Cite this DOI

Mani, D., & Tirupathamma, M. (2019). DETECTION OF CREDIT CARD FRAUD WITH RANDOM FOREST ALOGIRITHAM. *Journal of Science & Technology*, *04*(06), 37–41. https://doi.org/10.46243/jstj.2019.v4.i6.88

@article{mani2019detection,
  author    = {Mani, Dr.D.Sharada and Tirupathamma, Mrs.M.Lakshmi},
  title     = {{DETECTION OF CREDIT CARD FRAUD WITH RANDOM FOREST ALOGIRITHAM}},
  journal   = {Journal of Science \& Technology},
  year      = {2019},
  month     = {nov},
  volume    = {04},
  number    = {06},
  pages     = {37--41},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jstj.2019.v4.i6.88},
  url       = {https://doi.org/10.46243/jstj.2019.v4.i6.88},
  language  = {en},
  abstract  = {Real-world credit card fraud detection is the primary emphasis of the project. Credit card fraud has lately increased dramatically as a result of the amazing surge in the number of transactions. The goal is to get something without paying for it or to get money out of a bank account without authorization. All credit card issuers must now have effective fraud detection systems in order to reduce their losses. Making the business is a major difficulty since no cardholder or card must be present for a transaction to be completed.. Merchants are unable to determine if a consumer presenting their card is in fact the legitimate owner. It is possible to increase the accuracy of fraud detection by using the suggested technique, which makes use of a random forest. The random forest technique is used to analyse a data set and the current dataset of the user. Finally, improve the precision of the output data. The accuracy, sensitivity, specificity, and precision of the procedures are assessed. Processed characteristics are used to identify fraud, and a graphical model depiction is presented. The accuracy, sensitivity, specificity, and precision of the procedures are assessed.}
}

TY  - JOUR
TI  - DETECTION OF CREDIT CARD FRAUD WITH RANDOM FOREST ALOGIRITHAM
AU  - Mani, Dr.D.Sharada
AU  - Tirupathamma, Mrs.M.Lakshmi
JO  - Journal of Science & Technology
PY  - 2019
DA  - 2019/11/01/
VL  - 04
IS  - 06
SP  - 37
EP  - 41
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Real-world credit card fraud detection is the primary emphasis of the project. Credit card fraud has lately increased dramatically as a result of the amazing surge in the number of transactions. The goal is to get something without paying for it or to get money out of a bank account without authorization. All credit card issuers must now have effective fraud detection systems in order to reduce their losses. Making the business is a major difficulty since no cardholder or card must be present for a transaction to be completed.. Merchants are unable to determine if a consumer presenting their card is in fact the legitimate owner. It is possible to increase the accuracy of fraud detection by using the suggested technique, which makes use of a random forest. The random forest technique is used to analyse a data set and the current dataset of the user. Finally, improve the precision of the output data. The accuracy, sensitivity, specificity, and precision of the procedures are assessed. Processed characteristics are used to identify fraud, and a graphical model depiction is presented. The accuracy, sensitivity, specificity, and precision of the procedures are assessed.
DO  - 10.46243/jstj.2019.v4.i6.88
UR  - https://doi.org/10.46243/jstj.2019.v4.i6.88
ER  -

{
    "type": "article-journal",
    "id": "10.46243/jstj.2019.v4.i6.88",
    "DOI": "10.46243/jstj.2019.v4.i6.88",
    "URL": "https://doi.org/10.46243/jstj.2019.v4.i6.88",
    "title": "DETECTION OF CREDIT CARD FRAUD WITH RANDOM FOREST ALOGIRITHAM",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
        {
            "family": "Mani",
            "given": "Dr.D.Sharada"
        },
        {
            "family": "Tirupathamma",
            "given": "Mrs.M.Lakshmi"
        }
    ],
    "issued": {
        "date-parts": [
            [
                2019,
                11,
                1
            ]
        ]
    },
    "volume": "04",
    "issue": "06",
    "page": "37-41",
    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "Real-world credit card fraud detection is the primary emphasis of the project. Credit card fraud has lately increased dramatically as a result of the amazing surge in the number of transactions. The goal is to get something without paying for it or to get money out of a bank account without authorization. All credit card issuers must now have effective fraud detection systems in order to reduce their losses. Making the business is a major difficulty since no cardholder or card must be present for a transaction to be completed.. Merchants are unable to determine if a consumer presenting their card is in fact the legitimate owner. It is possible to increase the accuracy of fraud detection by using the suggested technique, which makes use of a random forest. The random forest technique is used to analyse a data set and the current dataset of the user. Finally, improve the precision of the output data. The accuracy, sensitivity, specificity, and precision of the procedures are assessed. Processed characteristics are used to identify fraud, and a graphical model depiction is presented. The accuracy, sensitivity, specificity, and precision of the procedures are assessed.",
    "ISSN": "2456-5660"
}

From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjstj.2019.v4.i6.88 gives all four in one JSON answer.