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experiments pso » experiments also (Expand Search), experiments p (Expand Search), experiments ns (Expand Search)
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PSO and GA hyperparameters.
Published 2024“…These subsets gave us low values of Sammon error at more than 70% at class and method levels on a validation dataset.</p></div>…”
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Results of the PSNR measure for all algorithms.
Published 2024“…According to the segmentation experimental results, thresholds optimized by CIWP-PSO could achieve higher Kapur entropy, and the multi-level thresholding segmentation algorithm based on CIWP-PSO outperforms the similar algorithms in high-bit depth image segmentation. …”
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Results of the FSIM measure for all algorithms.
Published 2024“…According to the segmentation experimental results, thresholds optimized by CIWP-PSO could achieve higher Kapur entropy, and the multi-level thresholding segmentation algorithm based on CIWP-PSO outperforms the similar algorithms in high-bit depth image segmentation. …”
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Results of the SSIM measure for all algorithms.
Published 2024“…According to the segmentation experimental results, thresholds optimized by CIWP-PSO could achieve higher Kapur entropy, and the multi-level thresholding segmentation algorithm based on CIWP-PSO outperforms the similar algorithms in high-bit depth image segmentation. …”
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Sanitized databases using MLHProtector algorithm.
Published 2025“…To address this issue, this work suggests two PPUM algorithms, namely <b>MLHProtector</b> and <b>FMLHProtector</b>, to operate at all abstraction levels in a transaction database to protect them from data mining algorithms. …”
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The GA iteration result.
Published 2025“…Results show that GA-FNN achieves an average classification accuracy of approximately 90% across high, medium, and low risk levels, outperforming traditional methods such as logistic regression, SVM (Support Vector Machine), and other metaheuristics like PSO (Particle Swarm Optimization) and SA (Simulated Algorithm). …”
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GA crossover and mutation process.
Published 2025“…Results show that GA-FNN achieves an average classification accuracy of approximately 90% across high, medium, and low risk levels, outperforming traditional methods such as logistic regression, SVM (Support Vector Machine), and other metaheuristics like PSO (Particle Swarm Optimization) and SA (Simulated Algorithm). …”
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The framework for the proposed model and GA-FNN.
Published 2025“…Results show that GA-FNN achieves an average classification accuracy of approximately 90% across high, medium, and low risk levels, outperforming traditional methods such as logistic regression, SVM (Support Vector Machine), and other metaheuristics like PSO (Particle Swarm Optimization) and SA (Simulated Algorithm). …”
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The structure for the FNN.
Published 2025“…Results show that GA-FNN achieves an average classification accuracy of approximately 90% across high, medium, and low risk levels, outperforming traditional methods such as logistic regression, SVM (Support Vector Machine), and other metaheuristics like PSO (Particle Swarm Optimization) and SA (Simulated Algorithm). …”
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