Improvement of the performance analysis of activation functions based on DLLSTM classifiers on Human Activity Recognition for classification | ||||
| SVU-International Journal of Engineering Sciences and Applications | ||||
| Volume 4, Issue 2, December 2023, Page 24-35 PDF (1.31 MB) | ||||
| Document Type: Original research articles | ||||
| DOI: 10.21608/svusrc.2023.177608.1088 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Eman ahmed badry   1; Adel Bedair 2; Hany Ahmed Atallah 3; Mohamed essai4
| ||||
| 1Department of Computer and System, Faculty of engineering, Al-Azhar University, Cairo | ||||
| 2. Faculty of Engineering, South Valley University, Qena, Egypt and E-JUST, Alexandria, Egypt. | ||||
| 3Electrical Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt | ||||
| 4Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Qena, Egypt. | ||||
| Abstract | ||||
| Human activity recognition techniques have achieved significant advancements in recent years. However, the performance of the generalization model may be hampered by the methods' heavy reliance on human feature extraction. Deep learning methods are becoming more and more effective, which has led to a lot of interest in employing these approaches to understand human behaviors in mobile and wearable computing settings. In place of the conventional hyperbolic tangent (tanh) activation function for human activity recognition, which can be applied in a variety of applications, in this study, the main part of LSTM neural networks is developed by employing 26 state functions to suggest Deep Learning Long Short-Term Memory (DLLSTM) classifiers. In LSTM network units, the sigmoid and tanh functions are often used as activation functions. The vanishing gradient issue that RNNs encounter can be effectively solved by LSTM networks. The effectiveness of the suggested DLLSTM classifiers for classification tasks was investigated using three different deep learning optimization techniques. The simulation results show that the suggested classifiers, which utilize the Modified -Elliott, Gaussian, and wave as DLLSTM classifiers, outperform the tanh classifier by getting a perfect accuracy rate of 99.92%, 99.5%, and 99.95% as opposed to their 96.4%, respectively. | ||||
| Keywords | ||||
| HAR; LSTM; DNN; activation Function; tanh gate | ||||
|
Statistics Article View: 152 PDF Download: 198 |
||||
