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Home / Archives / Volume-4 / Issue-3 / Article-7

Volume - 4 | Issue - 3 | september 2022

Evaluating Performance of Different Machine Learning Algorithms for the Acute EMG Hand Gesture Datasets
Jeevanshi Sharma  , Rajat Maheshwari, Salman Khan, Abid Ali Khan
Pages: 192-201
Cite this article
Sharma, Jeevanshi, Rajat Maheshwari, Salman Khan, and Abid Ali Khan. "Evaluating Performance of Different Machine Learning Algorithms for the Acute EMG Hand Gesture Datasets." Journal of Electronics and Informatics 4, no. 3 (2022): 192-201
DOI
10.36548/jei.2022.3.007
Published
14 September, 2022
Abstract

In this paper, different machine learning and tabular learning classification algorithms have been studied and compared on the acute hand-gesture Electromyogram dataset. The comparative study between different models such as KNN, RandomForest, TabNet, etc. depicts that small datasets can achieve high-level accuracy along with the intuition of high-performing neural net architectures through tabular learning approaches like TabNet. The performed analysis produced an accuracy of 99.9% through TabNet while other conventional classifiers also gave satisfactory results with KNN being at highest achieving accuracy of 97.8 %.

Keywords

Machine Learning TabNet Hand Gestures EMG Dataset XG Boost Algorithm

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