• Home
  • Browse
    • Current Issue
    • By Issue
    • By Author
    • By Subject
    • Author Index
    • Keyword Index
  • Journal Info
    • About Journal
    • Aims and Scope
    • Editorial Board
    • Editorial Staff
    • Publication Ethics
    • Indexing and Abstracting
    • Related Links
    • FAQ
    • Peer Review Process
    • News
  • Guide for Authors
  • Submit Manuscript
  • Reviewers
  • Contact Us
 
  • Login
  • Register
Home Articles List Article Information
  • Save Records
  • |
  • Printable Version
  • |
  • Recommend
  • |
  • How to cite Export to
    RIS EndNote BibTeX APA MLA Harvard Vancouver
  • |
  • Share Share
    CiteULike Mendeley Facebook Google LinkedIn Twitter
Journal of Computer & Robotics
arrow Articles in Press
arrow Current Issue
Journal Archive
Volume Volume 12 (2019)
Volume Volume 11 (2018)
Volume Volume 10 (2017)
Issue Issue 2
Issue Issue 1
Volume Volume 9 (2016)
Volume Volume 8 (2015)
Volume Volume 7 (2014)
Volume Volume 6 (2013)
Volume Volume 5 (2012)
Volume Volume 4 (2011)
Volume Volume 3 (2010)
Volume Volume 1 (2008)
Ghodsi, M., Saniee Abadeh, M. (2017). Fraud Detection of Credit Cards Using Neuro-fuzzy Approach Based on TLBO and PSO Algorithms. Journal of Computer & Robotics, 10(2), 57-68.
Maryam Ghodsi; Mohammad Saniee Abadeh. "Fraud Detection of Credit Cards Using Neuro-fuzzy Approach Based on TLBO and PSO Algorithms". Journal of Computer & Robotics, 10, 2, 2017, 57-68.
Ghodsi, M., Saniee Abadeh, M. (2017). 'Fraud Detection of Credit Cards Using Neuro-fuzzy Approach Based on TLBO and PSO Algorithms', Journal of Computer & Robotics, 10(2), pp. 57-68.
Ghodsi, M., Saniee Abadeh, M. Fraud Detection of Credit Cards Using Neuro-fuzzy Approach Based on TLBO and PSO Algorithms. Journal of Computer & Robotics, 2017; 10(2): 57-68.

Fraud Detection of Credit Cards Using Neuro-fuzzy Approach Based on TLBO and PSO Algorithms

Article 6, Volume 10, Issue 2, Summer and Autumn 2017, Page 57-68  XML PDF (545 K)
Document Type: Original Research (Full Papers)
Authors
Maryam Ghodsi1; Mohammad Saniee Abadeh* 2
1Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2Faculty of Computer and Electrical Engineering, Tarbiyat Modarres University, Tehran, Iran
Receive Date: 14 September 2016,  Revise Date: 05 November 2016,  Accept Date: 18 January 2017 
Abstract
The aim of this paper is to detect bank credit cards related frauds. The large amount of data and their similarity lead to a time consuming and low accurate separation of healthy and unhealthy samples behavior, by using traditional classifications. Therefore in this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used in order to reach a more efficient and accurate algorithm. By combining evolutionary algorithms with ANFIS, the optimal tuning of ANFIS parameters is achieved by the Teaching-Learning-Based Optimization (TLBO) and the Particle Swarm Optimization (PSO). The aim of using this approach is to improve the network performance and to reduce calculation complexities compared to gradient descent and least square methods. The proposed algorithm is implemented and evaluated on credit cards data to detect fraud. The results demonstrate superior performance of the designed scheme compared to other intelligent identification methods.
Keywords
Credit Cards Fraud Detection; Teaching-Learning-Based Optimization (TLBO); Adaptive Neuro-Fuzzy Inference System (ANFIS); Particle Swarm Optimization (PSO)
Statistics
Article View: 389
PDF Download: 502
Home | Glossary | News | Aims and Scope | Sitemap
Top Top

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Journal Management System. Designed by sinaweb.