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Journal of Interdisciplinary Mathematics cover

Journal of Interdisciplinary Mathematics

(Since 1998)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-012X·ISSN (Print): 0972-0502
CiteScore 2025: 1.9 (Q2)

CiteScore 2025: 1.9 (Q2)

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Monthly Journal: Publishes the methodological and theoretical role of mathematics and mathematical applications underpinning scientific research.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Journal of Interdisciplinary Mathematics
Open Access Research Article

Utilizing mathematical concepts of heat map for an intelligent and secure approach to efficiently detect credit card fraud

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pp. 1837–1854Vol. 26Issue 8December 2023DOI: 10.47974/JIM-1761XML
Received:
07 Sep 2023
Published Online:
29 Dec 2023
Article type:
Research Article
Language:
EN
Article no.:
JIM-1761
Pages:
1837–1854

Abstract

In the current scenario of digital world, every year the financial institutions have to face billions of dollars losses due to fraudulent transactions. Out of different categories of financial frauds credit card transaction fraud is the most common. To reduce the effect of these fraud transactions there is a need for a well-designed and secured fraud detection system with a state of art fraud detection model. Our work’s primary contribution is the creation of a fraud detection system that makes use of some mathematical usage of creating heat maps which is then enhanced with the use of a deep learning architecture and a sophisticated feature engineering method based on HCNN- Heat Map Convolutional Neural Network. HCNN is a model which create the heat maps for the imbalance data set without replicating the minor class records and without discarding major class records. The experimental findings show that our suggested technique is a practical and successful mechanism for detecting credit card fraud. The main objective of our model is to develop such a technique that can be used to detect and correctly classify more numbers of fraud transactions thus, our suggested technique, may detect considerably more fraudulent transactions than the benchmark methods with the accuracy of 91.7%.

Keywords

Machine learningCredit card fraudHeat mapFinancial fraudMathematical foundations for heat maps

Subject Classifications

68T07

References

[1] Pleil, J. D., Stiegel, M. A., Madden, M. C., & Sobus, J. R., Heat map visualization of complex environmental and biomarker measurements. Chemosphere, 84(5), 716-723 (2011). 
[2] The History of the Cluster Heat Map Leland Wilkinsona & Michael Friendly.
[3] Zhao, S., Guo, Y., Sheng, Q., & Shyr, Y., Advanced heat map and clustering analysis using heatmap3. BioMed research international, (2014).
[4] Netek, R., Pour, T., & Slezakova, R., Implementation of heat maps in geographical information system–exploratory study on traffic accident data. Open Geosciences, 10(1), 367-384 (2018).
[5] Chu, H., Lin, W., Wu, J., Zhou, X., Chen, Y., & Li, H., A new heat-map-based algorithm for human group activity recognition. In Proceedings of the 20th ACM international conference on Multimedia, pp. 1069-1072 (2012, October).
[6] Shcheglova, S. N., & Shcheglov, B. O. Mathematical Algorithm for Creating 3D Models of Heat Maps as a Biophysical Method for Assessing Environmental and Economic Fire Damage in the Far Eastern Federal District of Russia.
[7] Forough, J., & Momtazi, S., Ensemble of deep sequential models for credit card fraud detection. Applied Soft Computing, 99, 106883 (2021).
[8] Benchaji, I., Douzi, S., El Ouahidi, B., & Jaafari, J., Enhanced credit card fraud detection based on attention mechanism and LSTM deep model. Journal of Big Data, 8, 1-21 (2021).
[9] Gupta, A., Lohani, M. C., & Manchanda, M., Financial fraud detection using naive bayes algorithm in highly imbalance data set. Journal of Discrete Mathematical Sciences and Cryptography, 24(5), 1559-1572 (2021).
[10] Itoo, F., Meenakshi, & Singh, S., Comparison and analysis of logistic regression, Naïve Bayes and KNN machine learning algorithms for credit card fraud detection. International Journal of Information Technology, 13, 1503-1511 (2021).
[11] Taha, A. A., & Malebary, S. J., An intelligent approach to credit card fraud detection using an optimized light gradient boosting machine. IEEE Access, 8, 25579-25587 (2020).
[12] Vats, S., Singh, S., Kala, G., Tarar, R., & Dhawan, S., iDoc-X: An artificial intelligence model for tuberculosis diagnosis and localization. Journal of Discrete Mathematical Sciences and Cryptography, 24(5), 1257-1272 (2021). 
[13] Vats, S., Sagar, B. B., Singh, K., Ahmadian, A., & Pansera, B. A., Performance evaluation of an independent time optimized infrastructure for big data analytics that maintains symmetry. Symmetry, 12(8), 1274 (2020).
[14] Vats, S., & Sagar, B. B., An independent time optimized hybrid infrastructure for big data analytics. Modern Physics Letters B, 34(28), 2050311 (2020).
[15] Vats, S. A. T. V. I. K., Dubey, S. K., & Pandey, N. K., Genetic algorithms for credit card fraud detection. In International Conference on Education and Educational Technologies (2013, July).
[16] Lin, C. T., & Lee, I. F., Building decision analysis model by the experts knowledge–applying to management fraud detection. Journal of Information and Optimization Sciences, 31(3), 533-542 (2010).
[17] Singh, A., & Jain, A., Cost-sensitive metaheuristic technique for credit card fraud detection. Journal of Information and Optimization Sciences, 41(6), 1319-1331 (2020).
[18] Kumar, A., Singh, K., & Khan, T., L-RTAM: Logarithm based reliable trust assessment model for WBSNs. Journal of Discrete Mathematical Sciences and Cryptography, 24(6), 1701-1716 (2021).
[19] Khan, T., & Singh, K., Resource management based secure trust model for WSN. Journal of Discrete Mathematical Sciences and Cryptography, 22(8), 1453-1462 (2019).
[20] Shariq, M., Singh, K., Lal, C., Conti, M., & Khan, T., ESRAS: An efficient and secure ultra-lightweight RFID authentication scheme for low-cost tags. Computer Networks, 217, 109360 (2022).
[21] Kumar, A., Singh, K., Khan, T., Ahmadian, A., Saad, M. H. M., & Manjul, M., ETAS: An efficient trust assessment scheme for BANs. IEEE Access, 9, 83214-83233 (2021).
[22] Khan, T., Singh, K., & Purohit, K. C., ICMA: An efficient integrated congestion control approach. Recent Patents on Engineering, 14(3), 294-309 (2020).
[23] Khan, T., Singh, K., Shariq, M., Ahmad, K., Savita, K. S., Ahmadian, A., ... & Conti, M,. An efficient trust-based decision-making approach for WSNs: Machine learning oriented approach. Computer Communications, 209, 217-229 (2023).
[24] Khan, Tayyab, and Karan Singh. “RTM: Realistic Weight-Based Reliable Trust Model for Large Scale WSNs.” Wireless Personal Communications 129, no. 2 : 953-991 (2023).
[25] Singh, A., & Jain, A., Hybrid bio-inspired model for fraud detection with correlation-based feature selection. Journal of Discrete Mathematical Sciences and Cryptography, 24(5), 1365-1374 (2021).

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