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  1. 1
    The division free parallel algorithm for finding determinant

    The division free parallel algorithm for finding determinant by Karim, Sharmila, Omar, Zurni, Ibrahim, Haslinda

    Published 2013
    “…A cross multiplication method for determinant was generalized for any size of square matrices using a new permutation strategy.The permutation is generated based on starter sets.However, via permutation, the time execution of sequential algorithm became longer.Thus, in order to reduce the computation time, a parallel strategy was developed which is suited for master and slave paradigm of the high performance computer.A parallel algorithm is integrated with message passing interface.The numerical results showed that the parallel methods computed the determinants faster than the sequential counterparts particularly when the tasks were equally allocated.…”
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  2. 2
    Automatic Multi-Objective Clustering Algorithm Using Hybrid Particle Swarm Optimization With Simulated Annealing.

    Automatic Multi-Objective Clustering Algorithm Using Hybrid Particle Swarm Optimization With Simulated Annealing. by Abubaker, Ahmad Asad

    Published 2016
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  3. 3
    Dynamic partitioning and data allocation method on heterogeneous architecture / Muhammad Helmi Rosli

    Dynamic partitioning and data allocation method on heterogeneous architecture / Muhammad Helmi Rosli by Rosli, Muhammad Helmi

    Published 2015
    “…Each experiment highlight the advantages and disadvantages of the experimental architecture.The disadvantages from each experiment prompts the design of dynamic parallel partitioning and allocating framework. The case study use for this experiment is Sobel edge detection algorithm. …”
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  4. 4
    Optimal QoS aware multiple paths web service composition using heuristic algorithms and data mining techniques

    Optimal QoS aware multiple paths web service composition using heuristic algorithms and data mining techniques by Qtaish, Osama Kayed Taher

    Published 2014
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  5. 5
    Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets

    Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets by Saeed, Sana

    Published 2019
    “…A new self-adaptive hybrid algorithm (CSCMAES) is introduced for optimization. …”
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  6. 6
    Development of a heuristic procedure for balancing mixed-model parallel assembly line type II

    Development of a heuristic procedure for balancing mixed-model parallel assembly line type II by Esmaeilian, Gholamreza

    Published 2010
    “…To solve these problems, two heuristic algorithms were developed and coded in MATLAB®. The first one allocates each model to only one parallel assembly line and achieves the initial arrangement of tasks with the minimum number of workstations for each line. …”
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  7. 7
    Logic mining method via hybrid discrete hopfield neural network

    Logic mining method via hybrid discrete hopfield neural network by Guo, Yueling, Mohd Kasihmuddin, Mohd Shareduwan, Zamri, Nur Ezlin, Li, Jia, Romli, Nurul Atiqah, Mansor, Mohd Asyraf, Ruzai, Wan Nur Aqlili

    Published 2025
    “…To demonstrate the efficacy of the proposed logic mining approach, experiments were conducted using both simulated and real-world datasets. …”
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  8. 8
    WCDMA teletraffic performance improvement via power resource optimization using distributed parallel genetic algorithm

    WCDMA teletraffic performance improvement via power resource optimization using distributed parallel genetic algorithm by Prajindra S.K., Tiong S.K., Johnny Koh S.P., Yap D.F.W.

    Published 2023
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  9. 9
    Application of intelligence based genetic algorithm for job sequencing problem on parallel mixed-model assembly line

    Application of intelligence based genetic algorithm for job sequencing problem on parallel mixed-model assembly line by Noroziroshan, Alireza, Mohd Ariffin, Mohd Khairol Anuar, Ismail, Napsiah

    Published 2010
    “…Hence using the proposed mathematical model for large size problem was time consuming and inefficient as so many job allocation values should be checked. …”
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  10. 10
    Parallel guided image processing model for ficus deltoidea (Jack) moraceae varietal recognition

    Parallel guided image processing model for ficus deltoidea (Jack) moraceae varietal recognition by Abdul Rahman, Prof. Dr. Mohd Nordin

    Published 2018
    “…From the results, the sequential complex image processing model and computational flow design are significantly improved when executed through parallel model under multi-cores computer system. As the number of cores increases, the computational time taken by the parallel algorithm becomes less. © 2018, Springer Nature Singapore Pte Ltd.…”
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  11. 11
    Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method

    Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method by Abbaszadeh M., Soltani-Mohammadi S., Ahmed A.N.

