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  1. 1
    Parallel distributed genetic algorithm development based on microcontrollers framework

    Parallel distributed genetic algorithm development based on microcontrollers framework by Krishnan P.S., Kiong T.S., Koh J.

    Published 2023
    Conference paper
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  2. 2
    Improved normalization and standardization techniques for higher purity in K-means clustering

    Improved normalization and standardization techniques for higher purity in K-means clustering by Dalatu, Paul Inuwa, Fitrianto, Anwar, Mustapha, Aida

    Published 2016
    Subjects: “…Normalization; Standardization; K-means algorithm; Clustering; Purity; Rand index…”
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  3. 3
    Towards the utilization of normalized LMS algorithm in adaptive filter

    Towards the utilization of normalized LMS algorithm in adaptive filter by Misbah Emhammed, Misbah Abdelsalam, Ho, Yih Hwa

    Published 2014
    “…In this paper, we focused on how the development of algorithms helped reduce the level of noise. This in turn led us to utilize the Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) algorithms in order to do so. …”
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  4. 4
    How to Test the Means among Heteroscedastic Normal Populations

    How to Test the Means among Heteroscedastic Normal Populations by Abd. Rahman, Mohd Nawi

    Published 1988
    “…To facilitate the application of the procedure, an efficient estimation algorithm is shown for its means and the corresponding variance-covariance matrix. …”
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  5. 5
    Comparing means of two non-homogeneous normal populations

    Comparing means of two non-homogeneous normal populations by Abd Rahman, Mohd Nawi

    Published 1986
    “…However, it can be attractive to many if some efficient algorithm is available. This paper intends to give an alternative approach for testing the means of two normal populations having unequal variances but whose coefficient of variations are homogeneous. …”
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  6. 6
    A Stochastic Total Least Squares Solution of Adaptive Filtering Problem

    A Stochastic Total Least Squares Solution of Adaptive Filtering Problem by Javed, Shazia, Ahmad, Noor Atinah

    Published 2014
    “…The TLMS algorithm is computationally simpler than the other TLS algorithms and demonstrates a better performance as compared with the least mean square (LMS) and normalized least mean square (NLMS) algorithms. …”
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  7. 7
    A novel quantum calculus-based complex least mean square algorithm (q-CLMS)

    A novel quantum calculus-based complex least mean square algorithm (q-CLMS) by Sadiq, A., Naseem, I., Khan, S., Moinuddin, M., Togneri, R., Bennamoun, M.

    Published 2022
    “…The proposed algorithm is based on Wirtinger calculus and is called as q- Complex Least Mean Square (q-CLMS) algorithm. …”
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  8. 8
    A novel quantum calculus-based complex least mean square algorithm (q-CLMS)

    A novel quantum calculus-based complex least mean square algorithm (q-CLMS) by Sadiq, A., Naseem, I., Khan, S., Moinuddin, M., Togneri, R., Bennamoun, M.

    Published 2022
    “…The proposed algorithm is based on Wirtinger calculus and is called as q- Complex Least Mean Square (q-CLMS) algorithm. …”
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  9. 9
    A novel quantum calculus-based complex least mean square algorithm (q-CLMS)

    A novel quantum calculus-based complex least mean square algorithm (q-CLMS) by Sadiq, A., Naseem, I., Khan, S., Moinuddin, M., Togneri, R., Bennamoun, M.

    Published 2023
    “…The proposed algorithm is based on Wirtinger calculus and is called as q- Complex Least Mean Square (q-CLMS) algorithm. …”
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  10. 10
    Widely linear dynamic quaternion valued least mean square algorithm for linear filtering

    Widely linear dynamic quaternion valued least mean square algorithm for linear filtering by Mohammed, Aldulaimi Haydar Imad

    Published 2017
    “…The new adaptive algorithm is called dynamic quaternion least mean square algorithm (DQLMS) because of the normalization process of the filter input and the variable step-size. …”
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  11. 11
    Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly

    Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly by Zulkifly, Ahmad Zuladzlan

    Published 2019
    “…This project will use fuzzy k-means clustering algorithm to cluster the data because it is easy to implement and have many advantages. …”
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  12. 12
    Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm

    Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm by Dalatu, Paul Inuwa

    Published 2018
    “…We introduced two new approaches to normalization techniques to enhance the K-Means algorithms. …”
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  13. 13
    Integrating genetic algorithms and fuzzy c-means for anomaly detection

    Integrating genetic algorithms and fuzzy c-means for anomaly detection by Chimphlee, Witcha, Abdullah, Abdul Hanan, Sap, Noor Md., Chimphlee, Siriporn, Srinoy, Surat

    Published 2005
    “…Traditional anomaly detection algorithms require a set of purely normal data from which they train their model. …”
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  14. 14
    Improved performance in distributed estimation by convex combination of DNSAF and DNLMS algorithms

    Improved performance in distributed estimation by convex combination of DNSAF and DNLMS algorithms by Ahmad Pouradabi, Amir Rastegarnia, Azam Khalili, Ali Farzamnia

    Published 2022
    “…In diffusion estimation of distributed networks two characteristic parameters are crucial, the speed of convergence and steady-state error. Diffusion normalized least mean square (DNLMS) algorithm has low misadjustment error, but it is slow in convergence. …”
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  15. 15
    New approaches to normalization techniques to enhance k-means clustering algorithm

    New approaches to normalization techniques to enhance k-means clustering algorithm by Dalatu, Paul Inuwa, Midi, Habshah

    Published 2020
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  16. 16
    Comparison of performance and computational complexity of nonlinear active noise control algorithms

    Comparison of performance and computational complexity of nonlinear active noise control algorithms by Sahib, Mouayad A., Raja Ahmad, Raja Mohd Kamil

    Published 2011
    “…The evaluation of the algorithms performance is standardized in terms of the normalized mean square error while the computational complexity is calculated based on the number of multiplications and additions in a single iteration. …”
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  17. 17
    Short term forecasting based on hybrid least squares support vector machines

    Short term forecasting based on hybrid least squares support vector machines by Zuriani, Mustaffa, M. H., Sulaiman, Ernawan, Ferda, Noorhuzaimi, Mohd Noor

    Published 2018
    “…This study assesses the performance of each hybrid algorithms based on three statistical indices viz. Mean Square Error (MSE), Root Mean Square Percentage Error (RMSPE) and Theil’s U which is realized on raw and normalized data set. …”
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  18. 18
    The normalized random map of gradient for generating multifocus image fusion

    The normalized random map of gradient for generating multifocus image fusion by Ismail, ., Kamarul Hawari, Ghazali

    Published 2020
    “…This data has a significant role in predict the initial focus regions. The proposed algorithm successes to supersede difficulties of mathematical equations and algorithms. …”
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  19. 19
    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
    “…They used the classical bootstrap method to estimate the bootstrap location and the scale parameters based on calculating the Mean of Squared Residual (MSR). It is now evident that the classical mean and classical standard deviation are easily affected by the presence of outliers. …”
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  20. 20
    Adaptive interference canceller using analog algorithm with offset voltage

    Adaptive interference canceller using analog algorithm with offset voltage by Mohammed, Alaa Hadi

    Published 2015
    “…They require the utilization of the adaptive algorithms. Algorithms such as Least Mean Square (LMS), Normalized Least Mean Square (NLMS) and Recursive Least Square (RLS) algorithms often have poor numerical properties due to the practical implementation complexities. …”
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