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learning optimization » learning motivation (Expand Search), lead optimization (Expand Search)
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data path » data data (Expand Search), data part (Expand Search)
learning optimization » learning motivation (Expand Search), lead optimization (Expand Search)
path optimization » swarm optimization (Expand Search), whale optimization (Expand Search), based optimization (Expand Search)
new learning » e learning (Expand Search), deep learning (Expand Search), meta learning (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
data path » data data (Expand Search), data part (Expand Search)
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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. …”
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Secure MANET routing with blockchain-enhanced latent encoder coupled GANs and BEPO optimization
Published 2025“…The performance of the proposed LEGAN-BEPO-BCMANET technique attains 29.786%, 19.25%, 22.93%, 27.21%, 31.02%, 26.91%, and 25.61% greater throughput, compared to existing methods like Blockchain-based BATMAN protocol utilizing MANET with an ensemble algorithm (BATMAN-MANET), Block chain-based trusted distributed routing scheme with optimized dropout ensemble extreme learning neural network in MANET (DEELNN-MANET), A secured trusted routing utilizing structure of a new directed acyclic graph-blockchain in MANET internet of things environment (DAG-MANET), An Optimized Link State Routing Protocol with Blockchain Framework for Efficient Video-Packet Transmission and Security over MANET (OLSRP-MANET), Auto-metric Graph Neural Network based Blockchain Technology for Protected Dynamic Optimum Routing in MANET (AGNN-MANET) and Data security-based routing in MANETs under key management process (DSR-MANET) respectively.…”
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PathOlOgics_RBCs Python Scripts.zip
Published 2023“…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …”
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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“…</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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Analysis and design of algorithms for the manufacturing process of integrated circuits
Published 2023“…Additionally, the results obtained from our new ILP model indicate that our genetic algorithm results are very close to the optimal values.…”
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An intelligent decision-making system for embryo transfer in reproductive technology: a machine learning-based approach
Published 2025“…The aim of this study is to build Machine learning (ML) decision-support models to predict the optimal range of embryo numbers to transfer, using data from infertile couples identified through literature reviews. …”
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Data_Sheet_1_The impact of family urban integration on migrant worker mental health in China.docx
Published 2024“…Utilizing a machine learning clustering algorithm, the research endeavors to assess the level of urban integration experienced by migrant workers and their respective families. …”
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Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield...
Published 2022“…Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. …”