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  1. 1
    Performance Comparison of Parallel Bees Algorithm on Rosenbrock Function

    Performance Comparison of Parallel Bees Algorithm on Rosenbrock Function by Hammash, Nayif Mohammed

    Published 2012
    “…This thesis presents the parallel Bees Algorithm as a new approach for optimizing the last results for the Bees Algorithm. …”
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  2. 2
    Efficient Sequential and Parallel Routing Algorithms in Optical Multistage Interconnection Network

    Efficient Sequential and Parallel Routing Algorithms in Optical Multistage Interconnection Network by Abduh Kaid, Monir Abdullah

    Published 2005
    “…The efficient combination of simulated annealing algorithm with the best heuristic algorithms gave much better result in a very minimal time. …”
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  3. 3
    Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network

    Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network by Sefidgar, Seyed Mohammad Hossein

    Published 2014
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  4. 4
    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
    “…The simulation is implemented with iFogSim and java programming language. …”
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  5. 5
    Parallel batch self-organizing map on graphics processing unit using CUDA

    Parallel batch self-organizing map on graphics processing unit using CUDA by Daneshpajouh, H., Delisle, P., Boisson, J.-C., Krajecki, M., Zakaria, N.

    Published 2018
    “…The most computationally expensive parts of its training algorithm (such as steps to compute distance between each data vector and neuron, and determining the Best Matching Unit based on minimum distance) are identified and mapped on GPU to be processed in parallel. …”
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  6. 6
    Parallel batch self-organizing map on graphics processing unit using CUDA

    Parallel batch self-organizing map on graphics processing unit using CUDA by Daneshpajouh, H., Delisle, P., Boisson, J.-C., Krajecki, M., Zakaria, N.

    Published 2018
    “…The most computationally expensive parts of its training algorithm (such as steps to compute distance between each data vector and neuron, and determining the Best Matching Unit based on minimum distance) are identified and mapped on GPU to be processed in parallel. …”
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  7. 7
    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
    “…Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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  8. 8
    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
    “…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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  9. 9
    Accelerating DNA sequence alignment based on smith waterman algorithm using recursive variable expansion / Muhamad Faiz Ismail

    Accelerating DNA sequence alignment based on smith waterman algorithm using recursive variable expansion / Muhamad Faiz Ismail by Ismail, Muhamad Faiz

    Published 2014
    “…Reconfigurable computing, in which general purpose processor (GPP) is increasingly used for high performance computing where massive fine-grain parallelism can be exploited. …”
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  10. 10
    A Parallel-Model Speech Emotion Recognition Network Based on Feature Clustering

    A Parallel-Model Speech Emotion Recognition Network Based on Feature Clustering by Li-Min Zhang, Giap Weng Ng, Yu-Beng Leau, Hao Yan

    Published 2023
    “…To address this issue, we proposed a novel algorithm called F-Emotion to select speech emotion features and established a parallel deep learning model to recognize different types of emotions. …”
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  11. 11
    A parallel-model speech emotion recognition network based on feature clustering

    A parallel-model speech emotion recognition network based on feature clustering by Li-Min Zhang, Giap Weng Ng, Yu-Beng Leau, Hao Yan

    Published 2023
    “…To address this issue, we proposed a novel algorithm called F-Emotion to select speech emotion features and established a parallel deep learning model to recognize different types of emotions. …”
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  12. 12
    Parallelizing GF (p) montgomery elliptic curve crypto-system operations to improve security and performance.

    Parallelizing GF (p) montgomery elliptic curve crypto-system operations to improve security and performance. by Mohammad Alkhatib, Jaafar, Azmi, Md Said, Mohamad Rushdan, Ahmad Zukarnain, Zuriati

    Published 2012
    “…Hardware implementations with target FPGA for GF (p) Montgomery ECC are also presented. The best performance level was achieved when parallelizing Montgomery ECC computations to eight parallel multipliers (PM) using homogeneous coordinates. …”
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  13. 13
    Parallelizing GF (p) montgomery elliptic curve crypto-system operations to improve security and performance.

    Parallelizing GF (p) montgomery elliptic curve crypto-system operations to improve security and performance. by Alkhatib, Mohammad, Jaafar, Azmi, Md Said, Mohamad Rushdan, Ahmad Zulkarnain, Zuriati

    Published 2013
    “…Hardware implementations with target FPGA for GF (p) Montgomery ECC are also presented. The best performance level was achieved when parallelizing Montgomery ECC computations to eight parallel multipliers (PM) using homogeneous coordinates. …”
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  14. 14
    Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.

    Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models. by Kwad, Ayad Mahmood

    Published 2022
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  15. 15
    Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms

    Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms by Loo, C.K., Liew, W.S., Seera, M., Lim, Einly

    Published 2015
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  16. 16
    OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT

    OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT by Murad S.S., Badeel R., Alsandi N.S.A., Alshaaya R.F., Ahmed R.A., Muhammed A., Derahman M.

    Published 2023
    “…To achieve this, we propose a new noble mechanism called Optimized Min-Min (OMin-Min) algorithm, inspired by the Min-Min algorithm. …”
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  17. 17
    Parallel processing in Compute Unified Device Architecture (CUDA) for energy saving glass

    Parallel processing in Compute Unified Device Architecture (CUDA) for energy saving glass by Shibghatullah, Abdul Samad, Xia, Khoo Wen, Azmin, Farah Ayuni, Mohd Johar, Fauzi

    Published 2014
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  18. 18
    Network routing by mobile agents for image retrieval using fuzzy genetic algorithms

    Network routing by mobile agents for image retrieval using fuzzy genetic algorithms by Selamat, Ali, Selamat, Mohd. Hafiz, Bakri, Aryati, Sulaiman, Sarina, Selamat, Fatimah @ Salwa, Mohd. Daud, Mariah

    Published 2005
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  19. 19
    CUDA implementation of fractal image compression

    CUDA implementation of fractal image compression by Al Sideiri A., Alzeidi N., Al Hammoshi M., Chauhan M.S., AlFarsi G.

    Published 2023
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  20. 20
    Protein secondary structure prediction from amino acid sequences using a neural network classifier based on the Dempster-Shafer theory

    Protein secondary structure prediction from amino acid sequences using a neural network classifier based on the Dempster-Shafer theory by Vel Arjunan, Satya Nanda

    Published 2003
    “…In order to reduce the computational demand when training with large data of proteins, an interface was developed using the data parallel approach to parallelize the training phase of the classifier and other accompanying methods such as data clustering algorithms. …”
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