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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
    An enhanced segment particle swarm optimization algorithm for kinetic parameters estimation of the main metabolic model of Escherichia coli

    An enhanced segment particle swarm optimization algorithm for kinetic parameters estimation of the main metabolic model of Escherichia coli by Mohammed Adam, Kunna, Tuty Asmawaty, Abdul Kadir, Muhammad Akmal, Remli, Noorlin, Mohd Ali, Kohbalan, Moorthy, Noryanti, Muhammad

    Published 2020
    “…In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. …”
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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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    Final Year Project / Dissertation / Thesis
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  7. 7
    Integrated optimal control and parameter estimation algorithms for discrete-time nonlinear stochastic dynamical systems

    Integrated optimal control and parameter estimation algorithms for discrete-time nonlinear stochastic dynamical systems by Kek, Sie Long

    Published 2011
    “…The main idea is the integration of optimal control and parameter estimation. In this work, a simplified model-based optimal control model with adjustable parameters is constructed. …”
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  8. 8
    Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach

    Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach by Nurnajmin Qasrina Ann, ., Pebrianti, Dwi, Mohamad Fadhil, Abas, Bayuaji, Luhur

    Published 2022
    “…The most popular method to solve parameter estimation problem is using optimization algorithm that easily trap to local minima and poor in exploitation to find the good solutions. …”
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  9. 9
    Parameter estimation of cocomo model using the jaya algorithm for software cost estimation

    Parameter estimation of cocomo model using the jaya algorithm for software cost estimation by Tan, Jie Chee

    Published 2019
    “…This thesis proposed the Jaya algorithm based on estimation of the software based on the COCOMO I model. …”
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    Undergraduates Project Papers
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  10. 10
    Parameter estimation of essential amino acids in Arabidopsis thaliana using hybrid of bees algorithm and harmony search

    Parameter estimation of essential amino acids in Arabidopsis thaliana using hybrid of bees algorithm and harmony search by Aw, Mei Yee, Mohd Saberi, Mohamad, Chong, Chuii Khim, Safaai, Deris, Muhammad Akmal, Remli, Mohd Arfian, Ismail, Corchado, Juan Manuel, Omatu, Sigeru

    Published 2019
    “…In model building, the task of parameter estimation is difficult due to the huge numbers of kinetics parameters involved. …”
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  11. 11
    Finite impulse response optimizers for solving optimization problems

    Finite impulse response optimizers for solving optimization problems by Ab Rahman, Tasiransurini

    Published 2019
    “…Selecting optimal parameters’ values may improve an algorithm’s performance. …”
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  12. 12
    Finite impulse response optimizers for solving optimization problems

    Finite impulse response optimizers for solving optimization problems by Tasiransurini, Ab Rahman

    Published 2019
    “…Selecting optimal parameters’ values may improve an algorithm’s performance. …”
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  13. 13
    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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  14. 14
    A simple and fast algorithm to estimate effective anisotropy parameters

    A simple and fast algorithm to estimate effective anisotropy parameters by Prajapati, S., Ghosh, D.

    Published 2014
    “…To resolve above issues, we implement the modified and extended Backus's averaging algorithm to estimate the effective stiffness parameters and anisotropic parameters, which is very consistent with the well logs and successfully distinguished potential reservoir embedded by shale.…”
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  15. 15
    Estimation of small-scale kinetic parameters of escherichia coli (E. coli) model by enhanced segment particle swarm optimization algorithm ese-pso

    Estimation of small-scale kinetic parameters of escherichia coli (E. coli) model by enhanced segment particle swarm optimization algorithm ese-pso by Mohammed Adam Kunna, Azrag, Jasni Mohamad, Zain, Tuty Asmawaty, Abdul Kadir, Marina, Yusoff, Jaber, Aqeel Sakhy, Abdlrhman, Hybat Salih Mohamed, Ahmed, Yasmeen Hafiz Zaki, Husain, Mohamed Saad Bala

    Published 2023
    “…As an alternative to the state-of-the-art algorithm, this adjustment improves estimation accuracy. …”
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  16. 16
    Advanced online battery states coestimation using Kalman filter for electric vehicle Applications / Prashant Shrivastava

    Advanced online battery states coestimation using Kalman filter for electric vehicle Applications / Prashant Shrivastava by Prashant , Shrivastava

    Published 2021
    “…In addition to co-estimation of SOC and SOE in the first method, the SOP estimation is performed by using identified Rint battery model parameters using the forgetting factor recursive least square (FFRLS) algorithm. …”
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  17. 17
    Effect of displacement vector in the direction of arrival estimation / Nor Syaliza Talha

    Effect of displacement vector in the direction of arrival estimation / Nor Syaliza Talha by Talha, Nor Syaliza

    Published 2007
    “…The algorithms used in detecting the DOA are the Multiple Signal Classification (MUSIC) and Estimation of Signal Parameters via Rotational invariance Techniques (ESPRIT). …”
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  18. 18
    Effect of displacement vector in the direction of arrival estimation / Nor Syaliza Talha

    Effect of displacement vector in the direction of arrival estimation / Nor Syaliza Talha by Talha, Nor Syaliza

    “…The algorithms used in detecting the DOA arc the Multiple Signal Classification (MUSIC) and Estimation of Signal Parameters via Rotational invariancc Techniques (ESPRIT). …”
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  19. 19
    Multi-Agent cubature Kalman optimizer: A novel metaheuristic algorithm for solving numerical optimization problems

    Multi-Agent cubature Kalman optimizer: A novel metaheuristic algorithm for solving numerical optimization problems by Zulkifli, Musa, Zuwairie, Ibrahim, Mohd Ibrahim, Shapiai

    Published 2024
    “…CTT can use small values for parameters P(0), Q, and R, so CKF was developed to overcome KF and other estimation algorithms. …”
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  20. 20
    Prior selection for Gumbel distribution parameters using multiple-try metropolis algorithm for monthly maxima PM10 data

    Prior selection for Gumbel distribution parameters using multiple-try metropolis algorithm for monthly maxima PM10 data by Mohd Amin, Nor Azrita, Adam, Mohd Bakri, Ibrahim, Noor Akma

    Published 2013
    “…The main objective is to explore the accuracy of the parameter estimation to the change of priors and compare the results with a classical likelihood-based analysis. …”
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