Search alternatives:
codon optimization » wolf optimization (Expand Search)
model optimization » global optimization (Expand Search), based optimization (Expand Search), wolf optimization (Expand Search)
binary sample » final sample (Expand Search), binary people (Expand Search), intra sample (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
sample model » simple model (Expand Search), sample level (Expand Search)
based codon » based color (Expand Search), based cohort (Expand Search), based action (Expand Search)
codon optimization » wolf optimization (Expand Search)
model optimization » global optimization (Expand Search), based optimization (Expand Search), wolf optimization (Expand Search)
binary sample » final sample (Expand Search), binary people (Expand Search), intra sample (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
sample model » simple model (Expand Search), sample level (Expand Search)
based codon » based color (Expand Search), based cohort (Expand Search), based action (Expand Search)
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The list of parameters of the modified data set for machine learning (<i>n</i> = 162).
Published 2025Subjects: -
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Summary of existing CNN models.
Published 2024“…The model further showed superior results on binary classification compared with existing methods. …”
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Thesis-RAMIS-Figs_Slides
Published 2024“…<br><br>Finally, although the developed concepts, ideas and algorithms have been developed for inverse problems in geostatistics, the results are applicable to a wide range of disciplines where similar sampling problems need to be faced, included but not limited to design of communication networks, optimal integration and communication of swarms of robots and drones, remote sensing.…”
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Testing results for classifying AD, MCI and NC.
Published 2024“…The model further showed superior results on binary classification compared with existing methods. …”
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MCLP_quantum_annealer_V0.5
Published 2025“…<p dir="ltr">Geospatial optimization problems are fundamental research issues in geographic information science modeling, characterized by high dimensionality, dynamics, and discreteness. …”
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Processed dataset to train and test the WGAN-GP_IMOA_DA_Ensemble model
Published 2025“…This framework integrates a novel biologically inspired optimization algorithm, the Indian Millipede Optimization Algorithm (IMOA), for effective feature selection. …”
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Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP
Published 2022“…In this paper, a non-invasive and low-cost method of fatigue driving state identification based on genetic algorithm optimization of generalized regression neural network model is proposed. …”
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Psoas muscle CT radiomics-based machine learning models to predict response to infliximab in patients with Crohn’s disease
Published 2025“…<i>Z</i> score standardization and independent sample <i>t</i> test were applied to identify optimal predictive features, which were then utilized in seven ML algorithms for training and validation. …”
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Supplementary Material 8
Published 2025“…In AMR studies, datasets often contain more susceptible isolates than resistant ones, leading to biased model performance. SMOTE overcomes this issue by generating synthetic samples of the minority class (resistant isolates) through interpolation rather than simple duplication, thereby improving model generalization.…”
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DataSheet_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx
Published 2024“…Classification of genotypes was carried out using the K-nearest neighbor algorithm (KNN) and partial least squares (PLS) models. …”
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Table_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx
Published 2024“…Classification of genotypes was carried out using the K-nearest neighbor algorithm (KNN) and partial least squares (PLS) models. …”
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Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat.
Published 2025“…Optimization with GridSearchCV corroborated this stagnation, identifying a simple linear model (C=0.05, gamma='scale') as the optimal configuration, indicating that the additional complexity of nonlinear kernels did not confer predictive gains. …”
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DataSheet_1_Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.docx
Published 2024“…Logistic regression emerged as the optimal machine learning algorithm for both DLR models. …”