Search alternatives:
model optimization » codon optimization (Expand Search), global optimization (Expand Search), wolf optimization (Expand Search)
based optimization » whale optimization (Expand Search), bayesian optimization (Expand Search)
marker model » markov model (Expand Search), order model (Expand Search), cancer model (Expand Search)
binary mask » binary image (Expand Search)
mask based » task based (Expand Search), risk based (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), wolf optimization (Expand Search)
based optimization » whale optimization (Expand Search), bayesian optimization (Expand Search)
marker model » markov model (Expand Search), order model (Expand Search), cancer model (Expand Search)
binary mask » binary image (Expand Search)
mask based » task based (Expand Search), risk based (Expand Search)
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A* Path-Finding Algorithm to Determine Cell Connections
Published 2025“…To address this, the research integrates a modified A* pathfinding algorithm with a U-Net convolutional neural network, a custom statistical binary classification method, and a personalized Min-Max connectivity threshold to automate the detection of astrocyte connectivity.…”
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Flowchart scheme of the ML-based model.
Published 2024“…<b>I)</b> Testing data consisting of 20% of the entire dataset. <b>J)</b> Optimization of hyperparameter tuning. <b>K)</b> Algorithm selection from all models. …”
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Steps in the extraction of 14 coordinates from the CT slices for the curved MPR.
Published 2025“…Protruding paths are then eliminated using graph-based optimization algorithms, as demonstrated in f). …”
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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.…”