Search alternatives:
streams optimization » stress optimization (Expand Search), surface optimization (Expand Search), step optimization (Expand Search)
dose optimization » based optimization (Expand Search), model optimization (Expand Search), wolf optimization (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
data dose » data due (Expand Search), data de (Expand Search)
streams optimization » stress optimization (Expand Search), surface optimization (Expand Search), step optimization (Expand Search)
dose optimization » based optimization (Expand Search), model optimization (Expand Search), wolf optimization (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
data dose » data due (Expand Search), data de (Expand Search)
-
1
Event-driven data flow processing.
Published 2025“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”
-
2
Confusion matrix.
Published 2025“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”
-
3
Parameter settings.
Published 2025“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”
-
4
Dynamic resource allocation process.
Published 2025“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”
-
5
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.…”