Mobile and pervasive computing is one of the recent paradigms available in the area of information technology. The role of pervasive computing is foremost in the field where it provides the ability to distribute computational services to the surroundings where people work and leads to issues such as trust, privacy, and identity. To provide an optimal solution to these generic problems, the proposed research work aims to implement a deep learning-based pervasive computing architecture to address these problems. Long short-term memory architecture is used during the development of the proposed trusted model. The applicability of the proposed model is validated by comparing its performance with the generic back-propagation neural network. This model results with an accuracy rate of 93.87% for the LSTM-based model much better than 85.88% for the back-propagation-based deep model. The obtained results reflect the usefulness and applicability of such an approach and the competitiveness against other existing ones.

中文翻译：

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使用基于LSTM的分类模型为普适计算开发高效的基于深度学习的可信模型

移动和普及计算是信息技术领域中可用的最新范例之一。普适计算的作用在该领域最为重要，它提供了将计算服务分发到人们工作的环境并导致诸如信任，隐私和身份等问题的能力。为了为这些通用问题提供最佳解决方案，建议的研究工作旨在实现基于深度学习的普适计算体系结构来解决这些问题。在建议的受信模型的开发过程中使用了长短期内存体系结构。通过将模型的性能与通用反向传播神经网络进行比较，验证了该模型的适用性。对于基于LSTM的模型，此模型的准确率为93.87％，远胜于85。88％用于基于反向传播的深度模型。获得的结果反映了这种方法的有用性和适用性以及与其他现有方法的竞争性。