Publish In |
International Journal of Management and Applied Science (IJMAS)-IJMAS |
Journal Home Volume Issue |
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Issue |
Volume-5,Issue-9 ( Sep, 2019 ) | |||||||||
Paper Title |
Combination of Long-Short Term Memory (LSTM) and Multi-Layer Perceptron (MLP) Algorithms for Classifying Hand Gestures | |||||||||
Author Name |
Maricel L. Amit, Arnel C. Fajardo, Ruji P. Medina | |||||||||
Affilition |
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Pages |
140-145 | |||||||||
Abstract |
The study aimed to enhance the accuracy of the hand motion and gestures acknowledgment by utilizing the Long Short-Term Memory (LSTM), a feature extraction and Multi-layer Perceptron (MLP) as the classifier with regards to the classification of hand gestures to test the accuracy of the method compared to the other algorithms such as the LSTM+CNN (LCNN), CNN-RNN, VGG16, and DCNN+MCSVM. In this paper, a novel LSTM+MLP model is presented on the classification for American Sign Language (ASL) hand signs composed of a total of 400 images. Furthermore, based on the findings of the study, the model LSTM+MLP outperformed the previous algorithms which gained a 100% accuracy rate during the training and testing as shown in the tables, illustrations and graphs. Keywords - Classification, Long-Short Term Memory, Multi-layer Perceptron, Neural Networks | |||||||||
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