The skeleton algorithm identifies mitochondrial density in sample images from cells. حسب Margarita M. Ivanova (11604141)

Loss function value changes in CNN training changes with epochs for both training dataset and testing dataset. حسب Jiaxi Wang (545353)

The Precision-Recall curve of both the models. حسب Aastha Vatsyayan (9981916)

"…</p><p>Results</p><p>Using an independent validation dataset of ACMG & AMP classified variants, as well as a patient set of functionally validated variants, we showed how both algorithms perform and can be used to classify large numbers of variants in clinical as well as research settings.…"

Raw task performance on both experiments, with respect to conditions. حسب Anis Najar (9745561)

Principal component analysis applied to both datasets. حسب Anny K. G. Rodrigues (11689849)

Summary of the contrasts obtained from the normalized images for both inclusions as a function of the host type and the wavelength. حسب María Victoria Waks-Serra (21578611)

Optimum feature set for classification using both core and margin information utilizing GLCM, GRLM, and GLSZM texture methods and SVM-RBF classification algorithm. حسب Laurentius O. Osapoetra (9931759)

Venn diagrams showing overlap of the total of overrepresented/underrepresented kinases for both cohorts. حسب Erica A. K. DePasquale (11851483)

The image of the Aekley function. حسب Zhengyu Xu (8550660)

Bioinformatic Analysis Reveals both Oversampled and Underexplored Biosynthetic Diversity in Nonribosomal Peptides حسب Bo-Siyuan Jian (14669996)

The ALO algorithm optimization flowchart. حسب Wenjing Wang (181404)

"…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …"

The IALO algorithm solution flowchart. حسب Wenjing Wang (181404)

"…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …"

Comparison of the performance of both models against 13 other models on the independent test dataset. حسب Aastha Vatsyayan (9981916)

Varietal predicted parameters under both water conditions per groups of varieties. حسب Maud Delattre (19106551)

Evolution of the rescaled hydraulic diameter () and complexity () in both simulations and seal tomograms as a function of simulation timestep and relative age (). حسب Jonathan E Kings (20818367)

Continuous Probability Distributions generated by the PIPE Algorithm حسب LUIS G.B. PINHO (14073372)

"…The PIPE algorithm can generate several candidate functions to fit the empirical distribution of data. …"

Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional حسب Ryan Stocks (16867476)

"…Kohn–Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. …"

The impact of extrinsic noise sources on the measured SNR is plotted for both CW and TG against varying optical filter magnitudes. حسب Nicholas H. Vitale (20469289)

The pseudocode for the NAFPSO algorithm. حسب Huichao Guo (14515171)

"…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. …"

PSO algorithm flowchart. حسب Huichao Guo (14515171)

"…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. …"