بدائل البحث:
processing optimization » process optimization (توسيع البحث), process optimisation (توسيع البحث), routing optimization (توسيع البحث)
wolf optimization » whale optimization (توسيع البحث), swarm optimization (توسيع البحث), _ optimization (توسيع البحث)
data processing » image processing (توسيع البحث)
primary data » primary care (توسيع البحث)
binary fast » binary mask (توسيع البحث)
fast wolf » fast self (توسيع البحث)
processing optimization » process optimization (توسيع البحث), process optimisation (توسيع البحث), routing optimization (توسيع البحث)
wolf optimization » whale optimization (توسيع البحث), swarm optimization (توسيع البحث), _ optimization (توسيع البحث)
data processing » image processing (توسيع البحث)
primary data » primary care (توسيع البحث)
binary fast » binary mask (توسيع البحث)
fast wolf » fast self (توسيع البحث)
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81
Supporting data for “The role of forest composition heterogeneity on temperate ecosystem carbon dynamic under climate change"
منشور في 2025"…The process includes (1) harmonizing Landsat 5, 7, 8, and Sentinel-2 data using the HLS algorithm, and (2) filling temporal gaps with an optimized object-based STARFM fusion algorithm. …"
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82
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83
Data used to drive the Double Layer Carbon Model in the Qinling Mountains.
منشور في 2024"…It also incorporates climate change responses, adjust decomposition rates based on climate and environmental changes, and lead to robust estimates under different climatic scenarios. The simulation process of the DLCM involves initializing SOC stocks with spatially detailed baseline data, adding organic matter inputs based on vegetation production, and simulating microbial decomposition while adjusting for climate variables such as temperature and soil moisture. …"
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84
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85
Proposed method approach.
منشور في 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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86
LSTM model performance.
منشور في 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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87
Descriptive statistics.
منشور في 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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88
CNN-LSTM Model performance.
منشور في 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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89
MLP Model performance.
منشور في 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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90
RNN Model performance.
منشور في 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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91
CNN Model performance.
منشور في 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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92
Bi-directional LSTM Model performance.
منشور في 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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93
Early Parkinson’s disease identification via hybrid feature selection from multi-feature subsets and optimized CatBoost with SMOTE
منشور في 2025"…The proposed framework leverages a strong categorical boosting (CatBoost) algorithm optimized using Grid Search Optimization (GSO). …"
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94
Minimal Dateset.
منشور في 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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95
Loss Function Comparison.
منشور في 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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96
Comparative Results of Different Models.
منشور في 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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97
Loss Function Comparison.
منشور في 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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98
Overall Framework of the PSO-KM Model.
منشور في 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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99
Overall Framework of the PSO-KM Model.
منشور في 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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100