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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
    Different mutation and crossover set of genetic programming in an automated machine learning

    Different mutation and crossover set of genetic programming in an automated machine learning by Masrom, S., Mohamad, M., Hatim, S.M., Baharun, N., Omar, N., Abd. Rahman, A.S.

    Published 2020
    “…One of the progressing works for automated machine learning improvement is the inclusion of evolutionary algorithm such as Genetic Programming. The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm. …”
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  8. 8
    Different mutation and crossover set of genetic programming in an automated machine learning

    Different mutation and crossover set of genetic programming in an automated machine learning by Masrom, S., Mohamad, M., Hatim, S.M., Baharun, N., Omar, N., Abd. Rahman, A.S.

    Published 2020
    “…One of the progressing works for automated machine learning improvement is the inclusion of evolutionary algorithm such as Genetic Programming. The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm. …”
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  9. 9
    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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  10. 10
    Tourist Trip Design Problem with user preference and popularity: a case study of Langkawi Island / Nabilah Anuar Ahmad and Huda Zuhrah Ab. Halim

    Tourist Trip Design Problem with user preference and popularity: a case study of Langkawi Island / Nabilah Anuar Ahmad and Huda Zuhrah Ab. Halim by Anuar Ahmad, Nabilah, Ab. Halim, Huda Zuhrah

    Published 2023
    “…This study proposed enhancement in the greedy algorithm approach (Benjamin et al., 2019). A greedy algorithm solves a problem that selects the most appropriate option based on the current situation. …”
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  11. 11
    Implementation of Color Filtering on FPGA

    Implementation of Color Filtering on FPGA by Mohd Shukor, Mohd Nasir, Lo, H. H., Sebastian, Patrick

    Published 2007
    “…The second is the double band-pass-filter algorithm. Work is currently conducted to quantify the effectiveness of the band-pass filtering algorithm on FPGA before proceeding to test and implement the triple and quadruple band-pass filtering methods.…”
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  12. 12
    Nonlinear auto-regressive model structure selection using binary particle swarm optimization algorithm / Ahmad Ihsan Mohd Yassin

    Nonlinear auto-regressive model structure selection using binary particle swarm optimization algorithm / Ahmad Ihsan Mohd Yassin by Mohd Yassin, Ahmad Ihsan

    Published 2014
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  13. 13
    Economical optimization of conductor selection in planning radial distribution networks

    Economical optimization of conductor selection in planning radial distribution networks by Islam, S.J., Ghani, M.R.A.

    Published 1999
    “…A new computer algorithm and program is presented for selection of optimal conductor type and size for each feeder segment. …”
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  14. 14
    ECONOMICAL OPTIMIZATION OF CONDUCTOR SELECTION IN PLANNING RADIAL DISTRIBUTION NETWORKS

    ECONOMICAL OPTIMIZATION OF CONDUCTOR SELECTION IN PLANNING RADIAL DISTRIBUTION NETWORKS by Ab Ghani, Mohd Ruddin

    Published 1999
    “…A new computer algorithm and program is presented for selection of optimal conductor type and size for each feeder segment. …”
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  15. 15
    Implementation of color filtering on FPGA

    Implementation of color filtering on FPGA by P., Sebastian, M.N.B.M., Shukor, H.H., Lo

    Published 2007
    “…The second is the double band-pass-filter algorithm. Work is currently conducted to quantify the effectiveness of the band-pass filtering algorithm on FPGA before proceeding to test and implement the triple and quadruple band-pass filtering methods. …”
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  16. 16
    An evaluation of Monte Carlo-based hyper-heuristic for interaction testing of industrial embedded software applications.

    An evaluation of Monte Carlo-based hyper-heuristic for interaction testing of industrial embedded software applications. by S. Ahmed, Bestoun, Enoiu, Eduard, Afzal, Wasif, Kamal Z., Zamli

    Published 2020
    “…Current implementations rely on the memory-less selection that can be counterproductive as the selected search operator may not (historically) be the best performing operator for the current search instance. …”
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  17. 17
    BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning

    BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning by Chimeleze C., Jamil N., Ismail R., Lam K.-Y., Teh J.S., Samual J., Akachukwu Okeke C.

    Published 2023
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  18. 18
    Hybrid evolutionary-dolphin echolocation programming for sizing optimization of stand-alone photovoltaic systems / Zulkifli Othman

    Hybrid evolutionary-dolphin echolocation programming for sizing optimization of stand-alone photovoltaic systems / Zulkifli Othman by Othman, Zulkifli

    Published 2021
    “…Subsequently, the CI-based sizing algorithm, known as Evolutionary-Dolphin Echolocation Programming Sizing Algorithm (EDEPSA) is formulated to determine the optimal models of each system component such that either Performance Ratio (PR) or Levelized Cost of Electricity (LCOE) of the system is optimized. …”
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  19. 19
    Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation

    Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation by Mat Jani H., Lee S.P.

    Published 2023
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  20. 20
    Electroencephalogram signal interpretation system for mobile robot

    Electroencephalogram signal interpretation system for mobile robot by Hasan, Intan Helina

    Published 2013
    “…Using sample datasets, the EEG signal is analyzed to determine the most suitable scalp area for P300 detection, while optimization with genetic algorithm (GA) is developed to select best four channels. …”
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