Hybrid Deep Transfer Learning for Enhanced Brain Tumor Detection through the Integration of MobileNetV2 and InceptionV3

Procedia Computer Science, Год журнала: 2025, Номер 258, С. 2968 - 2977

Опубликована: Янв. 1, 2025

Язык: Английский

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ESARSA-MRFO-FS: Optimizing Manta-ray Foraging Optimizer using Expected-SARSA reinforcement learning for features selection

Knowledge-Based Systems, Год журнала: 2025, Номер unknown, С. 113695 - 113695

Опубликована: Май 1, 2025

Язык: Английский

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H2LFR: a hybrid two-layered feature ranking approach for enhanced data analysis

Knowledge and Information Systems, Год журнала: 2025, Номер unknown

Опубликована: Май 28, 2025

Язык: Английский

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Advancing feature ranking with hybrid feature ranking weighted majority model: a weighted majority voting strategy enhanced by the Harris hawks optimizer

Journal of Computational Design and Engineering, Год журнала: 2024, Номер 11(3), С. 308 - 325

Опубликована: Май 1, 2024

Abstract Feature selection (FS) is vital in improving the performance of machine learning (ML) algorithms. Despite its importance, identifying most important features remains challenging, highlighting need for advanced optimization techniques. In this study, we propose a novel hybrid feature ranking technique called Hybrid Ranking Weighted Majority Model (HFRWM2). HFRWM2 combines ML models with Harris Hawks Optimizer (HHO) metaheuristic. HHO known versatility addressing various challenges, thanks to ability handle continuous, discrete, and combinatorial problems. It achieves balance between exploration exploitation by mimicking cooperative hunting behavior Harris’s hawks, thus thoroughly exploring search space converging toward optimal solutions. Our approach operates two phases. First, an odd number models, conjunction HHO, generate encodings along metrics. These are then weighted based on their metrics vertically aggregated. This process produces rankings, facilitating extraction top-K features. The motivation behind our research 2-fold: enhance precision algorithms through optimized FS improve overall efficiency predictive models. To evaluate effectiveness HFRWM2, conducted rigorous tests datasets: “Australian” “Fertility.” findings demonstrate navigating We compared 12 other techniques found it outperform them. superiority was particularly evident graphical comparison dataset, where showed significant advancements ranking.

Язык: Английский

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Unleashing the power of Manta Rays Foraging Optimizer: A novel approach for hyper-parameter optimization in skin cancer classification

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 99, С. 106855 - 106855

Опубликована: Сен. 12, 2024

Язык: Английский

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