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  1. 1
    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
    “…The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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  2. 2
    Direct approach for mining association rules from structured XML data

    Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

    Published 2012
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  3. 3
    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection

    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

    Published 2008
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  4. 4
    Study and Implementation of Data Mining in Urban Gardening

    Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

    Published 2019
    “…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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  5. 5
    Mining Sequential Patterns Using I-PrefixSpan

    Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

    Published 2007
    “…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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  6. 6
    A web-based implementation of k-means algorithms

    A web-based implementation of k-means algorithms by Lee, Quan

    Published 2022
    “…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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  7. 7
    Mining Sequential Patterns using I-PrefixSpan

    Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

    Published 2008
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  8. 8
    Performance of Ad-Hoc on-Demand Distance Vector Discovery Algorithms Based On Packet Lifetime

    Performance of Ad-Hoc on-Demand Distance Vector Discovery Algorithms Based On Packet Lifetime by Ahmad, Noor Azlan

    Published 2008
    “…The performance analysis of the proposed algorithms was conducted by using the discrete-event simulator, OMNeT++. …”
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  9. 9
    Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems

    Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems by Nor Hidayati, Abdul Aziz, Zuwairie, Ibrahim, Nor Azlina, Ab. Aziz, Mohd Saberi, Mohamad, Watada, Junzo

    Published 2016
    “…The proposed ssSKF algorithm is tested using the 30 benchmark functions of CEC 2014, and its performance is compared to the original SKF algorithm, Black Hole (BH) algorithm, Particle Swarm Optimisation (PSO) algorithm, Grey Wolf Optimiser (GWO) algorithm, and Genetic Algorithm (GA). …”
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  10. 10
    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
    “…The main objective of this research is to validate the NIRR algorithm by developing a comprehensive simulation model using Discrete Event Simulation (DES). …”
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  11. 11
    Ant colony optimization for rule induction with simulated annealing for terms selection

    Ant colony optimization for rule induction with simulated annealing for terms selection by Saian, Rizauddin, Ku-Mahamud, Ku Ruhana

    Published 2012
    “…This paper proposes a sequential covering based algorithm that uses an ant colony optimization algorithm to directly extract classification rules from the data set.The proposed algorithm uses a Simulated Annealing algorithm to optimize terms selection, while growing a rule.The proposed algorithm minimizes the problem of a low quality discovered rule by an ant in a colony, where the rule discovered by an ant is not the best quality rule, by optimizing the terms selection in rule construction. …”
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  12. 12
    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
    “…The main objective of this research is to validate the NIRR algorithm by developing a comprehensive simulation model using Discrete Event Simulation (DES). …”
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  13. 13
    A discrete simulated kalman filter optimizer for combinatorial optimization problems

    A discrete simulated kalman filter optimizer for combinatorial optimization problems by Suhazri Amrin, Rahmad

    Published 2022
    “…In this research, three combinatorial problems namely the travelling salesman problem (TSP), assembly sequence planning (ASP), and the hole drilling proble are used to evaluate the proposed algorithm. Two types of analysis are used to evaluate the proposed algorithm. …”
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  14. 14
    Single-solution Simulated Kalman Filter algorithm for global optimisation problems

    Single-solution Simulated Kalman Filter algorithm for global optimisation problems by Abdul Aziz, N.H., Ibrahim, Z., Ab Aziz, N.A., Mohamad, M.S., Watada, J.

    Published 2018
    “…The proposed ssSKF algorithm is tested using the 30 benchmark functions of CEC 2014, and its performance is compared to that of the original SKF algorithm, Black Hole (BH) algorithm, Particle Swarm Optimisation (PSO) algorithm, Grey Wolf Optimiser (GWO) algorithm and Genetic Algorithm (GA). …”
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  15. 15
    Single-solution Simulated Kalman Filter algorithm for global optimisation problems

    Single-solution Simulated Kalman Filter algorithm for global optimisation problems by Abdul Aziz, N.H., Ibrahim, Z., Ab Aziz, N.A., Mohamad, M.S., Watada, J.

    Published 2018
    “…The proposed ssSKF algorithm is tested using the 30 benchmark functions of CEC 2014, and its performance is compared to that of the original SKF algorithm, Black Hole (BH) algorithm, Particle Swarm Optimisation (PSO) algorithm, Grey Wolf Optimiser (GWO) algorithm and Genetic Algorithm (GA). …”
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  16. 16
    Quality of service management algorithms in WiMAX networks

    Quality of service management algorithms in WiMAX networks by Saidu, Ibrahim

    Published 2015
    “…Simulation have been extensively used to evaluate the proposed algorithm. …”
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  17. 17
    A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules

    A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules by Rizauddin, Saian

    Published 2013
    “…In the first proposed algorithm, SA is used to optimize the rule's discovery activity by an ant. …”
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  18. 18
    Optimization-based simulation algorithm for predictive-reactive job-shop scheduling of reconfigurable manufacturing systems

    Optimization-based simulation algorithm for predictive-reactive job-shop scheduling of reconfigurable manufacturing systems by Tan, Joe Yee

    Published 2022
    “…Simulation experiments were run using different parameters to analyze the performance of the proposed algorithm with the system. …”
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  19. 19
    Incorporating the range-based method into GridSim for modeling task and resource heterogeneity

    Incorporating the range-based method into GridSim for modeling task and resource heterogeneity by Eng, Kailun, Muhammed, Abdullah, Mohamed, Mohamad Afendee, Hasan, Sazlinah

    Published 2017
    “…GridSim simulator has become a very popular simulation tool and has been widely used by Grid researchers to test and evaluate the performance of their proposed scheduling algorithms. …”
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  20. 20
    Optimized clustering with modified K-means algorithm

    Optimized clustering with modified K-means algorithm by Alibuhtto, Mohamed Cassim

    Published 2021
    “…Besides, some real data sets were examined to validate the proposed algorithm. Empirical evidences based on simulated data sets indicated that the proposed modified k-means algorithm is able to recognise the optimum number of clusters for uncorrelated data sets. …”
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