    Published 2023
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  12. 12
    Optimization of Workload Allocation Problem in a Network of Heterogeneous Computer Systems

    Optimization of Workload Allocation Problem in a Network of Heterogeneous Computer Systems by Rahela, Abdul Rahim

    Published 2005
    “…Multiple Queue Multiple Server Queueing models are used to model workload allocation problems in a network of computers. …”
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  13. 13
    New Sequential and Parallel Division Free Methods for Determinant of Matrices

    New Sequential and Parallel Division Free Methods for Determinant of Matrices by Sharmila, Karim

    Published 2013
    “…Numerical results showed that the parallel methods were able to compute determinants faster than the sequential counterparts, particularly when the tasks were equally allocated. …”
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  14. 14
    Image reconstruction using iterative transpose algorithm for optical tomography

    Image reconstruction using iterative transpose algorithm for optical tomography by Md. Yunos, Yusri, Abd. Rahim, Ruzairi, Green, R. G., Fazalul Rahiman, Mohd. Hafiz

    Published 2007
    “…This paper describes a transpose algorithm for use with an optical tomography system. …”
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  15. 15
    Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm

    Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm by Mohd Kasihmuddin, Mohd Shareduwan, Abdul Halim, Nur Shahira, Mohd Jamaludin, Siti Zulaikha, Mansor, Mohd. Asyraf, Alway, Alyaa, Zamri, Nur Ezlin, Azhar, Siti Aishah, Marsani, Muhammad Fadhil

    Published 2023
    “…The simulation suggested that the proposed model outperformed the existing model in doing logic mining for the online shoppers dataset.…”
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  16. 16
    Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli

    Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

    Published 2018
    “…To assess the applicability and accuracy of the proposed method for long-term electrical energy consumption, its estimates are compared with those obtained from artificial neural network (ANN), support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), rule-based data mining algorithm, GEP, linear, quadratic and exponential models optimized by particle swarm optimization (PSO), cuckoo search algorithm (CSA), artificial cooperative search (ACS) algorithm and backtracking search algorithm (BSA). …”
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  17. 17
    Improving energy consumption in cloud computing datacenters using a combination of energy-aware resource allocation and scheduling mechanism

    Improving energy consumption in cloud computing datacenters using a combination of energy-aware resource allocation and scheduling mechanism by Khalil Abd., Sura

    Published 2017
    “…Nowadays, DNA plays a vital role in many computing applications due to the massive processing parallelism. In addition, using fuzzy theory in genetic algorithm reduces the iteration of producing the population and assigning the suitable resources to the tasks-based and task length in the node capacity. …”
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  18. 18
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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  19. 19
    Metaheuristic optimization of perovskite solar cell performance using Taguchi grey relational analysis with grey wolf optimizer

    Metaheuristic optimization of perovskite solar cell performance using Taguchi grey relational analysis with grey wolf optimizer by Salehuddin, Fauziyah, Mat Junos @ Yunus, Siti Aisah, Ahmad Jalaludin, Nabilah, Mohd Nazli, Nurul Farina, Arith, Faiz, Mohd Zain, Anis Suhaila, Mohamd Rafidi, Nur Ruzanna, Kaharudin, Khairil Ezwan

    Published 2025
    “…The metaheuristic approach sequentially employs the L27 orthogonal array (OA) Taguchi-based design of experiment (DoE), Grey Relational Analysis (GRA), Multiple Linear Regression (MLR) and Grey Wolf Optimizer (GWO). The L27 OA Taguchi-based DoE is initially employed to mine sufficient output data simulated using one dimensional solar cell capacitance simulator (SCAPS-1D). …”
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  20. 20
    Parallel guided image processing model for ficus deltoidea (Jack) moraceae varietal recognition

    Parallel guided image processing model for ficus deltoidea (Jack) moraceae varietal recognition by Ahmad Fakhri, Ab. Nasir, Ahmad Shahrizan, Abdul Ghani, M. Nordin, A. Rahman

    Published 2018
    “…From the results, the sequential complex image processing model and computational flow design are significantly improved when executed through parallel model under multi-cores computer system. As the number of cores increases, the computational time taken by the parallel algorithm becomes less.…”
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