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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
    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
    “…Clustering analysis has been considered as a useful means for identifying patterns in dataset. The aim for this paper is to propose a comparison study between two well-known clustering algorithms namely fuzzy c-means (FCM) and k-means. …”
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  3. 3
    Comparing means of two non-homogeneous normal populations

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

    Published 1986
    “…The statistic is a funtion of the three M.L. estimates. A simple algorithm which can be implemented on a microcomputer is proposed for calculating the required values. …”
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  4. 4
    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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    Thesis
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  5. 5
    Efficient genetic partitioning-around-medoid algorithm for clustering

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

    Published 2019
    “…These algorithms mostly built upon the partitioning k-means clustering algorithm. …”
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  6. 6
    DESIGN AND IMPLEMENTATION OF INTELLIGENT MONITORING SYSTEMS FOR THERMAL POWER PLANT BOILER TRIPS

    DESIGN AND IMPLEMENTATION OF INTELLIGENT MONITORING SYSTEMS FOR THERMAL POWER PLANT BOILER TRIPS by FIRAS BASIM, ISMAIL ALNAIMI

    Published 2011
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    Thesis
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  7. 7
    ADAPTIVE LINEAR ALGORITHMS FOR POWER SYSTEM FUNDAMENTAL AND HARMONIC ESTIMATION

    ADAPTIVE LINEAR ALGORITHMS FOR POWER SYSTEM FUNDAMENTAL AND HARMONIC ESTIMATION by MUBARAK MOHMMED, HUSSAM ALHAJ

    Published 2015
    “…On the other hand, Least Mean Square (LMS) algorithm is known for its ease of structure, computation and simplicity. …”
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  8. 8
    A proposed variable parameter control chart for monitoring the multivariate coefficient of variation

    A proposed variable parameter control chart for monitoring the multivariate coefficient of variation by Chew, X. Y., Khoo, B. C., Khaw, K. W., Yeong, W. C. *, Chong, Z. L.

    Published 2019
    “…Formulae and algorithms to optimize the various performance measures are discussed. …”
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  9. 9
    Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration

    Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration by Shafika Sultan Abdullah, M.A., Malek, Namiq Sultan Abdullah, A., Mustapha

    Published 2015
    “…The performance of both simulation models were evaluated using statistical coefficients such as the root of mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). …”
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  10. 10
    Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration

    Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration by Abdullah S.S., Malek M.A., Abdullah N.S., Mustapha A.

    Published 2023
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  11. 11
    Maximum 2-satisfiability in radial basis function neural network

    Maximum 2-satisfiability in radial basis function neural network by Shehab Abdulhabib Alzaeemi, Saratha Sathasivam, Mohd Shareduwan Mohd Kasihmuddin, Mohd. Asyraf Mansor

    Published 2020
    “…MAX2SAT is a case of MAX-kSAT and is written in Conjunctive Normal Form (CNF) with two variables in each clause. This paper presents a new paradigm in using MAX2SAT by implementing in Radial Basis Function Neural Network (RBFNN). …”
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  12. 12
    Automated QT interval measurement using modified Pan-Tompkins algorithm with independent isoelectric line approach

    Automated QT interval measurement using modified Pan-Tompkins algorithm with independent isoelectric line approach by Jumahat, Shaliza, Gan, Kok Beng, Misran, Norbahiah, Islam, Mohammad Tariqul, Mahri, Nurhafizah, Ja'afar, Mohd. Hasni

    Published 2020
    “…The algorithm was implemented in Matlab and applied to the 60 seconds duration of 27 records in the PPUKM database with a sampling frequency of 500 Hz. …”
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  13. 13
    Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management

    Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management by almahameed, Bader aldeen, Bisharah, Majdi

    Published 2024
    “…Evaluation metrics such as Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, and R-squared are commonly employed in the assessment of Machine Learning models' performance. …”
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  14. 14
    Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions

    Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

    Published 2017
    “…The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. …”
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  15. 15
    Monitoring the coefficient of variation using a variable sample size EWMA chart

    Monitoring the coefficient of variation using a variable sample size EWMA chart by Muhammad, Anis Nabila, Yeong, Wai Chung, Chong, Zhi Lin, Lim, Sok Li, Khoo, Michael Boon Chong

    Published 2018
    “…CV charts are attracting attention due to their usefulness in monitoring processes with an inconsistent mean and a standard deviation which changes with the mean. …”
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  16. 16
    Removal of heavy metals from water by functionalized carbon nanotubes with deep eutectic solvents: An artificial neural network approach / Seef Saadi Fiyadh

    Removal of heavy metals from water by functionalized carbon nanotubes with deep eutectic solvents: An artificial neural network approach / Seef Saadi Fiyadh by Seef Saadi , Fiyadh

    Published 2019
    “…Moreover, various indicators were implemented to evaluate the ANN model’s productivity including relative root mean square error (RRMSE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE) and relative error (RE). …”
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  17. 17
    Dynamic Economic Dispatch For Power System

    Dynamic Economic Dispatch For Power System by Hussein, Saif Tahseen

    Published 2016
    “…This research finds large variable size DED problems can be easily implemented, PSO method is reliable and is suitable for real-time analysis. …”
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  18. 18
    Towards large scale unconstrained optimization

    Towards large scale unconstrained optimization by Abu Hassan, Malik

    Published 2007
    “…Therefore in dealing with large scale unconstrained problems with a large number of variables, modifications must be made to the standard implementation of the many existing algorithms for the small scale case. …”
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  19. 19
    A NOVEL FORWARD BACKWARD LINEAR PREDICTION ALGORITHM FOR SHORT TERM POWER LOAD FORECAST

    A NOVEL FORWARD BACKWARD LINEAR PREDICTION ALGORITHM FOR SHORT TERM POWER LOAD FORECAST by BAHARUDIN, ZUHAIRI

    Published 2010
    “…The proposed AR-based algorithm divides long data record into short segments and searches for the AR coefficients that simultaneously model the data with the least means squared errors. …”
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  20. 20
    Lightning fault classification for transmission line using support vector machine

    Lightning fault classification for transmission line using support vector machine by Asman, Saidatul Habsah, Ab Aziz, Nur Fadilah, Ab Kadir, Mohd Zainal Abidin, Ungku Amirulddin, Ungku Anisa, Roslan, Nurzanariah, Elsanabary, Ahmed

    Published 2023
    “…The input variables for the models were based on the root mean square (RMS) current duration, voltage dip, and energy wavelet measured at the sending end of a line. …”
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