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algorithm python » algorithm within (Expand Search), algorithms within (Expand Search)
python function » protein function (Expand Search)
algorithm both » algorithm blood (Expand Search), algorithm b (Expand Search), algorithm co (Expand Search)
algorithm etc » algorithm _ (Expand Search), algorithm b (Expand Search), algorithm a (Expand Search)
both function » body function (Expand Search), growth function (Expand Search), beach function (Expand Search)
etc function » fc function (Expand Search), spc function (Expand Search), npc function (Expand Search)
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The ALO algorithm optimization flowchart.
Published 2024“…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …”
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The IALO algorithm solution flowchart.
Published 2024“…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …”
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Comparison of scores obtained by our interpenetration and scoring algorithm (ISA) and ROSETTA for a subset of structures.
Published 2023“…However, our algorithm was 1000 times faster than pyROSETTA (both algorithms have been parallelized on a per-structure basis using the Python package joblib [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010531#pcbi.1010531.ref069" target="_blank">69</a>]).…”
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Comparison of UACI among different algorithms.
Published 2025“…Based on the 2D-SQSM, this paper further designs a highly robust color image encryption algorithm. First, by introducing different hash functions multiple times, the correlation between the key and plaintext is enhanced, significantly improving resistance against brute-force attacks; second, cyclic shifting and segmentation-recombination operations are applied separately to the three RGB channels to effectively disrupt pixel distribution and significantly reduce spatial correlation between pixels; finally, the chaotic sequence generated by the 2D-SQSM is utilized for XOR diffusion, further enhancing the randomness and diffusion capability of the ciphertext. …”
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Comparison of NPCR among different algorithms.
Published 2025“…Based on the 2D-SQSM, this paper further designs a highly robust color image encryption algorithm. First, by introducing different hash functions multiple times, the correlation between the key and plaintext is enhanced, significantly improving resistance against brute-force attacks; second, cyclic shifting and segmentation-recombination operations are applied separately to the three RGB channels to effectively disrupt pixel distribution and significantly reduce spatial correlation between pixels; finally, the chaotic sequence generated by the 2D-SQSM is utilized for XOR diffusion, further enhancing the randomness and diffusion capability of the ciphertext. …”
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Continuous Probability Distributions generated by the PIPE Algorithm
Published 2022“…The PIPE algorithm can generate several candidate functions to fit the empirical distribution of data. …”
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Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional
Published 2025“…Kohn–Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. …”
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The pseudocode for the NAFPSO algorithm.
Published 2025“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”
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PSO algorithm flowchart.
Published 2025“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”
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Parselmouth for bioacoustics: automated acoustic analysis in Python
Published 2023“…Five years ago, the Python package Parselmouth was released to provide easy and intuitive access to all functionality in the Praat software. …”