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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
    “…The thesis also provides a two different implementation of the modified FLEX algorithm using a java based parsers and XQuery implementation. …”
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  2. 2
    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
    “…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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  3. 3
    Scalable approach for mining association rules from structured XML data

    Scalable approach for mining association rules from structured XML data by Abazeed, Ashraf Riad, Mamat, Ali, Sulaiman, Md. Nasir, Ibrahim, Hamidah

    Published 2009
    “…Many techniques have been proposed to tackle the problem of mining XML data we study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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  4. 4
    Mining association rules from structured XML data

    Mining association rules from structured XML data by Abazeed, Ashraf Riad, Mamat, Ali, Sulaiman, Md. Nasir, Ibrahim, Hamidah

    Published 2009
    “…Many techniques have been proposed to tackle the problem of mining XML data. We study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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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
    Image clustering comparison of two color segmentation techniques

    Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

    Published 2010
    “…The clustering research is regarding the area of data mining and implementation of the clustering algorithms. …”
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  7. 7
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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  8. 8
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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  9. 9
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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  10. 10
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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  11. 11
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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  12. 12
    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
    “…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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  13. 13
    A spatial decision support system framework for optimization of cropping pattern and water resources allocation at pasargard plains, fars province, Iran

    A spatial decision support system framework for optimization of cropping pattern and water resources allocation at pasargard plains, fars province, Iran by Ghasemi, Mohammad Mehdi

    Published 2014
    “…Furthermore, the Policy Analysis Matrix (PAM) was used as a module to limit the number of possible cropping decisions based on social and economic analyses. …”
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  14. 14
    Surveillance camera placement optimization using Particle Swarm Optimization (PSO) algorithm and Mixed-Integer Linear Programming (MILP) model / `Ain Safia Roslan

    Surveillance camera placement optimization using Particle Swarm Optimization (PSO) algorithm and Mixed-Integer Linear Programming (MILP) model / `Ain Safia Roslan by Roslan, `Ain Safia

    Published 2024
    “…MATLAB was chosen as the primary software due to its robust capabilities in numerical computing and optimization, enabling efficient implementation and analysis of both algorithms. The study applied these optimization techniques to various Binary Integer Programming (BIP) matrix sizes (11×9, 39×24, and 172×49) representing the same 2D layouts, to evaluate their performance in different spatial configurations. …”
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  15. 15
    Workload performance evaluation of large spatial database for DSS based disaster management

    Workload performance evaluation of large spatial database for DSS based disaster management by Rohman, Muhammad Syaifur

    Published 2017
    “…Besides, the results of the evaluation using confusion matrix has resulted in excellent accuracy as well as improvement in execution time. …”
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  16. 16
    Spatial variations prediction in carbonate porosity using artificial neural network: Subis Limestones, Sarawak, Malaysia

    Spatial variations prediction in carbonate porosity using artificial neural network: Subis Limestones, Sarawak, Malaysia by Ali, Y., Padmanabhan, E., Andriamihaja, S., Faisal, A.

    Published 2019
    “…Therefore, this paper introduced an algorithm for predicting spatial variations in pore network. …”
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  17. 17
    Spatial variations prediction in carbonate porosity using artificial neural network: Subis Limestones, Sarawak, Malaysia

    Spatial variations prediction in carbonate porosity using artificial neural network: Subis Limestones, Sarawak, Malaysia by Ali, Y., Padmanabhan, E., Andriamihaja, S., Faisal, A.

    Published 2019
    “…Therefore, this paper introduced an algorithm for predicting spatial variations in pore network. …”
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  18. 18
    Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels

    Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels by Uwaechia, Anthony Ngozichukwuka, Mahyuddin, Nor Muzlifah, Ain, Mohd Fadzil, Abdul Latiff, Nurul Muazzah, Za'bah, Nor Farahidah

    Published 2019
    “…In this paper, by exploiting the highly correlated spatial structure of the obtained array response vectors and sparsity of the multipath signal components of the massive MIMO-OFDM channels, a modified spatial basis expansion model (modified-SBEM) is introduced. …”
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  19. 19
    Computational approach via half-sweep and preconditioned aor for fractional diffusion

    Computational approach via half-sweep and preconditioned aor for fractional diffusion by Andang Sunarto, Praveen Agarwal, Jumat Sulaiman, Jackel Vui Lung Chew

    Published 2022
    “…From the formulated half-sweep Caputo approximation to the time-fractional diffusion equation, a linear system corresponds to the equation contains a large and sparse coefficient matrix that needs to be solved efficiently. We construct a preconditioned matrix based on the first matrix and develop a preconditioned accelerated over relaxation (PAOR) algorithm to achieve a high convergence solution. …”
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  20. 20
    Block based low complexity iterative QR precoder structure for Massive MIMO

    Block based low complexity iterative QR precoder structure for Massive MIMO by Mok, Li Suet

    Published 2021
    “…In this thesis, we attempt to reduce the complexity of the BD schemes by partitioning channel matrix into corresponding square matrix. We make use of QR-based BD method to precode in a lower dimension channel matrix rather than computing all of them. …”
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