Search alternatives:
optimisation algorithm » optimization algorithms (Expand Search), maximization algorithm (Expand Search), identification algorithm (Expand Search)
process optimisation » process optimization (Expand Search), robust optimisation (Expand Search), process simulation (Expand Search)
guided optimization » based optimization (Expand Search), model optimization (Expand Search)
data process » data processing (Expand Search), damage process (Expand Search), data access (Expand Search)
primary data » primary care (Expand Search)
binary data » dietary data (Expand Search)
optimisation algorithm » optimization algorithms (Expand Search), maximization algorithm (Expand Search), identification algorithm (Expand Search)
process optimisation » process optimization (Expand Search), robust optimisation (Expand Search), process simulation (Expand Search)
guided optimization » based optimization (Expand Search), model optimization (Expand Search)
data process » data processing (Expand Search), damage process (Expand Search), data access (Expand Search)
primary data » primary care (Expand Search)
binary data » dietary data (Expand Search)
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Models’ performance without optimization.
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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Data_Sheet_1_Prediction of patient choice tendency in medical decision-making based on machine learning algorithm.pdf
Published 2023“…Objective<p>Machine learning (ML) algorithms, as an early branch of artificial intelligence technology, can effectively simulate human behavior by training on data from the training set. …”
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RNN performance comparison with/out optimization.
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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Datasets used for the study and their sources.
Published 2023“…Projecting into 2030, this study aimed at providing geographical information data for guiding future policies on siting required healthcare facilities. …”
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Scheduling method for pairing night-shift and morning-shift duties on metro lines with complex structure
Published 2022“…To improve the rest time of crews, this paper proposes a binary programming model to optimise the NMDPP. Moreover, a hybrid algorithm combining General Variable Neighbourhood Search (GVNS) with an Assignment Algorithm is designed to find high-quality solutions for this problem. …”
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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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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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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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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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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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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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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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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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Image 2_Integrative prognostic modeling for stage III lung adenosquamous carcinoma post-tumor resection: machine learning insights and web-based implementation.png
Published 2024“…Introduction<p>The prognostic landscape of stage III Lung Adenosquamous Carcinoma (ASC) following primary tumor resection remains underexplored. A thoughtfully developed prognostic model has the potential to guide clinicians in patient counseling and the formulation of effective therapeutic strategies.…”