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  1. 1
    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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  2. 2
    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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  3. 3
    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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  4. 4
    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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  5. 5
    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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  6. 6
    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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  7. 7
    A Model for Evaluation of Cryptography Algorithm on UUM Portal

    A Model for Evaluation of Cryptography Algorithm on UUM Portal by Norliana, Abdul Majid

    Published 2004
    “…The development of of simulation model involves three level of development. …”
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  8. 8
    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
    “…After that, we will develop our new algorithm for the QoS Model. Then, we simulate the new enhanced algorithm and compare to other algorithms.…”
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  9. 9
    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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  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, 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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  11. 11
    Assessing the simulation performances of multiple model selection algorithm

    Assessing the simulation performances of multiple model selection algorithm by Yusof, Norhayati, Ismail, Suzilah, Tuan Muda, Tuan Zalizam

    Published 2015
    “…The capability of the algorithm in finding the true specification of multiple models is measured by the percentage of simulation outcomes.Overall results show that the algorithm has performed well for a model with two equations.The findings also indicated that the number of variables in the true models affect the algorithm performances. …”
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  12. 12
    Ensemble Dual Recursive Learning Algorithms for Identifying Custom Tanks Flow with Leakage

    Ensemble Dual Recursive Learning Algorithms for Identifying Custom Tanks Flow with Leakage by Akib, Afifi, Saad , Nordin, Asirvadam , Vijanth Sagayan

    Published 2010
    “…Relative mass release of the leakage is introduced as the input for the simulation model and the data from the simulation model is taken at real time (on-line) to feed into the recursive algorithms. …”
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  13. 13
    Performance of Semi Active Lateral Control (SALC) algorithm for semi active suspension system in multibody co-simulation method / M. M. Abdul Majid ...[et al.]

    Performance of Semi Active Lateral Control (SALC) algorithm for semi active suspension system in multibody co-simulation method / M. M. Abdul Majid ...[et al.] by Abdul Majid, M. M., Salleh, M. S., Abu Hashim, M. A., Ismail, N. H., Mansor, S., Abu Bakar, S. A.

    Published 2018
    “…Thus limiting benefits on handling characteristic with current available algorithms. The research scope covered experimental measurements, simulation model correlation, and vehicle plant modelling using multibody approach (MSC Adams/Car), controller algorithm development in Matlab/Simulink and performance validation in co-simulation environment. …”
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  14. 14
    A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models

    A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

    Published 2017
    “…Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. …”
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  15. 15
    A discrete event simulation framework for utility accrual scheduling algorithm in uniprocessor environment

    A discrete event simulation framework for utility accrual scheduling algorithm in uniprocessor environment by Ahmad, Idawaty, K. Subramaniam, Shamala, Othman, Mohamed, Ahmad Zukarnain, Zuriati

    Published 2011
    “…Approach: The study focused on the proposed and the development of an event based discrete event simulator for the existing General Utility Scheduling (GUS) to facilitate the reuse of the algorithm under a common simulation environment. …”
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  16. 16
    Optimization of Prediction Error in CO2 Laser Cutting process by Taguchi Artificial Neural Network Hybrid with Genetic algorithm

    Optimization of Prediction Error in CO2 Laser Cutting process by Taguchi Artificial Neural Network Hybrid with Genetic algorithm by Nukman, Y., Hassan, M.A., Harizam, M.Z.

    Published 2013
    “…The simulation results showed that the developed GA-Taguchi ANN model could reduce the maximum prediction error below 10. …”
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  17. 17
    Octane number prediction for gasoline blends using convolution neural network / Zhu Yue

    Octane number prediction for gasoline blends using convolution neural network / Zhu Yue by Zhu , Yue

    Published 2021
    “…Simulation result show that RBF algorithm gives the best performance. …”
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  18. 18
    Identification algorithms of flexible structure using neural networks

    Identification algorithms of flexible structure using neural networks by Ismail, R., Ismail, A. Y., Mat Darus, I. Z.

    Published 2006
    “…A simulation algorithm of the plate is developed through a discretisation of the governing partial differential equation formulation of the plate dynamics using finite difference methods. …”
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  19. 19
    An intelligent framework for modelling and active vibration control of flexible structures

    An intelligent framework for modelling and active vibration control of flexible structures by Mohd. Hashim, Siti Zaiton

    Published 2004
    “…The simulation algorithm and interactive environment thus developed and validated form suitable test and verification platforms for the development of AVC strategies for flexible structures as well as for learning and research purposes.…”
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  20. 20
    Ensemble dual recursive learning algorithms for identifying flow with leakage

    Ensemble dual recursive learning algorithms for identifying flow with leakage by Akib, Afifi, Saad , Nordin, Asirvadam , Vijanth Sagayan

    Published 2010
    “…Relative mass release of the leakage is introduced as the input for the simulation model and the data from the simulation model is taken at real time (on-line) to feed into the recursive algorithms. …”
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