Search alternatives:
process optimization » model optimization (Expand Search)
driven optimization » design optimization (Expand Search), guided optimization (Expand Search), dose optimization (Expand Search)
cell process » cell processes (Expand Search), cycle process (Expand Search), creep process (Expand Search)
binary cell » primary cell (Expand Search)
binary mapk » binary mask (Expand Search), binary image (Expand Search)
process optimization » model optimization (Expand Search)
driven optimization » design optimization (Expand Search), guided optimization (Expand Search), dose optimization (Expand Search)
cell process » cell processes (Expand Search), cycle process (Expand Search), creep process (Expand Search)
binary cell » primary cell (Expand Search)
binary mapk » binary mask (Expand Search), binary image (Expand Search)
-
1
A* Path-Finding Algorithm to Determine Cell Connections
Published 2025“…Pixel paths were classified using a z-score brightness threshold of 1.21, optimized for noise reduction and accuracy. The A* algorithm then evaluated connectivity by minimizing Euclidean distance and heuristic cost between cells. …”
-
2
-
3
Image processing workflow.
Published 2020“…<p>Raw fluorescent microscope images (a) were processed with a binary segmentation algorithm, and clusters of bacterial cells were manually annotated. …”
-
4
-
5
-
6
-
7
-
8
-
9
-
10
-
11
-
12
-
13
-
14
-
15
PathOlOgics_RBCs Python Scripts.zip
Published 2023“…This process generated a ground-truth binary semantic segmentation mask and determined the bounding box coordinates (XYWH) for each cell. …”
-
16
-
17
-
18
-
19
Seed mix selection model
Published 2022“…Classic genetic algorithms consider a population of chromosomes and apply principles of natural selection (selection, mutation, and crossover processes) to generate optimal solutions. …”
-
20
Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles
Published 2025“…</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.…”