Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model
Joint Authors
Zhang, Jianhui
Khan, Sulaiman
Nazir, Shah
He, Yang
Nie, Baisheng
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-09
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
He, Yang& Nazir, Shah& Nie, Baisheng& Khan, Sulaiman& Zhang, Jianhui. 2020. Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model. Complexity،Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1141962
Modern Language Association (MLA)
He, Yang…[et al.]. Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model. Complexity No. 2020 (2020), pp.1-6.
https://search.emarefa.net/detail/BIM-1141962
American Medical Association (AMA)
He, Yang& Nazir, Shah& Nie, Baisheng& Khan, Sulaiman& Zhang, Jianhui. Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model. Complexity. 2020. Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1141962
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141962