Search alternatives:
model optimization » global optimization (Expand Search), based optimization (Expand Search), wolf optimization (Expand Search)
codon optimization » wolf optimization (Expand Search)
binary base » binary mask (Expand Search), ciliary base (Expand Search), binary image (Expand Search)
our model » four models (Expand Search), sir model (Expand Search)
model optimization » global optimization (Expand Search), based optimization (Expand Search), wolf optimization (Expand Search)
codon optimization » wolf optimization (Expand Search)
binary base » binary mask (Expand Search), ciliary base (Expand Search), binary image (Expand Search)
our model » four models (Expand Search), sir model (Expand Search)
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Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm
Published 2025“…Improvements in sensitivity, specificity, and overall classification accuracy highlight the effectiveness of </p><p dir="ltr">combining deep learning with optimization techniques. 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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Data_Sheet_1_Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM).pdf
Published 2024“…A support vector machine (SVM) is attractive because binary classification can be represented as an optimization problem, in particular as a Quadratic Unconstrained Binary Optimization (QUBO) model, which, in turn, maps naturally to an Ising model, thereby making annealing—classical, quantum, and hybrid—an attractive approach to explore. …”
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Analysis and design of algorithms for the manufacturing process of integrated circuits
Published 2023“…Additionally, the results obtained from our new ILP model indicate that our genetic algorithm results are very close to the optimal values.…”
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Unraveling Adsorbate-Induced Structural Evolution of Iron Carbide Nanoparticles
Published 2025“…For this purpose, we have developed a general procedure that we use to model an experimentally relevant 270-atom Fe<sub>182</sub>C<sub>88</sub> NP using the neural network-assisted stochastic surface walk global optimization algorithm (SSW-NN). …”
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Generalized Tensor Decomposition With Features on Multiple Modes
Published 2021“…Our proposal handles a broad range of data types, including continuous, count, and binary observations. …”
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Presentation_1_Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy.PPTX
Published 2021“…Currently, there are no optimized deep-learning algorithms for the automated classification of scarred vs. normal myocardium. …”
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Confusion matrix.
Published 2025“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”
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Parameter settings.
Published 2025“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”
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Event-driven data flow processing.
Published 2025“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”
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Dynamic resource allocation process.
Published 2025“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”
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Seed mix selection model
Published 2022“…</p> <p> </p> <p>We applied the seed mix selection model using a binary genetic algorithm to select seed mixes (R package ‘GA’; Scrucca 2013; Scrucca 2017). …”
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Flowchart of the entire pipeline.
Published 2024“…Then, the protein feature generation algorithms described in our previous study [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0315330#pone.0315330.ref022" target="_blank">22</a>] are applied to the data, and pairwise ML models are trained and evaluated (see Section Evaluation of pairwise machine learning models). …”
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Table_1_iRNA5hmC: The First Predictor to Identify RNA 5-Hydroxymethylcytosine Modifications Using Machine Learning.docx
Published 2020“…Our feature analysis results showed that feature optimization can help to capture the most discriminative features. …”
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Data_Sheet_1_A Data-Driven Framework for Identifying Intensive Care Unit Admissions Colonized With Multidrug-Resistant Organisms.docx
Published 2022“…We performed threshold optimization for converting predicted probabilities into binary predictions and identified the cut-off maximizing the sum of sensitivity and specificity.…”