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models optimization » model optimization (Expand Search), process optimization (Expand Search), wolf optimization (Expand Search)
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models optimization » model optimization (Expand Search), process optimization (Expand Search), wolf optimization (Expand Search)
while optimization » whale optimization (Expand Search), wolf optimization (Expand Search), phase optimization (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
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A* Path-Finding Algorithm to Determine Cell Connections
Published 2025“…</p><p dir="ltr">Astrocytes were dissociated from E18 mouse cortical tissue, and image data were processed using a Cellpose 2.0 model to mask nuclei. …”
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DataSheet_1_Raman Spectroscopic Differentiation of Streptococcus pneumoniae From Other Streptococci Using Laboratory Strains and Clinical Isolates.pdf
Published 2022“…Improvement of the classification rate is expected with optimized model parameters and algorithms as well as with a larger spectral data base for training.…”
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Predicting Thermal Decomposition Temperature of Binary Imidazolium Ionic Liquid Mixtures from Molecular Structures
Published 2021“…The subset of optimal descriptors was screened by combining the genetic algorithm with the multiple linear regression method. …”
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Data_Sheet_1_Posiform planting: generating QUBO instances for benchmarking.pdf
Published 2023“…<p>We are interested in benchmarking both quantum annealing and classical algorithms for minimizing quadratic unconstrained binary optimization (QUBO) problems. …”
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Table_1_An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.pdf
Published 2024“…To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. …”
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Datasets and their properties.
Published 2023“…In addition, we designed nested transfer (NT) functions and investigated the influence of the function on the level-1 optimizer. The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. …”
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Parameter settings.
Published 2023“…In addition, we designed nested transfer (NT) functions and investigated the influence of the function on the level-1 optimizer. The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. …”
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IRBMO vs. variant comparison adaptation data.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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Pseudo Code of RBMO.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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P-value on CEC-2017(Dim = 30).
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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Memory storage behavior.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”