Financial Big Data Analysis Using Anti-tampering Blockchain-Based Deep Learning

Title

Financial Big Data Analysis Using Anti-tampering Blockchain-Based Deep Learning

Creator

Praghash K.; Yuvaraj N.; Peter G.; Stonier A.A.; Priya R.D.

Description

This study recommends using blockchains to track and verify data in financial service chains. The financial industry may increase its core competitiveness and value by using a deep learning-based blockchain network to improve financial transaction security and capital flow stability. Future trading processes will benefit from blockchain knowledge. In this paper, we develop a blockchain model with a deep learning framework to prevent tampering with distributed databases by considering the limitations of current supply-chain finance research methodologies. The proposed model had 90.2% accuracy, 89.6% precision, 91.8% recall, 90.5% F1 Score, and 29% MAPE. Choosing distributed data properties and minimizing the process can improve accuracy. Using code merging and monitoring encryption, critical blockchain data can be obtained. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Source

Lecture Notes in Networks and Systems, Vol-647 LNNS, pp. 1031-1040.

Date

2023-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Anti-Tampering Model; Blockchain; Deep Learning; Financial Big Data

Coverage

Praghash K., Department of Electronics and Communication Engineering, Christ University, Bengaluru, India; Yuvaraj N., Department of Research and Publications, ICT Academy, IIT Madras Research Park, ManagerChennai, India; Peter G., CRISD, School of Engineering and Technology, University of Technology Sarawak, Sibu, Malaysia; Stonier A.A., School of Electrical Engineering, Vellore Institute of Technology, Tamil Nadu, Vellore, India; Priya R.D., Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Tamil Nadu, Coimbatore, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-303127408-4

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-031-27409-1_95

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163328423&doi=10.1007%2f978-3-031-27409-1_95&partnerID=40&md5=b13127a4758a0b68493932107a635851