Search alternatives:
model optimization » codon optimization (Expand Search), global optimization (Expand Search), wolf optimization (Expand Search)
based optimization » whale optimization (Expand Search)
binary phase » binary image (Expand Search), final phase (Expand Search)
phase model » base model (Expand Search)
binary task » binary mask (Expand Search)
task based » risk based (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), wolf optimization (Expand Search)
based optimization » whale optimization (Expand Search)
binary phase » binary image (Expand Search), final phase (Expand Search)
phase model » base model (Expand Search)
binary task » binary mask (Expand Search)
task based » risk based (Expand Search)
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MSE for ILSTM algorithm in binary classification.
Published 2023“…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”
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Proposed Algorithm.
Published 2025“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”
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Comparisons between ADAM and NADAM optimizers.
Published 2025“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”
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The Pseudo-Code of the IRBMO Algorithm.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
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Statistical summary of all models.
Published 2025“…<div><p>Modern sustainable farming demands precise water management techniques, particularly for crops like potatoes that require high-quality irrigation to ensure optimal growth. This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”