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bayesian optimization » based optimization (Expand Search)
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data process » data processing (Expand Search), damage process (Expand Search), data access (Expand Search)
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binary 95 » binary _ (Expand Search), binary b (Expand Search)
bayesian optimization » based optimization (Expand Search)
process optimization » model optimization (Expand Search)
primary data » primary care (Expand Search)
data process » data processing (Expand Search), damage process (Expand Search), data access (Expand Search)
95 bayesian » _ bayesian (Expand Search), a bayesian (Expand Search)
binary 95 » binary _ (Expand Search), binary b (Expand Search)
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101
Analysis PC1 AU-ROC curve.
Published 2024“…We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. The SPAM-XAI model reduces features, optimizes the model, and reduces time and space complexity, enhancing its robustness. …”
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102
Proposed method approach.
Published 2024“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
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103
LSTM model performance.
Published 2024“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
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104
Descriptive statistics.
Published 2024“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
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105
CNN-LSTM Model performance.
Published 2024“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
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106
MLP Model performance.
Published 2024“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
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107
RNN Model performance.
Published 2024“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
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108
CNN Model performance.
Published 2024“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
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109
Bi-directional LSTM Model performance.
Published 2024“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
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110
Early Parkinson’s disease identification via hybrid feature selection from multi-feature subsets and optimized CatBoost with SMOTE
Published 2025“…The proposed framework leverages a strong categorical boosting (CatBoost) algorithm optimized using Grid Search Optimization (GSO). …”
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111
Minimal Dateset.
Published 2025“…To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. …”
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112
Loss Function Comparison.
Published 2025“…To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. …”
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113
Comparative Results of Different Models.
Published 2025“…To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. …”
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114
Loss Function Comparison.
Published 2025“…To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. …”
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115
Overall Framework of the PSO-KM Model.
Published 2025“…To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. …”
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116
Overall Framework of the PSO-KM Model.
Published 2025“…To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. …”
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117
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118
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119
Supplementary file 1_Development of a venous thromboembolism risk prediction model for patients with primary membranous nephropathy based on machine learning.docx
Published 2025“…Objective<p>This study utilizes real-world data from primary membranous nephropathy (PMN) patients to preliminarily develop a venous thromboembolism (VTE) risk prediction model with machine learning. …”
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120