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  1. 1
    Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms

    Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms by Shaikh, Abdul Aabid, Raheman, Md Abdul, Hrairi, Meftah, Baig, Muneer

    Published 2024
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  2. 2
    DEVELOPMENT OF COMPOSITIONAL MODEL FOR PREDICTING VISCOSITY OF CRUDE OILS USING POLYNOMIAL NEURAL NETWORKS (PNN) INDUCED BY GROUP METHOD OF DATA HANDLING (GMDH)

    DEVELOPMENT OF COMPOSITIONAL MODEL FOR PREDICTING VISCOSITY OF CRUDE OILS USING POLYNOMIAL NEURAL NETWORKS (PNN) INDUCED BY GROUP METHOD OF DATA HANDLING (GMDH) by Wen Pin, Yong

    Published 2011
    “…GMDH is an inductive algorithm for computer-based mathematical modeling using neural network with active neurons that optimizes model coefficients for predetermine mathematical equation and selects the optimal model complexity. …”
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  3. 3
    A comparative study of deep learning algorithms in univariate and multivariate forecasting of the Malaysian stock market

    A comparative study of deep learning algorithms in univariate and multivariate forecasting of the Malaysian stock market by Mohd. Ridzuan Ab. Khalil, Azuraliza Abu Bakar

    Published 2023
    “…This study aims to develop a univariate and multivariate stock market forecasting model using three deep learning algorithms and compare the performance of those models. …”
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  4. 4
    Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed co-gasification of biomass wastes from oil palm

    Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed... by Ayodele, B.V., Mustapa, S.I., Kanthasamy, R., Mohammad, N., AlTurki, A., Babu, T.S.

    Published 2022
    “…Whereas the best performance in terms of prediction of the syngas composition was obtained using the NLRQM algorithm with an inbuilt SQP and LM algorithms. …”
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  5. 5
    Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed co-gasification of biomass wastes from oil palm

    Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed... by Ayodele B.V., Mustapa S.I., Kanthasamy R., Mohammad N., AlTurki A., Babu T.S.

    Published 2023
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  6. 6
    A comparative study on various ANN optimization algorithms for magnetorheological elastomer carbonyl iron particle concentration estimation

    A comparative study on various ANN optimization algorithms for magnetorheological elastomer carbonyl iron particle concentration estimation by Kasma Diana, Saharuddin, Mohd Hatta, Mohammed Ariff, Bahiuddin, Irfan, Nurhazimah, Nazmi, Mohd Azizi, Abdul Rahman, Mohd Ibrahim, Shapiai, Fauzan, Ahmad, Sarah Atifah, Saruchi

    Published 2024
    “…Therefore, this paper proposed a carbonyl iron particle (CIP) concentration based MRE prediction model using neural network algorithm. Neural network-based machine learning model is more approachable compared to conventional mathematical modelling approach due to easily identify trends and pattern while handling multi-variety data. …”
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  7. 7
    Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed co-gasification of biomass wastes from oil palm

    Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed... by Ayodele, B.V., Mustapa, S.I., Kanthasamy, R., Mohammad, N., AlTurki, A., Babu, T.S.

    Published 2022
    “…Whereas the best performance in terms of prediction of the syngas composition was obtained using the NLRQM algorithm with an inbuilt SQP and LM algorithms. …”
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  8. 8
    Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification

    Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification by Jamaluddin, Hishamuddin, Abd. Samad, M. F., Ahmad, Robiah, Yaacob, M. S.

    Published 2007
    “…The flexibility of a genetic algorithm allows various strategies to be applied to it. …”
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  9. 9
    Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification

    Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification by Jamaluddin, H., Samad, M. F. A., Ahmad, R., Yaacob, M. S.

