Deep convolutional neural network-based Leveraging Lion Swarm Optimizer for gesture recognition and classification
Vision-based human gesture detection is the task of forecasting a gesture, namely clapping or sign language gestures, or waving hello, utilizing various video frames. One of the attractive features of gesture detection is that it makes it possible for humans to interact with devices and computers wi...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-03-01
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| Series: | AIMS Mathematics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.2024457?viewType=HTML |
| _version_ | 1797257710943600640 |
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| author | Mashael Maashi Mohammed Abdullah Al-Hagery Mohammed Rizwanullah Azza Elneil Osman |
| author_facet | Mashael Maashi Mohammed Abdullah Al-Hagery Mohammed Rizwanullah Azza Elneil Osman |
| author_sort | Mashael Maashi |
| collection | DOAJ |
| description | Vision-based human gesture detection is the task of forecasting a gesture, namely clapping or sign language gestures, or waving hello, utilizing various video frames. One of the attractive features of gesture detection is that it makes it possible for humans to interact with devices and computers without the necessity for an external input tool like a remote control or a mouse. Gesture detection from videos has various applications, like robot learning, control of consumer electronics computer games, and mechanical systems. This study leverages the Lion Swarm optimizer with a deep convolutional neural network (LSO-DCNN) for gesture recognition and classification. The purpose of the LSO-DCNN technique lies in the proper identification and categorization of various categories of gestures that exist in the input images. The presented LSO-DCNN model follows a three-step procedure. At the initial step, the 1D-convolutional neural network (1D-CNN) method derives a collection of feature vectors. In the second step, the LSO algorithm optimally chooses the hyperparameter values of the 1D-CNN model. At the final step, the extreme gradient boosting (XGBoost) classifier allocates proper classes, i.e., it recognizes the gestures efficaciously. To demonstrate the enhanced gesture classification results of the LSO-DCNN approach, a wide range of experimental results are investigated. The brief comparative study reported the improvements in the LSO-DCNN technique in the gesture recognition process. |
| first_indexed | 2024-04-24T22:41:58Z |
| format | Article |
| id | doaj.art-d44ff28a54f04fcdba9a823a2a75f5d7 |
| institution | Directory Open Access Journal |
| issn | 2473-6988 |
| language | English |
| last_indexed | 2024-04-24T22:41:58Z |
| publishDate | 2024-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj.art-d44ff28a54f04fcdba9a823a2a75f5d72024-03-19T01:32:59ZengAIMS PressAIMS Mathematics2473-69882024-03-01949380939310.3934/math.2024457Deep convolutional neural network-based Leveraging Lion Swarm Optimizer for gesture recognition and classificationMashael Maashi 0Mohammed Abdullah Al-Hagery1Mohammed Rizwanullah 2Azza Elneil Osman31. Department of Software Engineering, College of Computer and Information Sciences, King Saud University, PO box 103786, Riyadh 11543, Saudi Arabia 2. King Salman Center for Disability Research, Riyadh, Saudi Arabia3. Department of Computer Science, College of Computer, Qassim University, Saudi Arabia4. Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia4. Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi ArabiaVision-based human gesture detection is the task of forecasting a gesture, namely clapping or sign language gestures, or waving hello, utilizing various video frames. One of the attractive features of gesture detection is that it makes it possible for humans to interact with devices and computers without the necessity for an external input tool like a remote control or a mouse. Gesture detection from videos has various applications, like robot learning, control of consumer electronics computer games, and mechanical systems. This study leverages the Lion Swarm optimizer with a deep convolutional neural network (LSO-DCNN) for gesture recognition and classification. The purpose of the LSO-DCNN technique lies in the proper identification and categorization of various categories of gestures that exist in the input images. The presented LSO-DCNN model follows a three-step procedure. At the initial step, the 1D-convolutional neural network (1D-CNN) method derives a collection of feature vectors. In the second step, the LSO algorithm optimally chooses the hyperparameter values of the 1D-CNN model. At the final step, the extreme gradient boosting (XGBoost) classifier allocates proper classes, i.e., it recognizes the gestures efficaciously. To demonstrate the enhanced gesture classification results of the LSO-DCNN approach, a wide range of experimental results are investigated. The brief comparative study reported the improvements in the LSO-DCNN technique in the gesture recognition process.https://www.aimspress.com/article/doi/10.3934/math.2024457?viewType=HTMLhuman-computer interactionswarm intelligencecnn modelgesture recognitiontransfer learning |
| spellingShingle | Mashael Maashi Mohammed Abdullah Al-Hagery Mohammed Rizwanullah Azza Elneil Osman Deep convolutional neural network-based Leveraging Lion Swarm Optimizer for gesture recognition and classification AIMS Mathematics human-computer interaction swarm intelligence cnn model gesture recognition transfer learning |
| title | Deep convolutional neural network-based Leveraging Lion Swarm Optimizer for gesture recognition and classification |
| title_full | Deep convolutional neural network-based Leveraging Lion Swarm Optimizer for gesture recognition and classification |
| title_fullStr | Deep convolutional neural network-based Leveraging Lion Swarm Optimizer for gesture recognition and classification |
| title_full_unstemmed | Deep convolutional neural network-based Leveraging Lion Swarm Optimizer for gesture recognition and classification |
| title_short | Deep convolutional neural network-based Leveraging Lion Swarm Optimizer for gesture recognition and classification |
| title_sort | deep convolutional neural network based leveraging lion swarm optimizer for gesture recognition and classification |
| topic | human-computer interaction swarm intelligence cnn model gesture recognition transfer learning |
| url | https://www.aimspress.com/article/doi/10.3934/math.2024457?viewType=HTML |
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