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model optimization » codon optimization (Expand Search), global optimization (Expand Search), wolf optimization (Expand Search)
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model optimization » codon optimization (Expand Search), global optimization (Expand Search), wolf optimization (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search)
binary mask » binary image (Expand Search)
mask model » mask models (Expand Search), risk model (Expand Search), base model (Expand Search)
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81
Data Sheet 1_Detection of litchi fruit maturity states based on unmanned aerial vehicle remote sensing and improved YOLOv8 model.docx
Published 2025“…In addition, YOLOv8-FPDW was more competitive than mainstream object detection algorithms. The study predicted the optimal harvest period for litchis, providing scientific support for orchard batch harvesting and fine management.…”
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82
Models and Dataset
Published 2025“…</p><p dir="ltr"><br></p><p dir="ltr"><b>RAO (Rao Optimization Algorithm):</b><br>RAO is a parameter-less optimization algorithm that updates solutions based on simple arithmetic operations involving the best and worst individuals in the population. …”
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83
Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat.
Published 2025“…Model evaluation was based on accuracy metrics and qualitative analysis of the confusion matrix.. …”
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84
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85
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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86
Supplementary Material 8
Published 2025“…</p><p dir="ltr">When applied to AMR prediction, SMOTE enhances the ability of classification models to accurately identify resistant <i>Escherichia coli</i> strains by balancing the dataset, ensuring that machine learning algorithms do not overlook rare resistance patterns. …”
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87
Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles
Published 2025“…</p><p dir="ltr">Encoding: Categorical variables such as surface coating and cell type were grouped into logical classes and label-encoded to enable model compatibility.</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”