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  1. 1

    Whale optimization algorithm based on tent chaotic map for feature selection in soft sensors by AlRijeb, Mothena Fakhri Shaker, Othman, Mohammad Lutfi, Ishak, Aris, Hassan, Mohd Khair, Albaker, Baraa Munqith

    Published 2025
    “…One of the powerful optimization algorithms that is used for feature selection is the Whale Optimization Algorithm (WOA), which is a nature-inspired metaheuristic optimization algorithm that mimics the social behavior of humpback whales. …”
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  2. 2

    Decision tree and rule-based classification for predicting online purchase behavior in Malaysia / Maslina Abdul Aziz, Nurul Ain Mustakim and Shuzlina Abdul Rahman by Abdul Aziz, Maslina, Mustakim, Nurul Ain, Abdul Rahman, Shuzlina

    Published 2024
    “…The performance of six machine learning models comprising J48, Random Tree, REPTree representing decision trees and JRip, PART, and OneR as rule-based algorithms was assessed. …”
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  3. 3

    Correlation analysis and predictive performance based on KNN and decision tree with augmented reality for nuclear primary cooling process / Ahmad Azhari Mohamad Nor by Mohamad Nor, Ahmad Azhari

    Published 2024
    “…These analyses provide nuanced insights into system operational dynamics and efficiency. Subsequently, predictive models employing k-nearest neighbour and decision tree algorithms are constructed and evaluated based on accuracy, precision, and recall metrics. …”
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  4. 4

    Fuzzy Systems and Bat Algorithm for Exergy Modeling in a Gas Turbine Generator by Alemu Lemma, Tamiru, Mohd Hashim, Fakhruldin

    Published 2011
    “…The fuzzy models are trained applying locally linear model tree algorithm followed by a meta-heuristic nature inspired algorithm called bat algorithm. …”
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  5. 5

    Enhanced dynamic security assessment for power system under normal and fake tripping contingencies. by A. Salih, Qusay

    Published 2019
    “…The hybrid logistic model tree (hybrid LMT) approach proposed in this study combines the symmetrical uncertainties (SU) algorithm and the logistic model tree (LMT) algorithm. …”
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  6. 6

    Development of trees management system using radial basis function neural network for rain forecast by Auzani, Hasnul, Hashim, Khairusy Syakirin Has-Yun, Mohd Nazri, Farah Aniza

    Published 2021
    “…Radial Basis Function Neural Network (RBFNN) algorithm was used in this study to predict rainfall and the main focus of this study is to analyze the factor that affects the performance of neural model. …”
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  7. 7

    Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms by Al-Rawashdeh, Mohammad, Al Nawaiseh, Moh’d, Yousef, Isam, Bisharah, Majdi, Alkhadrawi, Sajeda, Al-Bdour, Hamza

    Published 2024
    “…With 89.39 test accuracy and 99.82 train accuracy, the Random Forest model performs well. The Decision Tree model has 89.19 test and 99.94 train accuracy. …”
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  8. 8

    Development of a modified adaptive protection scheme using machine learning technique for fault classification in renewable energy penetrated transmission line by Olufemi, Osaji Emmanuel

    Published 2020
    “…The Random Tree standalone ML-AP relay model presented the best performing models from the ML-APS relay model with the best average performance for the correctly classified fault types of 97.61 % at 5 % significance level above other ML algorithms. …”
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  9. 9

    Predicting customer churn in telecommunication service provider industry using Random Forest / Wan Muhammad Naqib Zafran Wan Roslan by Wan Roslan, Wan Muhammad Naqib Zafran

    Published 2023
    “…Through many experimentation, it was found that a model with 20 trees, a maximum depth of 5, and a maximum of 8 features yielded the highest accuracy at 79%, with an area under the curve of 0.79 for the Receiver Operating Characteristics. …”
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  10. 10

    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Dheyab, Saad Ahmed, Mohammed Abdulameer, Shaymaa, Mostafa, Salama A

    Published 2022
    “…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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  11. 11

    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Dheyab, Saad Ahmed, Mohammed Abdulameer, Shaymaa, Mostafa, Salama A.

    Published 2022
    “…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40…”
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  12. 12

    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Ahmed Dheyab, Saad, Mohammed Abdulameer, Shaymaa, Mostafa, Salama A

    Published 2022
    “…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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  13. 13

    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Dheyab, Saad Ahmed, Mohammed Abdulameer, Shaymaa, Mostafa, Salama

    Published 2022
    “…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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  14. 14

    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Dheyab, Saad Ahmed, Mohammed Abdulameer, Shaymaa, Mostafa, Salama

    Published 2023
    “…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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  15. 15

    Application of machine learning algorithms to predict removal efficiency in treating produced water via gas hydrate-based desalination by Nallakukkala, Sirisha, Tackie-Otoo, Bennet Nii, Aliyu, Ruwaida, Lal, Bhajan, Nallakukkala, Jagadish Ram Deepak, Devi, Gayathri

    Published 2025
    “…In this context. ML algorithms provide powerful data driven means to model complex relationship within experimental datasets to improve process optimisation This study systematically evaluated several supervised ML models, including Random Forest (RF) Support Vector Machines (SVM), Ridge Regression, Lasso Regression, Decision Tree, Extra Tree Regression, Gradient Boost, and XGBoost, to predict removal efficiency in GHBD system. …”
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  16. 16

    Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers by Al-Gubri, Qusay, Mohd Ariff, Mohd Aifaa

    Published 2017
    “…This paper proposed a novel algorithm based on advanced feature selection technique for decision tree (DT) classifier to assess the dynamic security in power system. …”
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  17. 17

    Object-based imagery analysis for automatic urban tree species detection using high resolution satellite image by Shojanoori, Razieh

    Published 2016
    “…In this research, most of satisfactory results achieved from the generic model and proves it can be easily performed to different WorldView-2 images from different areas and provided the high accuracy through algorithms for tree species detection namely, Mesua Ferrea, Samanea Saman, and Casuarina Sumatrana without using any training data. …”
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  18. 18

    Algorithm comparison for data mining classification: assessing bank customer credit scoring default risk by Elaf Adel Abbas, Nisreen Abbas Hussein

    Published 2024
    “…The models’ Accuracy, precision, recall, receiver operating characteristic (ROC) curve, and precision-recall curve were evaluated. …”
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    A Comparative Analysis of Peak Load Shaving Strategies for Isolated Microgrid Using Actual Data by Rana, M.M., Rahman, A., Uddin, M., Sarkar, M.R., Shezan, S.A., Ishraque, M.F., Rafin, S.M.S.H., Atef, M.

    Published 2022
    “…The model consists of four major components such as, PV, BESS, variable load, and gas turbine generator (GTG) dispatch models for the proposed algorithm, where the BESS and PV models are not applicable for the capacity addition technique. …”
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