Neural Association with Multi Access Forensic Dashboard as Service (NAMAFDS)

T. Manikanta Vital, V. Lavanya () and P. Savaridassan ()
Additional contact information
T. Manikanta Vital: SRM Institute of Science and Technology, Department of Information Technology
V. Lavanya: SRM Institute of Science and Technology, Department of Information Technology
P. Savaridassan: SRM Institute of Science and Technology, Department of Information Technology

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 1051-1060 from Springer

Abstract: Abstract Cloud-Forensic-as-Service is the one which common person’s daily accessible service which is centrally reposted with all the criminal’s forensic data in cloud environments. The continuous updating with all the records as faces with proper trained infrastructure with exact mean and variance values which will be mounted as digitized data with novel method of accommodation where client can use to cross check the forensic analysis in higher accuracy result with finite precession. The neural association algorithm will be used in the SaaS (Software as Service) where the logic is maintained with dynamic self-updating of the digitized data with deep learning model and anonymous clients concurrently can access this service over the cloud. This forensic cloud will not be accessed by all common persons and also lack of awareness where to check this type forensic check up with easy user friendly support. So to address this problem this work proposed a new framework NAMAFDS (Neural Association with Multi Access Forensic Dashboard as Service) which will give continuous rendering services to clients who wants instant criminal forensic check-up which is globally indicated as Cloud-Forensic-as-Service.

Keywords: Neural; Forensic; Service; Threshold; Encryption; Repository; Evidence (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_107

Ordering information: This item can be ordered from
http://www.springer.com/9783030418625

DOI: 10.1007/978-3-030-41862-5_107

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().