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501
LSTM-optimized PID controller flow.
Published 2025“…To solve this problem, the study proposes an improved PID controller based on the Long Short-Term Memory (LSTM) optimized by the Whale Optimization Algorithm (WOA). …”
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502
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506
Images of brain tumors used in the experiments and their corresponding histograms.
Published 2024Subjects: -
507
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510
An optimal solution for the HFS instance.
Published 2025“…Next, a CP model (IPMMPO-CP) applicable to multi-scenario HFS problems is proposed. Finally, based on a large number of instances and real cases, IPMMPO-CP is compared with 9 representative algorithms and 2 latest CP models. …”
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511
Data and Scripts for 'A Predictive Model for Attention-deficit/hyperactivity Disorder and Its Subtypes in Children: A Back-propagation Neural Network Optimized with Genetic Algorithms Based on Multidimensional Cognitive Ability Tests'
Published 2024“…Using <a href="" target="_blank">multidimensional cognitive ability tests</a> and a <a href="" target="_blank">back-propagation neural network</a> optimized by a genetic algorithm, this study developed a computer-aided diagnostic model to distinguish children with attention-deficit/hyperactivity disorder and its specific subtypes. …”
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512
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LIE value carves of different algorithms when propagation probability p = 0.01.
Published 2025Subjects: -
518
Comparing the optimal values and CPU time for a classroom coded with ED-K1–11/B.
Published 2025Subjects: -
519
IRBMO vs. meta-heuristic algorithms boxplot.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
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520
IRBMO vs. feature selection algorithm boxplot.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”