Search alternatives:
based optimization » whale optimization (Expand Search)
when optimization » whale optimization (Expand Search), other optimization (Expand Search), wolf optimization (Expand Search)
binary dataset » final dataset (Expand Search), binary data (Expand Search), ovary dataset (Expand Search)
dataset when » dataset n (Expand Search), dataset over (Expand Search)
binary risk » primary risk (Expand Search), dietary risk (Expand Search)
based optimization » whale optimization (Expand Search)
when optimization » whale optimization (Expand Search), other optimization (Expand Search), wolf optimization (Expand Search)
binary dataset » final dataset (Expand Search), binary data (Expand Search), ovary dataset (Expand Search)
dataset when » dataset n (Expand Search), dataset over (Expand Search)
binary risk » primary risk (Expand Search), dietary risk (Expand Search)
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Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm
Published 2025“…In this work, we propose a novel framework that integrates </p><p dir="ltr">Convolutional Neural Networks (CNNs) for image classification and a binary Grey Wolf Optimization (GWO) </p><p dir="ltr">algorithm for feature selection. …”
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ROC curve for binary classification.
Published 2024“…<div><p>This study aims to develop an optimally performing convolutional neural network to classify Alzheimer’s disease into mild cognitive impairment, normal controls, or Alzheimer’s disease classes using a magnetic resonance imaging dataset. …”
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Confusion matrix for binary classification.
Published 2024“…<div><p>This study aims to develop an optimally performing convolutional neural network to classify Alzheimer’s disease into mild cognitive impairment, normal controls, or Alzheimer’s disease classes using a magnetic resonance imaging dataset. …”
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Hyperparameters of the LSTM Model.
Published 2025“…This effectively balances exploration and exploitation, and addresses the early convergence problem of the original algorithms. To choose the most crucial characteristics of the dataset, the feature selection method employs the binary format of AD-PSO-Guided WOA. …”
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Prediction results of individual models.
Published 2025“…This effectively balances exploration and exploitation, and addresses the early convergence problem of the original algorithms. To choose the most crucial characteristics of the dataset, the feature selection method employs the binary format of AD-PSO-Guided WOA. …”