    Published 2007
    “…The flexibility of a genetic algorithm allows various strategies to be applied to it. …”
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  10. 10
    Evaluating A New Adaptive Group Lasso Imputation Technique For Handling Missing Values In Compositional Data

    Evaluating A New Adaptive Group Lasso Imputation Technique For Handling Missing Values In Compositional Data by Tian, Ying

    Published 2024
    “…The complexity of compositional data with missing values renders traditional estimation methods inadequate. …”
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  11. 11
    Prediction of optimum compositions of parenteral nanoemulsion system loaded with low solubility drug for treatment of schizophrenia by artificial neural network

    Prediction of optimum compositions of parenteral nanoemulsion system loaded with low solubility drug for treatment of schizophrenia by artificial neural network by Samiun, Wan Sarah, Basri, Mahiran, Masoumi, Hamid Reza Fard, Khairudin, Nurshafira

    Published 2016
    “…The particle size of samples in various compositions was measured as output. To obtain the optimum topologies, ANNs were trained by Incremental Back Propagation (IBP), Genetic Algorithm (GA), Batch Back Propagation (BBP), Quick Propagation (QP), and Levenberg-Marquardt (LM) algorithms for testing data set. …”
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  12. 12
    Artificial neural networks for burst pressure strength of corroded subsea pipelines repaired with composite fiber-reinforced polymer patches / Mohd Fakri Muda

    Artificial neural networks for burst pressure strength of corroded subsea pipelines repaired with composite fiber-reinforced polymer patches / Mohd Fakri Muda by Muda, Mohd Fakri

    Published 2023
    “…A computational model for predicting the burst pressure strength of repaired pipelines with composite FRP patches was employed using the ANN algorithm. …”
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  13. 13
    Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification

    Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification by Abd Samad, Md Fahmi

    Published 2007
    “…The flexibility of a genetic algorithm allows various strategies to be applied to it. …”
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  14. 14
    Prediction Of Leaf Mechanical Properties Based On Geometry Features With Data Mining

    Prediction Of Leaf Mechanical Properties Based On Geometry Features With Data Mining by H’ng, Choo Wooi

    Published 2019
    “…The linear models and rules developed from the M5P algorithm were adopted for the FT indicator prediction modelling of 14 attributes. …”
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  15. 15
    A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction

    A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction by Hammid, Ali Thaeer, M. H., Sulaiman, Awad, Omar I.

    Published 2018
    “…Furthermore, the performance of the suggested robust firefly algorithm model is better than previously mentioned models in terms of speed and accuracy of prediction.…”
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  16. 16
    Self learning neuro-fuzzy modeling using hybrid genetic probabilistic approach for engine air/fuel ratio prediction

    Self learning neuro-fuzzy modeling using hybrid genetic probabilistic approach for engine air/fuel ratio prediction by Al-Himyari, Bayadir Abbas

    Published 2017
    “…A fitness function is proposed to deal with multi-objective problem without weight using a new composition method. The model was compared to other learning algorithms for NFS such as Fuzzy c-means (FCM) and grid partition algorithm. …”
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  17. 17
    Pemodelan data indeks komposit Kuala Lumpur menggunakan neurofuzzy

    Pemodelan data indeks komposit Kuala Lumpur menggunakan neurofuzzy by Mohd. Yunos, Zuriahati

    Published 2006
    “…In this research, a comprehensive pre-processing data modeling of stock market is developed to acquire granular informations that represent the behaviour of the data that is to be fed to the classifier. …”
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  18. 18
    Data Analysis and Machine Learning Algorithms Evaluation for Bioliq AI-based Predictive Tool

    Data Analysis and Machine Learning Algorithms Evaluation for Bioliq AI-based Predictive Tool by Samuel Simbine, Augusto

    Published 2019
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  19. 19
    Impact of data balancing and feature selection on machine learning based network intrusion detection

    Impact of data balancing and feature selection on machine learning based network intrusion detection by Barkah, Azhari Shouni, Selamat, Siti Rahayu, Zainal Abidin, Zaheera, Wahyudi, Rizki

    Published 2023
    “…Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) algorithms duplicate the data and construct synthetic data for the minority classes. …”
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  20. 20
    Data mining for structural damage identification using hybrid artificial neural network based algorithm for beam and slab girder / Meisam Gordan

    Data mining for structural damage identification using hybrid artificial neural network based algorithm for beam and slab girder / Meisam Gordan by Meisam , Gordan

    Published 2020
    “…In the modeling phase, amongst all DM algorithms, the applicability of machine learning, artificial intelligence and statistical data mining techniques were examined using Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Tree (CART) to detect the hidden patterns in vibration data. …”
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