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  1. 1
    A case study : 2D Vs 3D parallel differential equation toward tumor cell detection on multi-core parallel computing atmosphere

    A case study : 2D Vs 3D parallel differential equation toward tumor cell detection on multi-core parallel computing atmosphere by Islam, Md. Rajibul, Alias, Norma

    Published 2010
    “…With the intention to address such computational challenges; we recognised the particular significance of the iterative solvers and parallel algorithms on Multi-Core parallel computing atmosphere. …”
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  2. 2
    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
    “…The CPU profiler of JavaTM VisualVM measures the number of invocations of scheduling event handlers (procedures) in each algorithm as well as the total time spent in all invocations of this handler. …”
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  3. 3
    Parallelizing Algorithm For Object Recognition Based On Edge Detection On Sunfire Cluster.

    Parallelizing Algorithm For Object Recognition Based On Edge Detection On Sunfire Cluster. by Osman, Mohd Azam, Sanudin, Maziani, Ibrahim, Muhammad Ismail

    Published 2007
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  4. 4
    Parallel algorithms for edge detection on cluster computer

    Parallel algorithms for edge detection on cluster computer by Gunawan, Teddy Surya, Bacar, Elkabir Ansoya

    Published 2011
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  5. 5
    Tumor growth prediction using parallel computing: numerical solutions based on multi-dimensional partial differential equation (PDE)

    Tumor growth prediction using parallel computing: numerical solutions based on multi-dimensional partial differential equation (PDE) by Alias, Norma, Islam, Md. Rajibul

    Published 2010
    “…This study focuses on the implementation of parallel algorithm for the simulation of tumor growth using two dimensional Helmholtz’s wave equation on a distributed parallel computing system. …”
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  6. 6
    Multithreaded Scalable Matching Algorithm For Intrusion Detection Systems

    Multithreaded Scalable Matching Algorithm For Intrusion Detection Systems by Hnaif, Adnan Ahmad Abdelfattah

    Published 2010
    “…Therefore, the performance of the existing algorithms needs to be improved both in sequential and parallel to enhance the speed of the detection engine used in SNORT-NIDS. …”
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  7. 7
    A Toolkit for Simulation of Desktop Grid Environment

    A Toolkit for Simulation of Desktop Grid Environment by FOROUSHAN, PAYAM CHINI

    Published 2014
    “…In this type of environment it is nearly impossible to prove the effectiveness of a scheduling algorithm. Hence the main objective of this study is to develop a desktop grid simulator toolkit for measuring and modeling scheduler algorithm performance. …”
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  8. 8
    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT

    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT by Abdullah, Amran

    Published 2006
    “…Genetic Algorithm is one of the most popular optimization solutions used in various applications such as scheduling. …”
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  9. 9
    Early detection of breast cancer using wave elliptic equation with high performance computing

    Early detection of breast cancer using wave elliptic equation with high performance computing by Alias, Norma, Rosly, Nur Syazana, Satam, Noriza, A. Ghaffar, Zarith Safiza, Islam, Md. Rajibul

    Published 2008
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  10. 10
    Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm

    Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm by Al-Jumaili A.H.A., Muniyandi R.C., Hasan M.K., Singh M.J., Paw J.K.S., Al-Jumaily A.

    Published 2025
    “…The Canopy clustering approach, enabled by distributed machine learning through Apache Mahout, is utilized as a preprocessing step within the K-means clustering technique. The hybrid algorithm was implemented to minimise the length of time needed to address the massive scale of the detected parallel power load abnormalities. …”
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  11. 11
    NEUCOMP2 - parallel neural network compiler

    NEUCOMP2 - parallel neural network compiler by Sulaiman, Md. Nasir, Evans, D. J.

    Published 1996
    “…NEUCOMP2 was developed and run on the Sequent Balance 8000 computer system at Parallel Algorithm Research Centre, U.K.…”
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  12. 12
    Improving Class Timetabling using Genetic Algorithm

    Improving Class Timetabling using Genetic Algorithm by Qutishat, Ahmed Mohammed Ali

    Published 2006
    “…This paper reports the power fill techniques using GA in scheduling. Class timetabling problem is one of the applications in scheduling. …”
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  13. 13
    Examination timetabling using genetic algorithm case study: KUiTTHO

    Examination timetabling using genetic algorithm case study: KUiTTHO by Mohd Salikon, Mohd Zaki

    Published 2005
    “…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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  14. 14
    Effect Of The Addition Of Wastepaper To Concrete Mix

    Effect Of The Addition Of Wastepaper To Concrete Mix by Shukeri, Ritzawaty Mohamad

    Published 2009
    “…Therefore, the performance of the existing algorithms needs to be improved both in sequential and parallel to enhance the speed of the detection engine used in SNORT-NIDS. …”
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  15. 15
    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO

    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO by Mohd. Zaki, Mohd. Salikon

    Published 2005
    “…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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  16. 16
    Autonomous anomaly detection using density-based features in streaming data / Muhammmad Yunus Iqbal Basheer

    Autonomous anomaly detection using density-based features in streaming data / Muhammmad Yunus Iqbal Basheer by Iqbal Basheer, Muhammmad Yunus

    Published 2023
    “…Hence, it is critical for an anomaly detection algorithm to detect data anomalies patterns. …”
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  17. 17
    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
    “…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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  18. 18
    Parallel system for abnormal cell growth prediction based on fast numerical simulation

    Parallel system for abnormal cell growth prediction based on fast numerical simulation by Alias, Norma, Islam, Md. Rajibul, Shahir, Rosdiana, Hamzah, Norhafizah, Satam, Noriza, Abd. Ghaffar, Zarith Safiza, Darwis, Roziha, Ludin, Eliana, Azami, Masrin

    Published 2010
    “…The paper ends with a concluding remark on the parallel performance evaluations and numerical analysis in reducing the execution time, communication cost and computational complexity.…”
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  19. 19
    Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System

    Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System by Ali, Mohammed Hasan, Mohamed Fadli, Zolkipli

    Published 2019
    “…The detection of network attacks on computer systems remains an attractive but challenging research scope. …”
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  20. 20
    Enhanced and effective parallel optical flow method for vehicle detection and tracking

    Enhanced and effective parallel optical flow method for vehicle detection and tracking by Bhaskar, P.K., Yong, S.-P., Jung, L.T.

    Published 2016
    “…The foreground optical flow detector detects the object and a binary computation is done to define rectangular regions around every detected object. …”
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