Search alternatives:
driven optimization » design optimization (Expand Search), guided optimization (Expand Search), dose optimization (Expand Search)
across optimization » cost optimization (Expand Search), stress optimization (Expand Search), process optimization (Expand Search)
binary hastv » binary health (Expand Search), binary data (Expand Search), binary mask (Expand Search)
hastv driven » hastv1 driven (Expand Search), data driven (Expand Search), atp driven (Expand Search)
primary data » primary care (Expand Search)
driven optimization » design optimization (Expand Search), guided optimization (Expand Search), dose optimization (Expand Search)
across optimization » cost optimization (Expand Search), stress optimization (Expand Search), process optimization (Expand Search)
binary hastv » binary health (Expand Search), binary data (Expand Search), binary mask (Expand Search)
hastv driven » hastv1 driven (Expand Search), data driven (Expand Search), atp driven (Expand Search)
primary data » primary care (Expand Search)
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Pseudocode of artificial dragonfly algorithm.
Published 2023“…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”
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The Hopfield artificial neural network algorithm.
Published 2023“…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”
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Flow diagram of Wan Abdullah method for HNN.
Published 2023“…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”
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Training error and accuracy for all HNN models.
Published 2023“…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”
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G<i>m</i>R performance of various HNN-EB<i>k</i>SAT models.
Published 2023“…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”
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<i>MAPE performance of various</i> HNN-EB<i>k</i>SAT models.
Published 2023“…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”
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RMSE performance of various HNN-EB<i>k</i>SAT models.
Published 2023“…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”
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Models’ performance without optimization.
Published 2024“…The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. …”
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RNN performance comparison with/out optimization.
Published 2024“…The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. …”
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All online review text data.
Published 2025“…The findings showed: 1) Museum visitors were highly concentrated in eastern coastal regions, with spatial distribution evolving from single-core to multi-core clusters, gradually expanding into central areas (e.g., Henan, Hubei, Shaanxi). 2) Museum image perception has shifted from object-centered to more human-centered experiences, with significant differences across the various categories. 3) Over 75% of visitors reported positive experiences, with ethnography museums showing the highest satisfaction in 2024 (<i>Pro</i> = 0.922), whereas history museums consistently had the lowest. 4) Satisfaction drivers were dynamic, with 85.26% of perception themes significantly correlated with satisfaction (<i>p</i> < 0.01), with rich collections, distinctive features, immersive experiences, and diverse visitation forms identified as the primary contributors to positive visitor experiences. …”
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Proposed reinforcement learning architecture.
Published 2025“…<div><p>In the realm of game playing, deep reinforcement learning predominantly relies on visual input to map states to actions. The visual data extracted from the game environment serves as the primary foundation for state representation in reinforcement learning agents. …”
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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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