Benchmarking of an Enhanced Grasshopper for Feature Map Optimization of 3D and Depth Map Hand Gestures

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ID: 312867
2024
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Abstract
The Enhanced Grasshopper Optimizer (EGO) for the feature map optimization of 3D and depth map hand gestures is the objective of this paper's benchmarking experiment. Using a dataset of 3D and depth map hand gestures, the effectiveness of the EGO algorithm is examined and contrasted to alternative optimizers. The optimized feature map is tested using the Rosenbrock benchmark test function with EGO and SGD, the findings demonstrate that the EGO algorithm performs better than the alternative techniques in terms of precision and computational time. The execution time of EGO is also benchmarked in this study with the different numbers of input features and shows dominance in performing feature selection for 3D hand gesture detection and classification.
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imported_1777057452_69ebbeac77003 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Arifullah
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/481
Keywords Keywords not found

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