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  1. 1
    Musical instrument identification using Convolutional Neural Network (CNN) algorithm / Muhammad Nur Azri Irfan Abdul Rahman

    Musical instrument identification using Convolutional Neural Network (CNN) algorithm / Muhammad Nur Azri Irfan Abdul Rahman by Abdul Rahman, Muhammad Nur Azri Irfan

    Published 2025
    “…This approach tried to overcome the limitations of the manual method and traditional algorithm, which tends to fail with the diverse dataset, diverse visual features, and scalability. …”
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  2. 2
    A framework of test case prioritisation in regression testing using particle swarm-artificial bee colony algorithm

    A framework of test case prioritisation in regression testing using particle swarm-artificial bee colony algorithm by Ba-Quttayyan, Bakr Salim Abdullah

    Published 2024
    “…This research employed a modified version of the Design Science Research Methodology (DSRM), streamlined into five stages: problem identification, theoretical study, framework development, evaluation, and reporting. …”
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  3. 3
    An ensemble learning method for spam email detection system based on metaheuristic algorithms

    An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

    Published 2015
    “…In order to address the challenges that mentioned above in this study, in the first phase, a novel architecture based on ensemble feature selection techniques include Modified Binary Bat Algorithm (NBBA), Binary Quantum Particle Swarm Optimization (QBPSO) Algorithm and Binary Quantum Gravita tional Search Algorithm (QBGSA) is hybridized with the Multi-layer Perceptron (MLP) classifier in order to select relevant feature subsets and improve classification accuracy. …”
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  4. 4
    A BIOLOGICALLY INSPIRED OBJECT RECOGNITION SYSTEM

    A BIOLOGICALLY INSPIRED OBJECT RECOGNITION SYSTEM by Hamada , R. H. Al-Absi, Azween, Abdullah

    Published 2010
    “…In fact, it has been referred to as the most important problem in machine or computer vision. Researchers have developed many algorithms to solve the problem of object recognition that are machine vision motivated. …”
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  5. 5
    BIOLOGICALLY INSPIRED OBJECT RECOGNITION SYSTEM

    BIOLOGICALLY INSPIRED OBJECT RECOGNITION SYSTEM by HASSAN AL-ABSI, HAMADA RASHEED

    Published 2010
    “…In fact, it has been referred to as the most important problem in machine or computer vision. Researchers have developed many algorithms to solve the problem of object recognition that are machine vision motivated. …”
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  6. 6
    Analysis and modeling of database storage optimization power consumption usage for green data centers

    Analysis and modeling of database storage optimization power consumption usage for green data centers by Wan Noor Hamiza, Wan Ali

    Published 2014
    “…The proxies can be discovered by using a developed algorithm of functional dependencies (FDs) called as TANE algorithm. …”
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  7. 7
    Development of a lower-extremities exoskeleton for postural-sway in sit-to-stand movement

    Development of a lower-extremities exoskeleton for postural-sway in sit-to-stand movement by Lee, Jun Han

    Published 2025
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  8. 8
    Fuzzy-based multi-agent approach for reliability assessment and improvement of power system protection

    Fuzzy-based multi-agent approach for reliability assessment and improvement of power system protection by Nadheer Abdulridha, Shalash

    Published 2015
    “…The second agent is a reliability evaluation agent that uses a recursive algorithm to predict the suitability generator based on the frequency and duration reliability indices in each state while the third agent is the storage and transfer of data between the other two agents. …”
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  9. 9
    Evolutionary cost-cognizant regression test case prioritization for object-oriented programs

    Evolutionary cost-cognizant regression test case prioritization for object-oriented programs by Bello, AbdulKarim

    Published 2019
    “…Afterward evolutionary algorithm (EA) was employed to prioritize test cases based on the rate severity of fault detection per unit test cost. …”
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