Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Performance analysis of hybrid classification system model for big data stream using internet of things

Performance analysis of hybrid classification system model for big data stream using internet of... Big data and internet of things (IoT) have become the world's prominent technology and reached a high impact on millions of people's daily life that helps in managing environmental and physical systems processed in real-time. In this research, we proposed a hybrid classification system model named multi-output regression with deep belief networks (MOR-DBN) to improve the performance of the classifier when huge amount of streaming data is transferred from IoT devices. Moreover, the improved privacy-preserving rotation based condensation algorithm (P2RoCAl) with Geometric Transformation is also used for obtaining high utility for data streaming to protect from various kinds of attacks during data reconstruction. The simulation results show that proposed framework obtains a high accuracy when compared with other existing algorithms in terms of precision, recall, and F-measure. Overall the proposed MOR_DBN model outperforms by obtaining a 96.21% for SSDS dataset, 97.89% for FRDS dataset, 95.7% for HPDS dataset, and 99.23% for ESDS dataset. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of System of Systems Engineering Inderscience Publishers

Performance analysis of hybrid classification system model for big data stream using internet of things

Loading next page...
 
/lp/inderscience-publishers/performance-analysis-of-hybrid-classification-system-model-for-big-YndldOe3Yv

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1748-0671
eISSN
1748-068X
DOI
10.1504/ijsse.2021.121438
Publisher site
See Article on Publisher Site

Abstract

Big data and internet of things (IoT) have become the world's prominent technology and reached a high impact on millions of people's daily life that helps in managing environmental and physical systems processed in real-time. In this research, we proposed a hybrid classification system model named multi-output regression with deep belief networks (MOR-DBN) to improve the performance of the classifier when huge amount of streaming data is transferred from IoT devices. Moreover, the improved privacy-preserving rotation based condensation algorithm (P2RoCAl) with Geometric Transformation is also used for obtaining high utility for data streaming to protect from various kinds of attacks during data reconstruction. The simulation results show that proposed framework obtains a high accuracy when compared with other existing algorithms in terms of precision, recall, and F-measure. Overall the proposed MOR_DBN model outperforms by obtaining a 96.21% for SSDS dataset, 97.89% for FRDS dataset, 95.7% for HPDS dataset, and 99.23% for ESDS dataset.

Journal

International Journal of System of Systems EngineeringInderscience Publishers

Published: Jan 1, 2021

There are no references for this article.