EfficientNet B7 Convolutional Neural Network-Based Security and Privacy Preserving Method for Social IOT Environments
| International Journal of Electronics and Communication Engineering |
| © 2024 by SSRG - IJECE Journal |
| Volume 11 Issue 8 |
| Year of Publication : 2024 |
| Authors : C. Maniveena, R. Kalaiselvi |
10.14445/23488549/IJECE-V11I8P103
How to Cite?
C. Maniveena, R. Kalaiselvi, "EfficientNet B7 Convolutional Neural Network-Based Security and Privacy Preserving Method for Social IOT Environments," SSRG International Journal of Electronics and Communication Engineering, vol. 11, no. 8, pp. 22-30, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I8P103
Abstract:
This year, one of the most widely used technical frameworks lacks a specific Internet of Things (IoT). Focusing on communication reliability and dependability on IPv6 standards and internet communication technology, the EfficientNet b7 Social IoT network satisfies care and adaptability needs. Despite the high-quality photographs this effort produced, there was some loss during the system's training, which takes time. This work suggested using evolution deep learning to generate EfficientNet b7 feature frameworks for text classification tasks automatically. The proposed approach is tested in the context of an EfficientNet b7-based language similarity analysis model to see if it works. While character-level EfficientNet b7 algorithms have not received much attention for text classification problems, the EfficientNet b7 structures proposed in this research have demonstrated exceptional performance in data classification tasks. A great deal of testing has shown that they are more resilient to disruptions and that they can impact numerous organizations that implement language and information usage policies regarding user privacy protection, framework implications, and legal requirements.
Keywords:
Privacy preserving, EfficientNet b7, Internet of Things, Security, Convolution Neural Network.
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