Search alternatives:
estimation algorithm » optimization algorithms (Expand Search), maximization algorithm (Expand Search), detection algorithm (Expand Search)
robust estimation » pose estimation (Expand Search), risk estimation (Expand Search)
wolf optimization » whale optimization (Expand Search), swarm optimization (Expand Search), _ optimization (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
a robust » _ robust (Expand Search)
estimation algorithm » optimization algorithms (Expand Search), maximization algorithm (Expand Search), detection algorithm (Expand Search)
robust estimation » pose estimation (Expand Search), risk estimation (Expand Search)
wolf optimization » whale optimization (Expand Search), swarm optimization (Expand Search), _ optimization (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
a robust » _ robust (Expand Search)
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Individual Transition Label Noise Logistic Regression in Binary Classification for Incorrectly Labeled Data
Published 2021“…<p>We consider a binary classification problem in the case where some observations in the training data are incorrectly labeled. …”
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Determination of the Solute Content and Volumetric Properties of Binary Ionic Liquid Mixtures Using a Global Regularity of Molar Volume Expansion
Published 2021“…For instance, the water content, which is of great significance in IL studies, can easily be estimated using the proposed algorithm. By doing so, an overall AARD of 3.47% was obtained for the estimation of the water content of 68 binary systems. …”
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The flowchart of the proposed algorithm.
Published 2024“…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …”
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Summary of literature review.
Published 2024“…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …”
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Topic description.
Published 2024“…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …”
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Notations along with their descriptions.
Published 2024“…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …”
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Detail of the topics extracted from DUC2002.
Published 2024“…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …”
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QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm
Published 2020“…The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. …”
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Table 1_Provider fidelity in tuberculosis screening practices among adolescents and adults living with HIV in public health facilities in Tanzania: a cross-sectional evaluation.doc...
Published 2025“…Modified Poisson regression with robust variance was used to estimate prevalence ratios (PRs) to determine factors associated with two binary outcomes: (1) consistent TB screening over 12-month period, and (2) correct utilization of the screening tool. …”
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Table 2_Provider fidelity in tuberculosis screening practices among adolescents and adults living with HIV in public health facilities in Tanzania: a cross-sectional evaluation.doc...
Published 2025“…Modified Poisson regression with robust variance was used to estimate prevalence ratios (PRs) to determine factors associated with two binary outcomes: (1) consistent TB screening over 12-month period, and (2) correct utilization of the screening tool. …”
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Table_1_An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.pdf
Published 2024“…Next, a hybrid feature extraction approach is presented leveraging transfer learning from selected deep neural network models, InceptionV3 and DenseNet201, to extract comprehensive feature sets. To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. …”
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Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data
Published 2021“…Specifically, the proposed method obtains the optimal DTR via integrating estimations of decision rules at multiple stages into a single multicategory classification algorithm without imposing additional constraints, which is also more computationally efficient and robust. …”
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An AI-based Ecosystem for Real-time Gravitational Wave Analyses
Published 2024“…These searches include A-frame, a low-latency machine learning pipeline for compact binary sources of gravitational waves, DeepClean, a deep learning-based denoising scheme for astrophysical gravitational waves, GWAK, a semi-supervised AI strategy to identify unmodeled gravitational wave transients, and AMPLFI, a pipeline for deep learning-based parameter estimation, leading to latency improvements for multi-messenger targets. …”