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learning algorithm » learning algorithms (Expand Search)
method algorithm » network algorithm (Expand Search), means algorithm (Expand Search), mean algorithm (Expand Search)
develop based » developed based (Expand Search), develop masld (Expand Search), development based (Expand Search)
q learning » _ learning (Expand Search), e learning (Expand Search)
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The flowchart of QLDE algorithm.
Published 2025“…Subsequently, a parameter adaptive adjustment method based on <i>Q</i>-learning is proposed, which significantly augments the clustering performance of <i>K</i>-means. …”
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Schematic diagram of <i>K</i>-means algorithm.
Published 2025“…Subsequently, a parameter adaptive adjustment method based on <i>Q</i>-learning is proposed, which significantly augments the clustering performance of <i>K</i>-means. …”
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Performance of various biclustering algorithms on FilmTrust dataset with Q-learning.
Published 2025“…<p>Performance of various biclustering algorithms on FilmTrust dataset with Q-learning.</p>…”
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Performance of various biclustering algorithms on ML-100K dataset with Q- learning.
Published 2025“…<p>Performance of various biclustering algorithms on ML-100K dataset with Q- learning.</p>…”
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Performance of various biclustering algorithms on ML-latest-small dataset with Q-learning.
Published 2025“…<p>Performance of various biclustering algorithms on ML-latest-small dataset with Q-learning.…”
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Workshop Output: Embedding sustainability literacies in postdigital learning design
Published 2025“…The facilitators propose to develop a collection of crowd-sourced educator activities based on the workshop outputs for a collaborative OER, modelling the intersections of sustainability literacies, creative futures, and learning design.…”
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Developed energy optimization model with LSC based on OAWDO algorithm.
Published 2024“…<p>Developed energy optimization model with LSC based on OAWDO algorithm.…”
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Neighborhood operators (actions) in Q-learning.
Published 2025“…<div><p>Coal blending in thermal power plants is a complex multi-objective challenge involving economic, operational and environmental considerations. This study presents a Q-learning-enhanced NSGA-II (QLNSGA-II) algorithm that integrates the adaptive policy optimization of Q-learning with the elitist selection of NSGA-II to dynamically adjust crossover and mutation rates based on real-time performance metrics. …”