Enhancing credit card fraud detection in financial transactions through improved random forest algorithm (Record no. 22739)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250428150016.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250428b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 26023 |
| Author | Sowmiya, B. |
| 245 ## - TITLE STATEMENT | |
| Title | Enhancing credit card fraud detection in financial transactions through improved random forest algorithm |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.14(1), Oct |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | ICT Academy |
| Year | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 3089-3093p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Credit card Fraud detection is a critical task in various industries,<br/>including finance and e-commerce, where identifying fraudulent<br/>activities can help prevent financial losses and protect users. It begins<br/>by combining two datasets containing fraudulent and non-fraudulent<br/>transactions to create a comprehensive dataset for analysis. Data is<br/>preprocessed by removing unnecessary features, calculating distance<br/>metrics, and generating new variables to capture temporal patterns and<br/>transaction history. Multicollinearity issues are addressed through<br/>feature selection. Improved Random Forest (RF) algorithm is used to<br/>improve fraud detection. The experimental results indicate that the<br/>improved Random Forest algorithm achieves commendable accuracy<br/>in fraud detection. The proposed model achieves 99.87% training<br/>accuracy and 99.41% testing accuracy. The Model’s performance is<br/>evaluated by measuring precision, recall, F1-score and support. Our<br/>research emphasizes the importance of considering improved<br/>algorithms to achieve better results. The findings provide valuable<br/>insights for organizations aiming to enhance their fraud detection<br/>capabil.. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 773 0# - HOST ITEM ENTRY | |
| Title | ICTACT Journal on Soft Computing (IJSC) |
| Place, publisher, and date of publication | Chennai ICT Academy |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://ictactjournals.in/paper/1_IJSC_Vol_14_Iss_1_Paper_1_3089_3093.pdf |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
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| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 28/04/2025 | 2025-0712 | 28/04/2025 | 28/04/2025 | Articles Abstract Database |