Search alternatives:
required optimization » guided optimization (Expand Search), resource optimization (Expand Search), feature optimization (Expand Search)
models optimization » model optimization (Expand Search), process optimization (Expand Search), wolf 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)
models optimization » model optimization (Expand Search), process optimization (Expand Search), wolf 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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81
MLP Model performance.
Published 2024“…Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. …”
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82
RNN Model performance.
Published 2024“…Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. …”
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83
CNN Model performance.
Published 2024“…Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. …”
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84
Machine learning deployment strategies and schematic illustration of the proposed generative adversarial algorithm for domain adaptation.
Published 2022“…<p><b>(A)</b> There are four primary methods by which machine learning models can be deployed in a context with distinct data domains: 1) train a model on one domain and deploy it across multiple distinct domains, 2) train multiple bespoke models that are optimized for deployment on individual domains, 3) train and deploy a single global model on all domains, and 4) train a model on one domain and adapt it through technical means to make it performant on a distinct domain. …”
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85
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86
Data used to drive the Double Layer Carbon Model in the Qinling Mountains.
Published 2024“…The DLCM defines four soil carbon pools, categorized based on their location within the soil profile and their decomposition rates. The model divides the soil profile into topsoil (0-20 cm) and subsoil (20–100 cm) layers to match the SOC maps of the corresponding two layers generated by data-driven models. …”
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87
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90
Business priorities.
Published 2025“…Based on a comprehensive assessment of service transmission reliability and time costs, a route satisfaction evaluation function model has been developed. Utilizing this model, an enhanced Risk-Time Ant Colony Optimization (RT-ACO) routing algorithm is proposed, which builds upon the traditional ant colony algorithm. …”
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91
Topology of 14-node communication network.
Published 2025“…Based on a comprehensive assessment of service transmission reliability and time costs, a route satisfaction evaluation function model has been developed. Utilizing this model, an enhanced Risk-Time Ant Colony Optimization (RT-ACO) routing algorithm is proposed, which builds upon the traditional ant colony algorithm. …”
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92
Routing policy based on path satisfaction.
Published 2025“…Based on a comprehensive assessment of service transmission reliability and time costs, a route satisfaction evaluation function model has been developed. Utilizing this model, an enhanced Risk-Time Ant Colony Optimization (RT-ACO) routing algorithm is proposed, which builds upon the traditional ant colony algorithm. …”
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93
Changes of risk value under different parameters.
Published 2025“…Based on a comprehensive assessment of service transmission reliability and time costs, a route satisfaction evaluation function model has been developed. Utilizing this model, an enhanced Risk-Time Ant Colony Optimization (RT-ACO) routing algorithm is proposed, which builds upon the traditional ant colony algorithm. …”
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94
Performance of active and standby paths.
Published 2025“…Based on a comprehensive assessment of service transmission reliability and time costs, a route satisfaction evaluation function model has been developed. Utilizing this model, an enhanced Risk-Time Ant Colony Optimization (RT-ACO) routing algorithm is proposed, which builds upon the traditional ant colony algorithm. …”
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95
Proposed model tuned hyperparameters.
Published 2024“…Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. …”
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96
Proposed model sensitivity outcome.
Published 2024“…Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. …”
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97
CNN-LSTM Model performance.
Published 2024“…Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. …”
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98
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100