The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Metadata Harvesting (OAI2)
  • Digital Archiving Policy

IJACSA

  • About the Journal
  • Call for Papers
  • Author Guidelines
  • Fees/ APC
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Editors
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving
  • Editorial Board

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors

Computing Conference 2021

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys) 2021

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC) 2021

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future of Information and Communication Conference (FICC) 2021

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Guidelines
  • Fees
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Editors
  • Reviewers
  • Subscribe

Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Population based Optimized and Condensed Fuzzy Deep Belief Network for Credit Card Fraudulent Detection

Author 1: Jisha M. V
Author 2: D. Vimal Kumar

Download PDF

Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110970

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 9, 2020.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: In this information era, with the advancement in technology, there is a high risk due to financial fraud which is a continually increasing menace during online transactions. Credit card fraudulent identification is a toughest challenge because of two important issues, as the profile of the credit card user’s behavior changes constantly and credit card datasets are skewed. The factors which greatly affects the credit card fraudulent transaction detection are primarily based on data sampling models, features involved in feature selection and detection approaches implied. To overwhelm these issues, instead of using certainty theory, this paper encapsulates with three different empowered models are deployed for intellectual way of fraudulent transaction detection. In this work uncertainty theory of intuitionistic fuzzy theorem to determine the significant features which will influence the detection process effectively. Maximized relevancy among dependent and independent features of credit card dataset are determined using grade of membership and non-membership information of each features. The intuitionistic fuzzy mutual information with the knowledge of entropy it selects the features with highest information score as significant feature subset. This proposed model devised Fuzzy Deep Belief Network enriched with Sea Turtle Foraging for credit card fraudulent detection (EFDBN-STFA). The fuzzy deep belief network greatly handles the complex pattern of credit card transactions with its deep knowledge and stacked restricted Boltzmann machine the pattern of dataset is analyzed. The weights assigned to the hidden nodes are fine-tuned by the sea turtle foraging using its fitness measure and thus it improves the detection accuracy of the FDBN. Simulation results proved the efficacy of EFDBN-STFA on two different credit card datasets with its gained ability of handling hesitation factor and optimization using metaheuristic approach, it achieves higher detection rate with reduced false alarms compared to other existing detection models.

Keywords: Credit card fraudulent; uncertainty; intuitionistic fuzzy; fuzzy deep belief network; sea turtle foraging

Jisha M. V and D. Vimal Kumar, “Population based Optimized and Condensed Fuzzy Deep Belief Network for Credit Card Fraudulent Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 11(9), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110970

@article{V2020,
title = {Population based Optimized and Condensed Fuzzy Deep Belief Network for Credit Card Fraudulent Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110970},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110970},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {9},
author = {Jisha M. V and D. Vimal Kumar}
}


IJACSA

Upcoming Conferences

Future of Information and Communication Conference (FICC) 2021

29-30 April 2021

  • Virtual

Computing Conference 2021

15-16 July 2021

  • London, United Kingdom

IntelliSys 2021

2-3 September 2021

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2021

28-29 October 2021

  • Vancouver, Canada
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© 2018 The Science and Information (SAI) Organization Limited. Registered in England and Wales. Company Number 8933205. All rights reserved. thesai.org