lbp descriptor  (MathWorks Inc)

Journal: Soft Computing

Article Title: An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

doi: 10.1007/s00500-022-06886-3

Figure Lengend Snippet: The impact of features descriptors on the performance of AOA against other recent optimizers over fitness measures

Article Snippet: For this, we have integrated three textural descriptors, including HOG, LBP, and GLCM tested over smallest datasets (GT and FEI) and largest dataset (Gallagher’s database).

Techniques:

Journal: Soft Computing

Article Title: An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

doi: 10.1007/s00500-022-06886-3

Figure Lengend Snippet: The impact of features descriptors on the performance of AOA against other recent optimizers over Cpu Time measures

Article Snippet: For this, we have integrated three textural descriptors, including HOG, LBP, and GLCM tested over smallest datasets (GT and FEI) and largest dataset (Gallagher’s database).

Techniques:

Journal: Soft Computing

Article Title: An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

doi: 10.1007/s00500-022-06886-3

Figure Lengend Snippet: The impact of features descriptors on the performance of AOA against other recent optimizers over Accuracy measures

Article Snippet: For this, we have integrated three textural descriptors, including HOG, LBP, and GLCM tested over smallest datasets (GT and FEI) and largest dataset (Gallagher’s database).

Techniques:

Journal: Soft Computing

Article Title: An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

doi: 10.1007/s00500-022-06886-3

Figure Lengend Snippet: The impact of features descriptors on the performance of AOA against other recent optimizers over Selection ratio measures

Article Snippet: For this, we have integrated three textural descriptors, including HOG, LBP, and GLCM tested over smallest datasets (GT and FEI) and largest dataset (Gallagher’s database).

Techniques: Selection

Journal: Soft Computing

Article Title: An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

doi: 10.1007/s00500-022-06886-3

Figure Lengend Snippet: The impact of features descriptors on the performance of AOA against other recent optimizers over Recall measures

Article Snippet: For this, we have integrated three textural descriptors, including HOG, LBP, and GLCM tested over smallest datasets (GT and FEI) and largest dataset (Gallagher’s database).

Techniques:

Journal: Soft Computing

Article Title: An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

doi: 10.1007/s00500-022-06886-3

Figure Lengend Snippet: The impact of features descriptors on the performance of AOA against other recent optimizers over Precision measures

Article Snippet: For this, we have integrated three textural descriptors, including HOG, LBP, and GLCM tested over smallest datasets (GT and FEI) and largest dataset (Gallagher’s database).

Techniques:

Journal: Soft Computing

Article Title: An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

doi: 10.1007/s00500-022-06886-3

Figure Lengend Snippet: The impact of features descriptors on the performance of AOA against other recent optimizers over F-score measures

Article Snippet: For this, we have integrated three textural descriptors, including HOG, LBP, and GLCM tested over smallest datasets (GT and FEI) and largest dataset (Gallagher’s database).

Techniques:

Journal: Soft Computing

Article Title: An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

doi: 10.1007/s00500-022-06886-3

Figure Lengend Snippet: Comparative performance in terms of accuracy with the existing methods– GT dataset

Article Snippet: For this, we have integrated three textural descriptors, including HOG, LBP, and GLCM tested over smallest datasets (GT and FEI) and largest dataset (Gallagher’s database).

Techniques:

Journal: Soft Computing

Article Title: An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

doi: 10.1007/s00500-022-06886-3

Figure Lengend Snippet: Statistical study using Wilcoxon’s test ( In bold best values \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p <0.05$$\end{document} p < 0.05 , which implies that AOA is substantial against algorithm X)

Article Snippet: For this, we have integrated three textural descriptors, including HOG, LBP, and GLCM tested over smallest datasets (GT and FEI) and largest dataset (Gallagher’s database).

Techniques:

Journal: Soft Computing

Article Title: An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

doi: 10.1007/s00500-022-06886-3

Figure Lengend Snippet: Comparative performance in terms of accuracy with the existing approaches–FEI dataset

Article Snippet: For this, we have integrated three textural descriptors, including HOG, LBP, and GLCM tested over smallest datasets (GT and FEI) and largest dataset (Gallagher’s database).

Techniques:

Journal: Soft Computing

Article Title: An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

doi: 10.1007/s00500-022-06886-3

Figure Lengend Snippet: Comparative performance in terms of accuracy with the existing approaches–Gallagher’s dataset under Dago’s protocol

Article Snippet: For this, we have integrated three textural descriptors, including HOG, LBP, and GLCM tested over smallest datasets (GT and FEI) and largest dataset (Gallagher’s database).

Techniques: