A security and privacy preserving approach based on social IoT and classification using DenseNet convolutional neural network

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 character-level 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