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1921
Computational Temporal Ghost Imaging Using Intensity-Only Detection Over a Single Optical Fiber
Published 2018-01-01Get full text
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1922
Optimal tracking controllers with Off-policy Reinforcement Learning Algorithm in Quadrotor
Published 2022-02-01Get full text
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1923
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1924
Masterpiece Optimization Algorithm: A New Method for Solving Engineering Problems
Published 2025-01-01Get full text
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1925
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1926
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1927
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1928
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1929
Computational Design and Evaluation of Peptides to Target SARS-CoV-2 Spike–ACE2 Interaction
Published 2025-04-01Get full text
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1930
Optimization of Teaching Management System Based on Association Rules Algorithm
Published 2021-01-01Get full text
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1931
Damage Detection of Steel Roof Systems Using Planet Optimization Algorithm
Published 2025-10-01“…The achieved results illustrate that in terms of stability, robustness, and quality of the obtained solution, POA is one of most outstanding optimization algorithms. POA ranks No. 1 in functions F1, F4, F7, F8, F13, while POA is highly ranked and sufficiently competitive with the other contenders in the rest of tests. …”
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1932
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1934
Prediction of black soldier fly larval sex and morphological traits using computer vision and deep learning
Published 2025-08-01Get full text
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1935
Optimized DenseNet Architectures for Precise Classification of Edible and Poisonous Mushrooms
Published 2025-06-01Get full text
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1936
Discrete Sizing Optimization of Steel Structures Using Modified Fireworks Algorithm
Published 2025-01-01Get full text
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1937
An Improved Ant Colony Optimization to Uncover Customer Characteristics for Churn Prediction
Published 2025-04-01Get full text
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1938
A Dynamic State Cluster-Based Particle Swarm Optimization Algorithm
Published 2025-08-01“…To address these limitations, this paper introduces a dynamic state cluster-based particle swarm optimization (DSCPSO) algorithm, which employs population phenotypic entropy based on clustering technique. (1) The algorithm provides theoretical splitting points by mathematically analyzing the population into four states: convergence, exploitation, escape, and exploration, enabling more effective parameter adaptive mechanisms. (2) DSCPSO incorporates sinusoidal chaos mapping to dynamically adjust inertia weights, allowing particles to better align with the population’s evolutionary state. (3) During the convergence state, an intelligent particle migration strategy (IPMS) enhances search efficiency within the solution space, preventing unnecessary computational resource consumption. …”
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1939
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1940
Solving the Independent Domination Problem by the Quantum Approximate Optimization Algorithm
Published 2024-12-01Get full text
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