Search alternatives:
required optimization » guided optimization (Expand Search), resource optimization (Expand Search), feature optimization (Expand Search)
robust optimization » process optimization (Expand Search), robust estimation (Expand Search), joint optimization (Expand Search)
task required » task requiring (Expand Search), time required (Expand Search), also required (Expand Search)
primary data » primary care (Expand Search)
binary task » binary mask (Expand Search)
required optimization » guided optimization (Expand Search), resource optimization (Expand Search), feature optimization (Expand Search)
robust optimization » process optimization (Expand Search), robust estimation (Expand Search), joint optimization (Expand Search)
task required » task requiring (Expand Search), time required (Expand Search), also required (Expand Search)
primary data » primary care (Expand Search)
binary task » binary mask (Expand Search)
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41
Nonlinear fast convergence factor.
Published 2023“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”
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42
CEC2019 benchmark functions.
Published 2023“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”
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43
Results of Comprehensive weighting.
Published 2025“…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …”
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44
The prediction error of each model.
Published 2025“…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …”
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45
VIF analysis results for hazard-causing factors.
Published 2025“…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …”
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46
Benchmark function information.
Published 2025“…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …”
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47
Geographical distribution of the study area.
Published 2025“…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …”
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48
Results for model hyperparameter values.
Published 2025“…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …”
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49
Flow chart of this study.
Published 2025“…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …”
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50
Stability analysis of each model.
Published 2025“…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …”
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55
Data used to drive the Double Layer Carbon Model in the Qinling Mountains.
Published 2024“…It relies on comprehensive input data, including initial SOC stocks, climate data, and vegetation production to drive these simulations.…”
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56
Predicting the Shear Viscosity of Carbonated Aqueous Amine Solutions and Their Blends by Using an Artificial Neural Network Model
Published 2020“…A total of 1682 amine + CO<sub>2</sub> + water viscosity data sets for primary, secondary, and tertiary amines and 220 data points for further accuracy examinations were used. …”
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57
Inconsistency concept for a triad (2, 5, 3).
Published 2025“…The proposed regeneration method emulates three primary phases of a biological process: identifying the most damaged areas (by identifying inconsistencies in the pairwise comparison matrix), cell proliferation (filling in missing data), and stabilization (optimization of global consistency). …”
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58
Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics
Published 2019“…However, extraction of trace-abundance signals from complex data sets (<i>m</i>/<i>z</i> value, separation time, signal abundance) that result from ultrasensitive studies requires improved data processing algorithms. …”
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Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics
Published 2019“…However, extraction of trace-abundance signals from complex data sets (<i>m</i>/<i>z</i> value, separation time, signal abundance) that result from ultrasensitive studies requires improved data processing algorithms. …”
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Early Parkinson’s disease identification via hybrid feature selection from multi-feature subsets and optimized CatBoost with SMOTE
Published 2025“…<p>Achieving high accuracy, efficiency and robustness remains a primary challenge in Parkinson's disease (PD) detection, as existing methods often struggle with these aspects. …”