Efficient Net B7 Convolutional Neural Network-Based Security and Privacy Preserving Method for Social IOT Environments

Main Article Content

Maniveena. C
Kalaiselvi. R

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 automatically generate EfficientNet b7 feature frameworks for text classification tasks. 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.

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Articles

Author Biographies

Maniveena. C, Noorul Islam Centre for Higher Education

Research Scholar, Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Thuckalay Kanyakumari, India

Kalaiselvi. R, RMK College of Engineering and Technology

Associate Professor, Department of Computer Science and Engineering, RMK College of Engineering and Technology, Puduvoyal, Gummidipoondi, Anna University, Chennai, India

How to Cite

[1]
“Efficient Net B7 Convolutional Neural Network-Based Security and Privacy Preserving Method for Social IOT Environments ”, IJCSR, vol. 3, no. 2, pp. 47–54, Sep. 2024, doi: 10.37391/.

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