Search alternatives:
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
code optimization » codon optimization (Expand Search), dose optimization (Expand Search), cost optimization (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
data code » data model (Expand Search), data came (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
a model » _ model (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
code optimization » codon optimization (Expand Search), dose optimization (Expand Search), cost optimization (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
data code » data model (Expand Search), data came (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
a model » _ model (Expand Search)
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101
SHAP bar plot.
Published 2025“…</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
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102
Sample screening flowchart.
Published 2025“…</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
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103
Descriptive statistics for variables.
Published 2025“…</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
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104
SHAP summary plot.
Published 2025“…</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
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105
Display of the web prediction interface.
Published 2025“…</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
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106
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107
Wilcoxon test results for feature selection.
Published 2025“…<div><p>Modern sustainable farming demands precise water management techniques, particularly for crops like potatoes that require high-quality irrigation to ensure optimal growth. This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”
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108
Feature selection metrics and their definitions.
Published 2025“…<div><p>Modern sustainable farming demands precise water management techniques, particularly for crops like potatoes that require high-quality irrigation to ensure optimal growth. This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”
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109
Feature selection results.
Published 2025“…<div><p>Modern sustainable farming demands precise water management techniques, particularly for crops like potatoes that require high-quality irrigation to ensure optimal growth. This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”
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110
ANOVA test for feature selection.
Published 2025“…<div><p>Modern sustainable farming demands precise water management techniques, particularly for crops like potatoes that require high-quality irrigation to ensure optimal growth. This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”
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111
Predicting Thermal Decomposition Temperature of Binary Imidazolium Ionic Liquid Mixtures from Molecular Structures
Published 2021“…This study is devoted to develop a quantitative structure–property relationship model for predicting the <i>T</i><sub>d</sub>,<sub>5%onset</sub> of binary imidazolium IL mixtures. …”
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112
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113
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114
Solubility Prediction of Different Forms of Pharmaceuticals in Single and Mixed Solvents Using Symmetric Electrolyte Nonrandom Two-Liquid Segment Activity Coefficient Model
Published 2019“…A particle swarm optimization algorithm is incorporated to preregress conceptual segment parameters of solutes. …”
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115
Summary of existing CNN models.
Published 2024“…The model was trained and evaluated using a 10-fold cross-validation sampling approach with a learning rate of 0.001 and 200 training epochs at each instance. …”
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116
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117
DataSheet_1_Raman Spectroscopic Differentiation of Streptococcus pneumoniae From Other Streptococci Using Laboratory Strains and Clinical Isolates.pdf
Published 2022“…Improvement of the classification rate is expected with optimized model parameters and algorithms as well as with a larger spectral data base for training.…”
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118
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119
MCLP_quantum_annealer_V0.5
Published 2025“…<p dir="ltr">Geospatial optimization problems are fundamental research issues in geographic information science modeling, characterized by high dimensionality, dynamics, and discreteness. …”
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120
Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment
Published 2019“…The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. …”