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  1. 1
    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
    “…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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  2. 2
    Optimized clustering with modified K-means algorithm

    Optimized clustering with modified K-means algorithm by Alibuhtto, Mohamed Cassim

    Published 2021
    “…However, the choice of k is a prominent problem in the process of the k-means algorithm. …”
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    Thesis
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  3. 3
    Enhanced Flipping Technique to Reduce Variability in Image Steganography

    Enhanced Flipping Technique to Reduce Variability in Image Steganography by Kamil S., Abdullah S.N.H.S., Hasan M.K., Bohani F.A.

    Published 2023
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  4. 4
    Gas Identi cation by Using a Cluster-k-Nearest-Neighbor

    Gas Identi cation by Using a Cluster-k-Nearest-Neighbor by Brahim Belhaouari, samir

    Published 2009
    “…We find 98.7% of accuracy in the classification of 6 different types of Gas by using K-means cluster algorithm and we find almost the same by using the new clustering algorithm.…”
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  5. 5
    Taguchi?s T-method with Normalization-Based Binary Bat Algorithm

    Taguchi?s T-method with Normalization-Based Binary Bat Algorithm by Marlan Z.M., Jamaludin K.R., Harudin N.

    Published 2025
    Conference paper
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  6. 6
    The performance of Taguchi�s T-method with binary bat algorithm based on great value priority binarization for prediction

    The performance of Taguchi�s T-method with binary bat algorithm based on great value priority binarization for prediction by Marlan Z.M., Ramlie F., Jamaludin K.R., Harudin N.

    Published 2023
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  7. 7
    Efficient genetic partitioning-around-medoid algorithm for clustering

    Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

    Published 2019
    “…However, the complexity of the kmedoid based algorithms in general is more than the complexity of the k-means based algorithms. …”
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  8. 8
    Web-based expert system for material selection of natural fiber- reinforced polymer composites

    Web-based expert system for material selection of natural fiber- reinforced polymer composites by Ahmed Ali, Basheer Ahmed

    Published 2015
    “…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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  9. 9
    HABCSm: A Hamming Based t -way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation

    HABCSm: A Hamming Based t -way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation by Alazzawi, A.K., Rais, H.M., Basri, S., Alsariera, Y.A., Capretz, L.F., Imam, A.A., Balogun, A.O.

    Published 2021
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  10. 10
    HABCSm: A Hamming Based t -way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation

    HABCSm: A Hamming Based t -way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation by Alazzawi, A.K., Rais, H.M., Basri, S., Alsariera, Y.A., Capretz, L.F., Imam, A.A., Balogun, A.O.

    Published 2021
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  11. 11
    On Newton-Raphson formulation and algorithm for displacement based structural dynamics problem with quadratic damping nonlinearity

    On Newton-Raphson formulation and algorithm for displacement based structural dynamics problem with quadratic damping nonlinearity by Koh, K.J., Yasreen, A.Y.M.

    Published 2017
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  12. 12
    A Comparative Study Of Fuzzy C-Means And K-Means Clustering Techniques

    A Comparative Study Of Fuzzy C-Means And K-Means Clustering Techniques by Sharifah Sakinah, Syed Ahmad

    Published 2014
    “…Next, we also optimize the fuzzification variable, m in FCM algorithm in order to improve the clustering performance. …”
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  13. 13
    FPGA implementation of variable precision Euclid’s GCD algorithm

    FPGA implementation of variable precision Euclid’s GCD algorithm by Al-Haija, Qasem Abu, Syed Ahmad Abdul Rahman, Sharifah Mumtazah, Alfarran, Islam

    Published 2017
    “…Methodology: In this paper, we implement a fast GCD coprocessor based on Euclid's method with variable precisions (32-bit to 1024-bit). …”
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  14. 14
    A machine learning approach of predicting high potential archers by means of physical fitness indicators

    A machine learning approach of predicting high potential archers by means of physical fitness indicators by Muazu Musa, Rabiu, Abdul Majeed, Anwar P.P., Taha, Zahari, Chang, Siow Wee, Ab. Nasir, Ahmad Fakhri, Abdullah, Mohamad Razali

    Published 2019
    “…k-nearest neighbour (k-NN) has been shown to be an effective learning algorithm for classification and prediction. …”
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  15. 15
    A machine learning approach of predicting high potential archers by means of physical fitness indicators

    A machine learning approach of predicting high potential archers by means of physical fitness indicators by Musa, Rabiu Muazu, Anwar, P. P. Abdul Majeed, Zahari, Taha

    Published 2019
    “…k-nearest neighbour (k-NN) has been shown to be an effective learning algorithm for classification and prediction. …”
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  16. 16
    Design optimization of valve timing at various engine speeds using Multi-Objective Genetic Algorithm (MOGA)

    Design optimization of valve timing at various engine speeds using Multi-Objective Genetic Algorithm (MOGA) by Mohiuddin, A. K. M., Ashour, Ahmed Aly Ibrahim Shaaban, Yap, Haw Shin

    Published 2008
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  17. 17
    Deterministic Image Enhancement Based on Fuzzy Method

    Deterministic Image Enhancement Based on Fuzzy Method by Libao Yang, Suzelawati Zenian, Rozaimi Zakaria

    Published 2022
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  18. 18
    Characterization of water quality conditions in the Klang River Basin, Malaysia using self organizing map and K-means algorithm

    Characterization of water quality conditions in the Klang River Basin, Malaysia using self organizing map and K-means algorithm by Mohd Sharif, Sharifah, Mohd Kusin, Faradiella, Asha’ari, Zulfa Hanan, Aris, Ahmad Zaharin

    Published 2015
    “…The self organizing map (SOM) combined with the K-means algorithm arranged the data based on the relationships of 25 variables. …”
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  19. 19
    A comparative effectiveness of hierarchical and non-hierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions

    A comparative effectiveness of hierarchical and non-hierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions by Zun, Liang Chuan, Fam, Soo Fen, Wan Yusof, Wan Nur Syahidah, Senawi, Azlyna, Mohd Akramin, Mohd Romlay, Wendy, Ling Shinyie, Tan, Lit Ken

    Published 2022
    “…In practice, these 20 rain gauge stations can be categorised into two statistically homogeneous rainfall regions, namely distinct spatial and temporal variability in the rainfall amounts. The results of the analysis show that Forgy K-means non-hierarchical (FKNH), Hartigan-Wong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. …”
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  20. 20
    A comparative effectiveness of hierarchical and non-hierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions

    A comparative effectiveness of hierarchical and non-hierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions by Chuan, Zun Liang, Wan Nur Syahidah, Wan Yusoff, Azlyna, Senawi, Mohd Akramin, Mohd Romlay, Fam, Soo-Fen, Wendy Ling, Shinyie, Tan Lit, Ken

    Published 2022
    “…In practice, these 20 rain gauge stations can be categorised into two statistically homogeneous rainfall regions, namely distinct spatial and temporal variability in the rainfall amounts. The results of the analysis show that Forgy K-means non-hierarchical (FKNH), Hartigan- Wong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. …”
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