Original scientific paper
https://doi.org/10.1080/00051144.2023.2296788
A security and privacy preserving approach based on social IoT and classification using DenseNet convolutional neural network
C. Maniveena
; Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Thuckalay, India
*
R. Kalaiselvi
; Department of Computer Science and Engineering, RMK College of Engineering and Technology, Puduvoyal, India
* Corresponding author.
Abstract
This method is able to synthesize fine-detailed images by the use of a global attention that gives
more attention to the words in the textual descriptions. Also we have the deep attention multimodal similarity model (DAMSM) that calculates the matching loss in the generator. Though
this work produced images of high quality, there was some loss while training the system and
it takes enough time for training. Although there has been little study on applying characterlevel Dense Net algorithms for text classification tasks; the Dense Net structures we suggested in
this paper have shown outstanding performance in image classification tasks. Extensive testing
has revealed that they perform better when it comes to their ability to withstand interruption
and that they can influence exerted many organizations implementing information usage and
language information on the specifications of user privacy protection, framework implies, and
regulatory requirements.
Keywords
Security; privacy preservation; text classification; DenseNet; social IoT
Hrčak ID:
322977
URI
Publication date:
8.1.2024.
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