使用卷积神经网络的基于社交物联网演化编码的安全和隐私保护方法

Maniveena C, R. Kalaiselvi
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引用次数: 0

摘要

今年最流行的技术框架之一无疑是物联网(IoT)。它渗透到众多行业,对人们生活的各个领域都产生了深远的影响。万物互联 "时代是由物联网技术的飞速发展而来,但它也改变了网络边缘终端设备的功能。物联网 "这一名称的演变,就是让物能够智能化,能够与经过验证的设备(物联网)进行对话。在智能设备之间,社交物联网(IoT)设备进行互动并采用社交网络概念。智能设备之间需要安全连接才能实现社交性。为了确定所建议的策略是否实用,我们将其应用到基于卷积神经网络(CNN)的语言相似性分析模型中。使用相遇训练法创建的模型比原始卷积神经网络更准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A security and privacy preserving approach based on social IoT evolving encoding using convolutional neural network
One of the most popular technological frameworks of the year is without a certain Internet of Things (IoT). It permeates numerous industries and has a profound impact on people's lives in all spheres. The “Internet of everything” age is by the IoT technology's rapid development, but it also alters the function of terminal equipment at the network's edge. The name “Internet of Things” has evolved as a result enabling things to be intelligent and competent in talking with verified devices (IoT). Between smart devices, social IoT (IoT) devices interact and adopt social networking concepts. It takes a secure connection between the smart gadgets to enable sociability. To determine whether the suggested strategy is practical it is applied to a convolutional neural network (CNN)-based language similarity analysis model in the context. The model created using the encounter training method is more accurate than the original CNN.
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