Search Results - (( 3d visualization swarm algorithm ) OR ( whale optimization svm algorithm ))*

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    Improved whale optimization algorithm for feature selection in Arabic sentiment analysis by Tubishat, Mohammad, Abushariah, Mohammad A.M., Idris, Norisma, Aljarah, Ibrahim

    Published 2019
    “…In SA, feature selection phase is an important phase for machine learning classifiers specifically when the datasets used in training is huge. Whale Optimization Algorithm (WOA) is one of the recent metaheuristic optimization algorithm that mimics the whale hunting mechanism. …”
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  3. 3

    Hybrid feature selection of microarray prostate cancer diagnostic system by Mohd Ali, Nursabillilah, Hanafi, Ainain Nur, Karis, Mohd Safirin, Shamsudin, Nur Hazahsha, Shair, Ezreen Farina, Abdul Aziz, Nor Hidayati

    Published 2024
    “…The performance of GA, particle swarm optimization (PSO), and whale optimization algorithm (WOA) is compared in terms of accuracy, computation time, and the number of selected features. …”
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    The Development Of A Robust Algorithm For Uav Path Planning In 3d Environment by Kok, Kai Yit

    Published 2016
    “…Significant research has been conducted on Unmanned Aerial Vehicle (UAV) path planning using evolutionary algorithms, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), and Biogeographic-Based Optimization (BBO). …”
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    Information fusion and data augmentation with deep features for a deep learning-based baby cry recognition / Zhang Ke by Zhang , Ke

    Published 2024
    “…To validate the effectiveness of the proposed method, examples are analyzed, and applied in baby cry recognition. The Whale optimization algorithm-Variational mode decomposition is used to optimally decompose the baby cry signals. …”
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    Knee-based energy-saving measures selection using MOPSO and NSGA-II by Md Ramli, Siti Solehah, Ibrahim, Mohammad Nizam, Mohamad @ Ahmad, Anuar, Daud, Kamarulazhar, Saidina Omar, Abdul Malek, Ibrahim, Nor Shuhada

    Published 2026
    “…For each algorithm, 30 seeds generate Pareto fronts in the cost–energy saving plane, shown in 2D for clear comparison, and the resulting carbon metric is visualized as a 3D projection. …”
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