Search alternatives:
learning optimization » learning motivation (Expand Search), lead optimization (Expand Search)
based optimization » whale optimization (Expand Search)
also learning » a learning (Expand Search), l2 learning (Expand Search), shallow learning (Expand Search)
binary some » binary isomers (Expand Search)
some based » home based (Expand Search), score based (Expand Search), slope based (Expand Search)
learning optimization » learning motivation (Expand Search), lead optimization (Expand Search)
based optimization » whale optimization (Expand Search)
also learning » a learning (Expand Search), l2 learning (Expand Search), shallow learning (Expand Search)
binary some » binary isomers (Expand Search)
some based » home based (Expand Search), score based (Expand Search), slope 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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Results of machine learning algorithm.
Published 2024“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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ROC comparison of machine learning algorithm.
Published 2024“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm
Published 2025“…Our results show that deep learning and optimization </p><p dir="ltr">methods, such as the binary GWO algorithm, can be successfully applied to melanoma diagnosis. …”
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Algorithm for generating hyperparameter.
Published 2024“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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