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implementing learning » implementing building (Expand Search)
learning algorithm » learning algorithms (Expand Search)
using algorithm » using algorithms (Expand Search), routing algorithm (Expand Search), fusion algorithm (Expand Search)
data algorithm » data algorithms (Expand Search), update algorithm (Expand Search), atlas algorithm (Expand Search)
element data » settlement data (Expand Search), relevant data (Expand Search), movement data (Expand Search)
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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“…This study focuses on developing an efficient classification framework for species-level tree mapping in the Hauz Khas Urban Forest, New Delhi, India, using EO-1 Hyperion hyperspectral imagery.</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). …”
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Sanitized databases using MLHProtector algorithm.
Published 2025“…<div><p>Privacy is as a critical issue in the age of data. Organizations and corporations who publicly share their data always have a major concern that their sensitive information may be leaked or extracted by rivals or attackers using data miners. …”
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Supporting data for "Interpreting complex ecological patterns and processes across differentscales using Artificial Intelligence"
Published 2025“…</p><p dir="ltr">Firstly, a Python package HSC3D, was developed to quantify habitat structural complexity (HSC) at the community level. Built with machine learning algorithms and novel, scale-invariant metrics, this package provides more precise, scale-invariant representations of HSC than traditional approaches, which can be applied to a variety of habitats in different ecosystems. …”
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Data Sheet 1_Machine learning-based prediction of clinical outcomes in cervical cancer using routine hematological indices: development and web implementation.docx
Published 2025“…A panel of hematological indices was evaluated, including inflammatory markers, coagulation parameters, and metabolic indicators. Machine learning (ML) algorithms innovatively integrated with traditional regression were employed for feature selection and model development. …”
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