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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
    A simulation based fly optimisation algorithm for swarms of mini autonomous surface vehicles application

    A simulation based fly optimisation algorithm for swarms of mini autonomous surface vehicles application by Zainal Abidin, Zulkifli

    Published 2011
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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
    Energy and Cost Integration for Multi-Objective Optimisation in a Sustainable Turning Process

    Energy and Cost Integration for Multi-Objective Optimisation in a Sustainable Turning Process by Bagaber, Salem Abdullah, A. R., Yusoff

    Published 2018
    “…Quality is represented by surface roughness. The multi-objective optimisation using Response Surface Methodology (RSM) was compared with the Non-Sorted Genetic Algorithm II (NSGA II) before experimental confirmation tests were made. …”
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  9. 9
    Application of response surface methodology coupled with genetic algorithm in the optimization of cutting conditions for surface roughness in end-milling of Inconel 718 using coated WC-co inserts

    Application of response surface methodology coupled with genetic algorithm in the optimization of cutting conditions for surface roughness in end-milling of Inconel 718 using coate... by Ishtiyaq, M. H., Amin, A. K. M. Nurul, Karim, A.N. Mustafizul, Ginta, Turnad Lenggo, Patwari, Muhammed Anayet Ullah

    Published 2007
    “…This paper presents an efficient method of optimisation of surface roughness in end milling of Inconel 718 using TiAlN coated inserts under dry conditions by coupling response surface methodology (RSM) with genetic algorithm (GA). …”
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  10. 10
    Multi-Objective Optimisation of CNC Milling Process for Al 6061 using Modified NSGA-II

    Multi-Objective Optimisation of CNC Milling Process for Al 6061 using Modified NSGA-II by M. F. F., Ab Rashid, N. M. Zuki, N. M., A. N. M., Rose

    Published 2016
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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
    Optimisation of tilt angles for fixed PV Panel via computational algorithm

    Optimisation of tilt angles for fixed PV Panel via computational algorithm by Tang, Hong Wei

    Published 2022
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  13. 13
    Local-based stereo matching algorithm using multi-cost pyramid fusion, hybrid random aggregation and hierarchical cluster-edge refinement

    Local-based stereo matching algorithm using multi-cost pyramid fusion, hybrid random aggregation and hierarchical cluster-edge refinement by Kadmin, Ahmad Fauzan

    Published 2023
    “…Similar to how human eyes and binocular vision perceive depth, the visual disparity information obtained from this pair of images captured by the cameras represents the impression of perceived depth. The stereo vision algorithm computes disparity using local, global, and semiglobal optimisation methods established by the researcher. …”
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  14. 14
    Energy and cost integration model for multi-objective optimisation in turning process of stainless steel 316

    Energy and cost integration model for multi-objective optimisation in turning process of stainless steel 316 by Bagaber, Salem Salah Abdullah

    Published 2019
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  15. 15
    B-spline curve fitting with different parameterization methods

    B-spline curve fitting with different parameterization methods by Kheng, Jia Shen

    Published 2020
    “…After generating control points, distance between the generated and original data points is used to identify the error of the algorithm. Later, genetic algorithm and differential evolution optimization are used to optimise the error of the curve. …”
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  16. 16
    Analytical Modelling And Efficiency Optimisation Of Permanent Magnet Synchronous Machine Using Particle Swarm Optimisation

    Analytical Modelling And Efficiency Optimisation Of Permanent Magnet Synchronous Machine Using Particle Swarm Optimisation by Ling, Poh Ping

    Published 2018
    “…From PSO study, the four machine design variables has been simultaneously optimised and successfully produced parameters for a performance-optimised machine. …”
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  17. 17
    Theory-guided machine learning for predicting and minimising surface settlement caused by the excavation of twin tunnels / Chia Yu Huat

    Theory-guided machine learning for predicting and minimising surface settlement caused by the excavation of twin tunnels / Chia Yu Huat by Chia , Yu Huat

    Published 2024
    “…This is due to the data generated from the numerical model possess the pattern for the ML algorithm ease of prediction. In addition, Coati Optimization algorithm, Particle Swarm Opimisation (PSO) and Bayesian Optimsiation (BO) are integrated to identify optimal parameters and minimize settlement during twin tunnel excavation and GBT with the optimisation algorithm has shown consistent capability identifying the least SS induced by twin tunnels Keyword: …”
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  18. 18
    Multi-objectives process optimization in end milling process of aluminium alloy 6061-T6 using genetic algorithm

    Multi-objectives process optimization in end milling process of aluminium alloy 6061-T6 using genetic algorithm by W., Safiei, Rahman, M. M., M.Y., Ali

    Published 2024
    “…The optimum parameters were determined according to the design flow, constraints value and mathematical algorithm. Based on MOGA-II analysis, every workflow generated 1600 feasible solutions for optimization that meet the design space requirement. …”
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  19. 19
    Characterisation and model updating of modal parameters of a car hood structure / Mohamad Shamsul Azraf Sulaiman

    Characterisation and model updating of modal parameters of a car hood structure / Mohamad Shamsul Azraf Sulaiman by Sulaiman, Mohamad Shamsul Azraf

    Published 2020
    “…The finite element model of Case Study 3 was then used in the optimisation method using model updating with MSC NASTRAN SOL200 algorithm to compute the sensitivity analysis based on the input parameters of the spot weld and adhesive joints and the body of the car hood structure. …”
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  20. 20
    Homeostatic-inspired controller algorithm for a hybrid-driven autonomous underwater glider

    Homeostatic-inspired controller algorithm for a hybrid-driven autonomous underwater glider by Isa, Khalid

    Published 2015
    “…The controller is able to adapt efficiently to the dynamically changing conditions and is able to compensate the disturbance from water currents. The controller algorithm has been designed based on the human innate control mechanism by integrating three artificial systems: the artificial neural network (ANN), artificial endocrine system (AES), and artificial immune system (AIS). …”
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