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learning algorithm » learning algorithms (Expand Search)
data algorithm » data algorithms (Expand Search), update algorithm (Expand Search), atlas algorithm (Expand Search)
new algorithm » novel algorithm (Expand Search), _ algorithm (Expand Search), ii algorithm (Expand Search)
data learning » meta learning (Expand Search), deep learning (Expand Search), a learning (Expand Search)
element data » settlement data (Expand Search), relevant data (Expand Search), movement data (Expand Search)
develop new » developing new (Expand Search)
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Algorithmic experimental parameter design.
Published 2024“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
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Spatial spectrum estimation for three algorithms.
Published 2024“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
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Risk element category diagram.
Published 2025“…This article used these data to establish an LSTM model, which trained LSTM to identify potential risks and provide early warning by learning patterns and trends in historical data. …”
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Data-Driven Development of Heterogeneous Catalysts for Propane Dehydrogenation with Machine Learning and Metaheuristic Optimization
Published 2024“…This work highlights the excellent predictive capability of the framework and offers a new data-driven approach for developing high-performance materials for heterogeneous catalysis.…”
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Data-Driven Development of Heterogeneous Catalysts for Propane Dehydrogenation with Machine Learning and Metaheuristic Optimization
Published 2024“…This work highlights the excellent predictive capability of the framework and offers a new data-driven approach for developing high-performance materials for heterogeneous catalysis.…”
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Comparison of algorithm performance aesults.
Published 2025“…The training of the knowledge graph embedding model is similar to that of many models, which requires a large amount of data for learning to achieve the purpose of model development. …”
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