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Blockchain Concepts on Computer Vision With Human-Computer Interaction and Secured Data-Sharing Framework

Blockchain Concepts on Computer Vision With Human-Computer Interaction and Secured Data-Sharing Framework

Priyadharshini K., R. Aroul Canessane
Copyright: © 2022 |Volume: 11 |Issue: 4 |Pages: 21
ISSN: 2156-177X|EISSN: 2156-1761|EISBN13: 9781683182528|DOI: 10.4018/IJFSA.312240
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MLA

K., Priyadharshini, and R. Aroul Canessane. "Blockchain Concepts on Computer Vision With Human-Computer Interaction and Secured Data-Sharing Framework." IJFSA vol.11, no.4 2022: pp.1-21. http://doi.org/10.4018/IJFSA.312240

APA

K., P. & Canessane, R. A. (2022). Blockchain Concepts on Computer Vision With Human-Computer Interaction and Secured Data-Sharing Framework. International Journal of Fuzzy System Applications (IJFSA), 11(4), 1-21. http://doi.org/10.4018/IJFSA.312240

Chicago

K., Priyadharshini, and R. Aroul Canessane. "Blockchain Concepts on Computer Vision With Human-Computer Interaction and Secured Data-Sharing Framework," International Journal of Fuzzy System Applications (IJFSA) 11, no.4: 1-21. http://doi.org/10.4018/IJFSA.312240

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Abstract

Presently, the technological developments in the field of human-computer interaction (HCI) have shown that developers are developing cognitive vision systems that provide normal and effective operating mechanism for smart sensors, and the privacy should be maintained in the course of data transfer. Blockchain technology received significant interest to remove third-party business providers, to introduce HCI rapidly, and for secure information sharing in the network. Therefore this paper presents blockchain assisted cognitive vision systems for human-computer interaction and secured data sharing (BCVS-DS) framework. Cognitive vision systems use the information from various sensors that is used to handle and joined by blending techniques. The secured data sharing (SDS) method is flexible and efficiently manages permission by spreading various user characteristics to multiple authorization centers. Experimental results are tested for BCVS-DS by AVEC dataset. BCVS-DS achieves the highest classification accuracy of 94.32%.

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