Search Results - (( java application mining algorithm ) OR ( parameter selection technique algorithm ))

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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
    An Alternative Algorithm for Soft Set Parameter Selection using Special Order

    An Alternative Algorithm for Soft Set Parameter Selection using Special Order by Mohammed, Mohammed Adam Taheir, Wan Maseri, Wan Mohd, Ruzaini, Abdullah Arshah, Mungad, M., Sutoyo, Edi, Chiroma, Haruna

    Published 2015
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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
    Integrated ACOR/IACOMV-R-SVM Algorithm

    Integrated ACOR/IACOMV-R-SVM Algorithm by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2017
    “…The first algorithm, ACOR-SVM, will tune SVM parameters, while the second IACOMV-R-SVM algorithm will simultaneously tune SVM parameters and select the feature subset. …”
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  9. 9
    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
    “…These models are developed by integrating multilayer perceptron neural network and evolutionary optimization techniques. Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. …”
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  10. 10
    Hybrid ACO and SVM algorithm for pattern classification

    Hybrid ACO and SVM algorithm for pattern classification by Alwan, Hiba Basim

    Published 2013
    “…The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. …”
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  11. 11
    A study on the parameter selection of bat algorithm in in optimizing parameters in camera auto calibration problem

    A study on the parameter selection of bat algorithm in in optimizing parameters in camera auto calibration problem by Mohd Said, Rahaini, A Aziz, Khairul Azha, Zainal Abidin, Amar Faiz, Mat Jizat, Jessnor Arif, Mohd Khairuddin, Ismail, Widiyanto, Sigit, Abdul Waduth, Mohamed Faisal

    Published 2022
    “…This paper studies the correlation of different parameters selection in Bat Algorithm in solving the camera auto-calibration problem. …”
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  12. 12
    Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System

    Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System by Aljanabi, Mohammad, Mohd Arfian, Ismail, Mezhuyev, Vitaliy

    Published 2020
    “…ITLBO with supervised machine learning (ML) technique was used for feature subset selection (FSS). …”
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  13. 13
    Simultaneous Computation of Model Order and Parameter Estimation for System Identification Based on Gravitational Search Algorithm

    Simultaneous Computation of Model Order and Parameter Estimation for System Identification Based on Gravitational Search Algorithm by Kamil Zakwan, Mohd Azmi, Pebrianti, Dwi, Zuwairie, Ibrahim, Shahdan, Sudin, Sophan Wahyudi, Nawawi

    Published 2015
    “…In this paper, a technique termed as Simultaneous Model Order and Parameter Estimation (SMOPE), which is specifically based on Gravitational Search Algorithm (GSA) is proposed to combine model order selection and parameter estimation in one process. …”
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  14. 14
    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh

    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh by Rawaa Dawoud Hassan, Al-Dabbagh

    Published 2015
    “…The performance of DE algorithm depends heavily on the selected mutation strategy and its associated control parameters. …”
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  15. 15
    Aco-based feature selection algorithm for classification

    Aco-based feature selection algorithm for classification by Al-mazini, Hassan Fouad Abbas

    Published 2022
    “…The ACO feature clustering method utilises the ability of various mechanisms such as intensification and diversification for local and global optimisation to provide highly correlated features. The adaptive technique for ant selection enables the parameter to adaptively change based on the feedback of the search space. …”
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  16. 16
    Mixed variable ant colony optimization technique for feature subset selection and model selection

    Mixed variable ant colony optimization technique for feature subset selection and model selection by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
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  17. 17
    Kernel and multi-class classifiers for multi-floor wlan localisation

    Kernel and multi-class classifiers for multi-floor wlan localisation by Abd Rahman, Mohd Amiruddin

    Published 2016
    “…The main focus of this work is to extend the kernel density approach and implement multi- class machine learning classifiers to improve the localisation accuracy and processing time of the each and overall elements of the proposed technique. For fingerprint database optimisation, novel access point (AP) selection algorithms which are based on variant AP selection are investigated to improve computational accuracy compared to existing AP selection algorithms such as Max-Mean and InfoGain. …”
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  18. 18
    Spatial network k-Nearest neighbor: A survey and future directives

    Spatial network k-Nearest neighbor: A survey and future directives by Borhanuddin B., Solemon B.

    Published 2023
    “…However, among these techniques, which k- NN road network algorithm has the highest rate of query performance based on the selected parameters? …”
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  19. 19
    Monthly rainfall prediction model of Peninsular Malaysia using clonal selection algorithm

    Monthly rainfall prediction model of Peninsular Malaysia using clonal selection algorithm by Rodi, N.S.Noor, Malek, M.A, Ismail, Amelia Ritahani

    Published 2018
    “…The proposed Clonal Selection Algorithm (CSA) is one of the main algorithms in AIS, which inspired on Clonal selection theory in the immune system of human body that includes selection, hyper mutation, and receptor editing processes. …”
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  20. 20
    Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares

    Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares by Uraibi, Hassan Sami

    Published 2009
    “…Kallel et al. ( 2002 ) proposed utilizing the bootstrap technique for model selection. They used the classical bootstrap method to estimate the bootstrap location and the scale parameters based on calculating the Mean of Squared Residual (MSR). …”
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