Correlation heatmap of performance scores among different feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO. حسب Doaa Sami Khafaga (21463870)

Density distribution plot of performance metrics for various feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO. حسب Doaa Sami Khafaga (21463870)

a) Accuracy and b) selected feature size of algorithms on the COVID-19 dataset. حسب Nebojsa Bacanin (13932723)

Performance of the bAD-PSO-Guided WOA algorithm compared with another algorithm. حسب Ahmed M. Elshewey (21463867)

One advantage of our algorithm is that it is not binary in nature. حسب Yong Kuk Kim (13955214)

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment حسب Jianfang Cao (1881379)

"…<div><p>An image classification algorithm based on adaptive feature weight updating is proposed to address the low classification accuracy of the current single-feature classification algorithms and simple multifeature fusion algorithms. …"

Proposed Algorithm. حسب Hend Bayoumi (22693738)

Optimization of tracking parameters based on <i>P</i>_<i>swap</i>. حسب Benjamin Gallois (10131683)

Comparisons between ADAM and NADAM optimizers. حسب Hend Bayoumi (22693738)

Comparison between Bisection and Newton-Raphson in terms of (A) total time consumed, and (B) maximum computation rate. حسب Hend Bayoumi (22693738)

A workflow based on Sentinel-1 SAR data and open-source algorithms for unsupervised burned area detection in Mediterranean ecosystems حسب Giandomenico De Luca (11007332)

Overview of the segmentation algorithm. حسب Michael C. Robitaille (11949174)

MCnebula: Critical Chemical Classes for the Classification and Boost Identification by Visualization for Untargeted LC–MS/MS Data Analysis حسب Lichuang Huang (10679028)

"…This framework consists of three vital steps as follows: (1) abundance-based classes (ABC) selection algorithm, (2) critical chemical classes to classify “features” (corresponding to compounds), and (3) visualization as multiple Child-Nebulae (network graph) with annotation, chemical classification, and structure. …"

MCnebula: Critical Chemical Classes for the Classification and Boost Identification by Visualization for Untargeted LC–MS/MS Data Analysis حسب Lichuang Huang (10679028)

"…This framework consists of three vital steps as follows: (1) abundance-based classes (ABC) selection algorithm, (2) critical chemical classes to classify “features” (corresponding to compounds), and (3) visualization as multiple Child-Nebulae (network graph) with annotation, chemical classification, and structure. …"

MCnebula: Critical Chemical Classes for the Classification and Boost Identification by Visualization for Untargeted LC–MS/MS Data Analysis حسب Lichuang Huang (10679028)

"…This framework consists of three vital steps as follows: (1) abundance-based classes (ABC) selection algorithm, (2) critical chemical classes to classify “features” (corresponding to compounds), and (3) visualization as multiple Child-Nebulae (network graph) with annotation, chemical classification, and structure. …"

MCnebula: Critical Chemical Classes for the Classification and Boost Identification by Visualization for Untargeted LC–MS/MS Data Analysis حسب Lichuang Huang (10679028)

"…This framework consists of three vital steps as follows: (1) abundance-based classes (ABC) selection algorithm, (2) critical chemical classes to classify “features” (corresponding to compounds), and (3) visualization as multiple Child-Nebulae (network graph) with annotation, chemical classification, and structure. …"

Comparisons of computation rate performance for different offloading algorithms.for N = 10, 20, 30. حسب Hend Bayoumi (22693738)

Comparison of total time consumed for different offloading algorithms.for N = 10, 20, 30. حسب Hend Bayoumi (22693738)

Scheme of a transmission DHM. حسب Raul Castañeda (13932971)

Fig 4 - حسب Olaide N. Oyelade (14047002)