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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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    Thesis
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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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    Final Year Project / Dissertation / Thesis
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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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    Citation Index Journal
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  7. 7
    Automatic control of flotation process using computer vision

    Automatic control of flotation process using computer vision by Saravani, Ali Jahed

    Published 2015
    “…In addition, several algorithms were implemented to measure the other important image variables such as froth velocity,froth color and bubble collapse rate. …”
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  8. 8
    Design optimization of valve timing at various engine speeds using Multi-Objective Genetic Algorithm (MOGA)

    Design optimization of valve timing at various engine speeds using Multi-Objective Genetic Algorithm (MOGA) by Mohiuddin, A. K. M., Ashour, Ahmed Aly Ibrahim Shaaban, Yap, Haw Shin

    Published 2008
    “…The primary concern is to acquire the clear picture of the implementation of Multi-Objective Genetic Algorithm and the essential of variable valve timing effects on the engine performances in various engine speeds. …”
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    Proceeding Paper
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  9. 9
    Development of fault detection, diagnosis and control system identification using multivariate statistical process control (MSPC)

    Development of fault detection, diagnosis and control system identification using multivariate statistical process control (MSPC) by Ibrahim, Kamarul 'Asri, Ahmad, Arshad, Ali, Mohamad Wijayanuddin, Mak, Weng Yee

    Published 2006
    “…Thus, the availability of theoretical models is not an important factor in the implementation of MSPC on processes. …”
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    Monograph
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  10. 10
    Bayesian inference for the bivariate extreme model

    Bayesian inference for the bivariate extreme model by Mohd Amin, Nor Azrita, Adam, Mohd Bakri

    Published 2016
    “…Using simulation study, the capability of MTM algorithm to analyze the posterior distribution is implement. …”
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  11. 11
    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 simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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  12. 12
    Smoothed functional algorithm with norm-limited update vector for identification of continuous-time fractional-order Hammerstein Models

    Smoothed functional algorithm with norm-limited update vector for identification of continuous-time fractional-order Hammerstein Models by Mok, Ren Hao, Mohd Ashraf, Ahmad

    Published 2024
    “…As a result of this, the proposed NL-SFA based method is applied to identify the variables of the linear and non-linear subsystems in the Hammerstein model. …”
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  13. 13
    Optimization of Upstream Offshore Oilfield Production Planning under Uncertainty and Downstream Crude Oil Scheduling at Refinery Front-End

    Optimization of Upstream Offshore Oilfield Production Planning under Uncertainty and Downstream Crude Oil Scheduling at Refinery Front-End by Tan Yin Keong, Tan Yin

    Published 2012
    “…The solution obtained from the LB–MILP model, i.e., the decision variables (binary variables), was used to obtain a feasible solution for model UB–NLP. …”
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  14. 14
    Optimization of Upstream Offshore Oilfield Production Planning under Uncertainty and Downstream Crude Oil Scheduling at Refinery Front-End

    Optimization of Upstream Offshore Oilfield Production Planning under Uncertainty and Downstream Crude Oil Scheduling at Refinery Front-End by Tan , Yin Keong

    Published 2009
    “…The solution obtained from the LB-MILP model, i.e., the decision variables (binary variables), was used to obtain a feasible solution for model UB-NLP. …”
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  15. 15
    Application of Multi-objective Genetic Algorithm (MOGA) for design optimization of valve timing at various engine speeds

    Application of Multi-objective Genetic Algorithm (MOGA) for design optimization of valve timing at various engine speeds by Mohiuddin, A. K. M., Rahman, Mohammed Ataur, Haw Shin, Yap

    Published 2011
    “…The primary concern is to acquire the clear picture of the implementation of Multi-Objective Genetic Algorithm and the essential of variable valve timing effects on the engine performances in various engine speeds. …”
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  16. 16
    Variable Global Optimization min-sum (VGOMS) algorithm of decode-and forward-protocol for the relay node in the cooperative channel

    Variable Global Optimization min-sum (VGOMS) algorithm of decode-and forward-protocol for the relay node in the cooperative channel by Hushairi, Zen, Al-Khalid, Hj Othman, Khairuddin, Abdul Hamid, Jamaah, Suut

    Published 2020
    “…A low complexity min-sum (MS) based algorithm called the Variable Global Optimization min-sum (VGOMS) algorithm has been developed to minimise the error corrective performance. …”
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  17. 17
    Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm

    Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm by Basri Badyalina, Nurkhairany Amyra Mokhtar, Nur Amalina Mat Jan, Muhammad Fadhil Marsani, Mohamad Faizal Ramli, Muhammad Majid, Fatin Farazh Ya'acob

    Published 2022
    “…Therefore, in this paper, we enhance the GMDH model by implementing the ABC algorithm to optimize the parameter of partial description GMDH model with some transfer functions, namely polynomial, radial basis, sigmoid and hyperbolic tangent function. …”
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  18. 18
    Variable Global Optimization min-sum (VGOMS) algorithm of decodeand-forward protocol for the relay node in the cooperative channel

    Variable Global Optimization min-sum (VGOMS) algorithm of decodeand-forward protocol for the relay node in the cooperative channel by Hushairi, Zen, Jamaah, Suut, Al-Khalid, Bin Hj Othman, Khairuddin, Bin Ab Hamid

    Published 2019
    “…A low complexity min-sum (MS) based algorithm called the Variable Global Optimization min-sum (VGOMS) algorithm has been developed to minimise the error corrective performance. …”
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  19. 19
    The Implementation of Genetic Algorithm in Path Optimization

    The Implementation of Genetic Algorithm in Path Optimization by Jumali, Suriana

    Published 2005
    “…In this project, TSP will be used to model and easy visualize the path optimization problem and Genetic Algorithm (GA) was chosen to be implemented in resolving the problem. …”
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  20. 20
    Effect of input variables selection on energy demand prediction based on intelligent hybrid neural networks

    Effect of input variables selection on energy demand prediction based on intelligent hybrid neural networks by Islam, B., Baharudin, Z., Nallagownden, P.

    Published 2015
    “…In this paper, the important issues related with the best input variable selection for a hybrid model is addressed. …”
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