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  1. 1

    Clustering ensemble learning method based on incremental genetic algorithms by Ghaemi, Reza

    Published 2012
    “…Moreover, experiments demonstrate that final clustering solution generated by the proposed incremental genetic-based clustering ensemble algorithm using the pattern ensemble learning method possess comparative or better clustering accuracy than clustering solutions generated by the incremental genetic-based clustering ensemble algorithms using other recombination operators. …”
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  2. 2

    Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm by Manoharan P., Ravichandran S., Kavitha S., Tengku Hashim T.J., Alsoud A.R., Sin T.C.

    Published 2025
    “…The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. …”
    Article
  3. 3

    A hybrid technique of deep learning neural networks with finite difference method for higher order fractional Volterra-Fredholm integro-differential equations with φ-Caputo operato... by Alsa’Di, Kawthar, Nik Long, Nik Mohd Asri

    Published 2025
    “…This technique uses the Adaptive Moment Estimation Method (Adam) as an optimization algorithm with feed-forward deep learning to minimize the error function and training the model using five layers with different activation functions. …”
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    Fault classification in smart distribution network using support vector machine by Chuan O.W., Ab Aziz N.F., Yasin Z.M., Salim N.A., Wahab N.A.

    Published 2023
    “…In this paper, a machine-learning algorithm known as Support Vector Machine (SVM) for fault type classification in distribution system has been developed. …”
    Article
  6. 6

    Deep learning convolutional neural network algorithms for the early detection and diagnosis of dental caries on periapical radiographs: a systematic review by Musri, Nabila, Christie, Brenda, Ichwan, Solachuddin Jauhari Arief, Cahyanto, Arief

    Published 2021
    “…Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects. …”
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  7. 7

    Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad by Ahmad, Khairul Adilah

    Published 2018
    “…This learning algorithm represents an automatic generation of membership functions and rules from the data. …”
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  8. 8

    A review on machine learning in smart antenna: methods and techniques by Mohammed Sadiq, Sulaiman, Nasri, Mohd Isa, Maryam, Hamidon, Mohd Nizar

    Published 2022
    “…These systems require constant monitoring in order to function properly, allowing users to apply machine learning algorithms to analyse large amounts of data from various antenna settings. …”
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  9. 9

    Penggunaan penggugusan subtraktif bagi menjana peraturan kabur by Agus Priyono, Muhammad Ridwan, Ahmad Jais Alias, Riza Atiq O. K. Rahmat, Azmi Hassan, Mohd. Alauddin Mohd. Ali

    Published 2005
    “…Depending on the system, the assessment of each possibility input might be not necessary if this very seldom or never occur. By using the fuzzy clustering algorithm, membership function could be counted based on two possible clustering methods. …”
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  10. 10
  11. 11

    Polynomial neural network for solving Caputo-conformable fractional Volterra–Fredholm integro-differential equation with three-point non-local boundary conditions by Alsa’di, Kawthar, Nik Long, Nik Mohd Asri, Senu, Norazak, Eshkuvatov, Z.K.

    Published 2025
    “…A hybrid technique, combining a polynomial neural network (PNN) with an extreme learning machine algorithm without using any activation functions, is developed. …”
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  12. 12

    Random forest algorithm for co2 water alternating gas incremental recovery factor prediction by Belazreg, L., Mahmood, S.M., Aulia, A.

    Published 2020
    “…The aim of this paper is using an ensemble machine learning algorithm to develop a WAG incremental recovery factor predictive model that can be used by reservoir engineers to estimate WAG incremental recovery factor prior kick-off of laboratory experiments and comprehensive technical studies. …”
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  13. 13

    Operational structural damage identification using de-noised modal feature in machine learning / Chen Shilei by Chen , Shilei

    Published 2021
    “…By integrating ISMA, both supervised and unsupervised machine learning algorithms were investigated to develop real-time damage identification schemes. …”
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    Machine learning application in predicting anterior cruciate ligament injury among basketball players by Longfei, Guo

    Published 2025
    “…A one-year follow-up was conducted to monitor ACL injury, identifying n=11 injured players. Four machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR)—were developed to predict ACL injury. …”
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  17. 17

    Operational matrix based on orthogonal polynomials and artificial neural networks methods for solving fractal-fractional differential equations by Shloof, Aml Melad Asan

    Published 2024
    “…Initially, the algorithm utilized a truncated series. The values of the unknown coefficients in this truncated power series were then determined using an optimization technique to minimize the criterion function. …”
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  18. 18

    New techniques incorporating computational intelligence based for voltage stability evaluation and improvement in power system / Nur Fadilah Ab. Aziz by Ab. Aziz, Nur Fadilah

    Published 2014
    “…Another new hybrid algorithm that used Evolutionary Programming (EP) termed as Evolutionary Support Vector Machine (ESVM) was also developed for comparative study. …”
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  19. 19

    Fault diagnosis in unbalanced radial distribution networks using generalised regression neural network by Mirzaei, Maryam

    Published 2011
    “…To achieve this goal, the initial or pre-fault condition of the system has to be computed. Using the proposed method, less learning time of PNN is required for classification. …”
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  20. 20

    Penggunaan penggugusan subtraktif bagi menjana peraturan kabur by Priyono, Agus, Ridwan, Purnomo, Alias, Ahmad Jais, Rahmat, Riza Atiq, Hassan, Azmi, Mohd Ali, Mohd Alaudin

    Published 2005
    “…Depending on the system, the assessment of each possibility input might be not necessary if this very seldom or never occur. By using the fuzzy clustering algorithm, membership function could be counted based on two possible clustering methods. …”
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    Article