Lens imaging opposition-based learning. by Yuqi Xiong (12343771)

“…The algorithm integrates three key strategies: a precise population elimination strategy, which optimizes the population structure by eliminating individuals with low fitness and intelligently generating new ones; a lens imaging-based opposition learning strategy, which expands the exploration of the solution space through reflection and scaling to reduce the risk of local optima; and a boundary control strategy based on the best individual, which effectively constrains the search range to avoid inefficient searches and premature convergence. …”

The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms. by Ahmed M. Elshewey (21463867)

Principle of lens imaging. by Yuefeng Leng (20678476)

Optical Assessment of Tear Glucose by Smart Biosensor Based on Nanoparticle Embedded Contact Lens by Hee-Jae Jeon (4614121)

“…Additionally, we propose an image processing algorithm that automatically optimizes the measurement accuracy even in the presence of image blurring, possibly caused by breathing, subtle movements, and eye blinking. …”

Optical Assessment of Tear Glucose by Smart Biosensor Based on Nanoparticle Embedded Contact Lens by Hee-Jae Jeon (4614121)

“…Additionally, we propose an image processing algorithm that automatically optimizes the measurement accuracy even in the presence of image blurring, possibly caused by breathing, subtle movements, and eye blinking. …”

Flowchart of low-light image enhancement based on MESBOA. by Yuqi Xiong (12343771)

Flowchart of low-light image enhancement based on MESBOA. by Yuqi Xiong (12343771)

Compare algorithm parameter settings. by Yuqi Xiong (12343771)

“…The algorithm integrates three key strategies: a precise population elimination strategy, which optimizes the population structure by eliminating individuals with low fitness and intelligently generating new ones; a lens imaging-based opposition learning strategy, which expands the exploration of the solution space through reflection and scaling to reduce the risk of local optima; and a boundary control strategy based on the best individual, which effectively constrains the search range to avoid inefficient searches and premature convergence. …”

Minimizing the Optical Illusion of Nanoparticles in Single Cells Using Four-Dimensional Cuboid Multiangle Illumination-Based Light-Sheet Super-Resolution Imaging by Yingying Cao (4777638)

Subjects:

Minimizing the Optical Illusion of Nanoparticles in Single Cells Using Four-Dimensional Cuboid Multiangle Illumination-Based Light-Sheet Super-Resolution Imaging by Yingying Cao (4777638)

Subjects:

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…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. …”

Image4_CNN-Based Cell Analysis: From Image to Quantitative Representation.TIF by Cédric Allier (4180903)

“…<p>We present a novel deep learning-based quantification pipeline for the analysis of cell culture images acquired by lens-free microscopy. …”

Image1_CNN-Based Cell Analysis: From Image to Quantitative Representation.TIF by Cédric Allier (4180903)

“…<p>We present a novel deep learning-based quantification pipeline for the analysis of cell culture images acquired by lens-free microscopy. …”

Image3_CNN-Based Cell Analysis: From Image to Quantitative Representation.TIF by Cédric Allier (4180903)

“…<p>We present a novel deep learning-based quantification pipeline for the analysis of cell culture images acquired by lens-free microscopy. …”

Image2_CNN-Based Cell Analysis: From Image to Quantitative Representation.TIF by Cédric Allier (4180903)

“…<p>We present a novel deep learning-based quantification pipeline for the analysis of cell culture images acquired by lens-free microscopy. …”

DataSheet1_CNN-Based Cell Analysis: From Image to Quantitative Representation.pdf by Cédric Allier (4180903)

“…<p>We present a novel deep learning-based quantification pipeline for the analysis of cell culture images acquired by lens-free microscopy. …”

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

“…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. …”

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…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. …”

-value on CEC2022 (dim = 20). by Yuqi Xiong (12343771)

“…The algorithm integrates three key strategies: a precise population elimination strategy, which optimizes the population structure by eliminating individuals with low fitness and intelligently generating new ones; a lens imaging-based opposition learning strategy, which expands the exploration of the solution space through reflection and scaling to reduce the risk of local optima; and a boundary control strategy based on the best individual, which effectively constrains the search range to avoid inefficient searches and premature convergence. …”

Precision elimination strategy. by Yuqi Xiong (12343771)

“…The algorithm integrates three key strategies: a precise population elimination strategy, which optimizes the population structure by eliminating individuals with low fitness and intelligently generating new ones; a lens imaging-based opposition learning strategy, which expands the exploration of the solution space through reflection and scaling to reduce the risk of local optima; and a boundary control strategy based on the best individual, which effectively constrains the search range to avoid inefficient searches and premature convergence. …”