An Ensemble Of Machine And Deep Learning Models For Real Time Credit Card Scam Recognition
Created by W.Langdon from
gp-bibliography.bib Revision:1.7656
- @InProceedings{Goyal:2023:ICCCI,
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author = "Khushi Goyal and Shaurya Singh and Muskan Gulati and
A. Suresh",
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booktitle = "2023 International Conference on Computer
Communication and Informatics (ICCCI)",
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title = "An Ensemble Of Machine And Deep Learning Models For
Real Time Credit Card Scam Recognition",
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year = "2023",
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abstract = "As the E-commerce sector is getting large, the use of
electronic money and is getting wider and wider. Credit
cards are the most useful and easy tools for payment.
It is easy to use and reduces the efforts made by
humans. But with advantages some disadvantages also
come hand in hand. Many frauds take place while making
the transactions and due to this many people lose
millions of money. Hence, there need to be a detection
system so that people can make the transactions without
the fear of frauds. In today's time there are many
technologies which can help in making such a system.
Some technologies are 'Neural Network, Artificial
Intelligence, Bayesian Network, Data mining, Artificial
Immune System, K-nearest neighbour algorithm, Decision
Tree, Fuzzy Logic Based System, Support Vector Machine,
Machine learning, Genetic Programming etc'. This paper
will include many surveys which will be conducted in
which people will use different techniques to make a
strong system. The work will also be aiming at making a
strong detection system using libraries like numpy,
sklearn and other py libraries. The problem is solved
by using a classifier which can differentiate between
fraud and legit transactions based on the class and
time. The dataset contains 31 columns among which 28
columns are named as v1, v2, v3a. Due to security
purposes, 2 columns are time and amount [1]. The total
amount of transactions were 283.806 with only 492 fraud
cases and rest legit transactions. In today's time
there are credit cards in the market for kids who are
under 18 as well. Therefore it is important for a
system to be developed for safety. Fraudsters can use
the money for many illegal practices as well. This
paper will use Random Forest Classifier and Decision
tree to test the dataset [2]. The dataset is of some
card holders from Europe.",
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keywords = "genetic algorithms, genetic programming, Surveys,
Support vector machines, SVM, Credit cards, Libraries,
Real-time systems, Fraud, Systems support, Machine
Leaning, Deep Learning, Credit Card, Neural Network,
ANN, E-commerce, Online shopping",
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DOI = "doi:10.1109/ICCCI56745.2023.10128473",
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ISSN = "2473-7577",
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month = jan,
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notes = "Also known as \cite{10128473}",
- }
Genetic Programming entries for
Khushi Goyal
Shaurya Singh
Muskan Gulati
A Suresh
Citations