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  1. 1
    Ant colony optimization algorithm for load balancing in grid computing

    Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2012
    “…This research proposes an enhancement of the ant colony optimization algorithm that caters for dynamic scheduling and load balancing in the grid computing system. …”
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  2. 2
    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing

    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

    Published 2011
    “…This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system. …”
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  3. 3
    Resource management in grid computing using ant colony optimization

    Resource management in grid computing using ant colony optimization by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2011
    “…Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources.Stagnation in grid computing system may occur when all jobs require or are assigned to the same resources which lead to the resources having high workload or the time taken to process a job is high.This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system.The algorithm consists of three new mechanisms that organize the work of an ant colony i.e. initial pheromone value mechanism, resource selection mechanism and pheromone update mechanism.The resource allocation problem is modeled as a graph that can be used by the ant to deliver its pheromone.This graph consists of four types of vertices which are job, requirement, resource and capacity that are used in constructing the grid resource management element.The proposed EACO algorithm takes into consideration the capacity of resources and the characteristics of jobs in determining the best resource to process a job.EACO selects the resources based on the pheromone value on each resource which is recorded in a matrix form.The initial pheromone value of each resource for each job is calculated based on the estimated transmission time and execution time of a given job. …”
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  4. 4
    Optimization of blood vessel detection in retina images using multithreading and native code for portable devices

    Optimization of blood vessel detection in retina images using multithreading and native code for portable devices by Tran, Duc Ngoc, Hussin, Fawnizu Azmadi, Yusoff, Mohd Zuki

    Published 2013
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  5. 5
    Performance evaluation of real-time multiprocessor scheduling algorithms

    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
    “…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
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  6. 6
    Implementation of New Improved Round Robin (NIRR) CPU scheduling algorithm using discrete event simulation

    Implementation of New Improved Round Robin (NIRR) CPU scheduling algorithm using discrete event simulation by Chang, Jan Voon

    Published 2015
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  7. 7
    Route Optimization System

    Route Optimization System by Zulkifli, Abdul Hayy

    Published 2005
    “…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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  8. 8
    A New High Order Algorithm With Low Computational Complexity for Electric Field Simulation

    A New High Order Algorithm With Low Computational Complexity for Electric Field Simulation by HASAN, KHATIM, SULAIMAN, JUMAT, ABDUL KARIM, SAMSUL ARIFFIN

    Published 2010
    “…Approach: Our recent research had found a new method with lower complexity and can simulate the problem faster than the existing FDTD algorithm. …”
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  9. 9
    A new high order algorithm with low computational complexity for electric field simulation

    A new high order algorithm with low computational complexity for electric field simulation by Mohammad Khatim Hasan, Jumat Sulaiman, Samsul Ariffin Abdul Karim

    Published 2010
    “…Approach: Our recent research had found a new method with lower complexity and can simulate the problem faster than the existing FDTD algorithm. …”
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  10. 10
    Automatic Number Plate Recognition on android platform: With some Java code excerpts

    Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

    Published 2016
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  11. 11
    Implementation of New Improved Round Robin (NIRR) CPU scheduling algorithm using discrete event simulation

    Implementation of New Improved Round Robin (NIRR) CPU scheduling algorithm using discrete event simulation by Chang, Jan Voon, Ahmad, Idawaty

    Published 2016
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  12. 12
    Development of Algorithm for QoS Model in Cloud Computing

    Development of Algorithm for QoS Model in Cloud Computing by Alfadli, Mohammed Abdullah, Noordin, Mohamad Fauzan, Billah, Md. Masum

    Published 2021
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  13. 13
    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. …”
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  14. 14
    Parallel algorithms for numerical simulations of EHD ion-drag micropump on distributed parallel computing systems

    Parallel algorithms for numerical simulations of EHD ion-drag micropump on distributed parallel computing systems by Shakeel Ahmed, Kamboh

    Published 2014
    “…Hence, the numerical simulations take unacceptably more execution time on sequential computers to run the simulation packages. …”
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  15. 15
    1D Multigrid Solver For Finite Element Method

    1D Multigrid Solver For Finite Element Method by Azhar, Mohamad Amiruddin

    Published 2022
    “…The new algorithm also has been tested using time simulation. …”
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  16. 16
    A new fair marker algorithm for DiffServ networks

    A new fair marker algorithm for DiffServ networks by Elshaikh, M.A., Mohamed, Othman, Subramaniam, Shamala, Md. Desa, Jalil

    Published 2008
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  17. 17
    The Hybrid of WOABAT-IFDO Optimization Algorithm and Its Application in Crowd Evacuation Simulation

    The Hybrid of WOABAT-IFDO Optimization Algorithm and Its Application in Crowd Evacuation Simulation by Hamizan, Sharbini, Roselina, Sallehuddin, Habibollah, Haron

    Published 2023
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    Proceeding
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  18. 18
    Improving resource management with multi-instance broker scheduling algorithm in hierarchical grid computing

    Improving resource management with multi-instance broker scheduling algorithm in hierarchical grid computing by Yahaya, Bakri

    Published 2016
    “…Multi-Instance Broker Scheduling Algorithm (MiBSA) has been proposed as a new scheduling algorithm to get rid of the drawback from the iHLBA algorithm. …”
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  19. 19
    Benchmark simulator with dynamic environment for job scheduling in grid computing

    Benchmark simulator with dynamic environment for job scheduling in grid computing by Ku-Mahamud, Ku Ruhana

    Published 2014
    “…Job scheduling algorithm has a significant influence on grid computing performance. …”
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  20. 20
    GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE

    GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE by MOHAMMED SHARIFF, NUR ATIQAH

    Published 2020
    “…This leads to the problems where the engineers have to feed their simulation with new inputs and parameters to get the best solution. …”
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