Search alternatives:
driven optimization » design optimization (Expand Search), guided optimization (Expand Search), dose optimization (Expand Search)
based optimization » whale optimization (Expand Search)
binary mapk » binary mask (Expand Search), binary image (Expand Search)
tree » three (Expand Search), free (Expand Search)
driven optimization » design optimization (Expand Search), guided optimization (Expand Search), dose optimization (Expand Search)
based optimization » whale optimization (Expand Search)
binary mapk » binary mask (Expand Search), binary image (Expand Search)
tree » three (Expand Search), free (Expand Search)
-
1
-
2
-
3
-
4
-
5
-
6
-
7
-
8
-
9
-
10
-
11
Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things
Published 2025“…Hence, Binary Black Widow Optimization Algorithm (BBWOA) is proposed in this manuscript to improve the BRBPNN classifier that detects intrusion precisely. …”
-
12
Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data
Published 2022“…Therefore, the resampling algorithm employed should vary depending on the data distribution to achieve optimal classification performance. …”
-
13
Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx
Published 2025“…</p>Methods<p>Thirteen supervised classification algorithms were comparatively evaluated, encompassing traditional spectral/statistical classifiers—Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Parallelepiped, Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Binary Encoding—and machine learning algorithms including Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). …”
-
14
Supplementary Material 8
Published 2025“…</li><li><b>XGboost: </b>An optimized gradient boosting algorithm that efficiently handles large genomic datasets, commonly used for high-accuracy predictions in <i>E. coli</i> classification.…”