (SACH) in the region (Bolt Adapter).The laser peening was carried to the fatigue specimens to improving the fatigue properties of bolt’s material. The tests of mechanical properties and fatigue behavior were carried for material that the bolt manufacture from it, a region where the failure occur and inserted of these properties to the program of engineering analysis (Ansys) to calculate the safety factor of fatigue. The results showed that the safety factor after hardening by laser is increased by 42.8%.

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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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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
IFFT-Based Microwave Non-Destructive Testing for Delamination Detection and Thickness Estimation
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Publication Date
Thu Apr 20 2023
Journal Name
Fire
An Efficient Wildfire Detection System for AI-Embedded Applications Using Satellite Imagery
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Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

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Publication Date
Mon Jan 01 2024
Journal Name
Baghdad Science Journal
Artificial Neural Network and Latent Semantic Analysis for Adverse Drug Reaction Detection
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Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

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Publication Date
Thu May 05 2022
Journal Name
Journal Of Taibah University For Science
Innovative economic anthocyanin dye source for enhancing the performance of dye-sensitized solar cell
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Publication Date
Sun Mar 20 2016
Journal Name
Al-academy
Indicative coding of the actor’s performance in the Iraqi theater show
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Publication Date
Wed Jan 01 2020
Journal Name
Biochemical Cellular Archives
Synthesis, characterizationand antibacterialactivity of PVA cyclic acetal (Cu) metal complex / chitosan polymer blend
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Polyacetal was synthesized from the reaction of PVA with para-methyoxy benzaldehyde. Polymer metal complexwas prepared by reaction with Cu, polymer blend with Chitosan was prepared through the technique of solution casting method.All prepared compounds have been characterized through FT-IR, DSC, SEM as well as the Biological activity. The FT-IR results indicated the formation of polyacetal. The DSC results indicated the thermal stability regarding prepared polymer, polymermetal complex and Chitosan polymer blends. Antibacterial potential related to synthesized polyacetal, its metal complex andChitosan blend against four types of bacteria namely, Staphylococcus aureas, Psedomonas aeruginosa, Bacillus subtilis, Escherichia coli was examined a

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Publication Date
Thu Aug 31 2023
Journal Name
Journal Of Wasit For Science And Medicine
Synthesis, Characterization and Antimicrobial Study of Polyacetal-Co complex/Nano Chitosan polymer blend
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Polycyclicacetal was prepared by the reaction of PEG with 4-nitrobenzaldehyde. Cobalt was used for producing a polymer metal complex and solution casting was used to produce a polymer blend including nano chitosan. All produced compounds have been characterized by FT-IR, DSC/ TGA, and SEM techniques as well as biological activity. The production of polyacetal is illustrated by the FT-IR analysis. The DSC/TGA results indicate the prepared polymer blends' thermal stability. Staphylococcus aureas, Klebsiella pneumoniae, Bacillus subtilis, and Escherichia coli were the four types of bacteria selected to study and evaluate the antibacterial activity of produced polyacetal, its metal complex, and polymer blend. Results indicates that ther

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Publication Date
Sun Jun 01 2025
Journal Name
Iranian Journal Of Microbiology
Prevalence of Helicobacter pylori infection among dyspeptic patients attending Baghdad medical city complex
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Background and Objectives: Dyspepsia is a disorder characterized by difficulty in digestion and represents a major health concern. Therefore, it is crucial to identify functional dyspepsia linked to Helicobacter pylori (H. pylori). This research aimed to determine the prevalence of H. pylori among patients with dyspepsia and to examine the potential risk factors associated with the infection. Materials and Methods: From August 14th to September 21st, 2024, a total of 105 patients with dyspepsia, who attended the Central Laboratory of Baghdad Medical City Complex (Iraq), were enrolled in this study. Data on nonsteroidal anti-inflam- matory drugs (NSAIDs), smoking, family history, fasting habits and frequent fast food consumption wer

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