A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

₹5,500.00
Product Code: Python - Cybersecurity
Availability: In Stock
Viewed 816 times

Product Description

Aim:

           People can use credit cards for online transactions as it provides an efficient and easy-to-use facility.  With the increase in usage of credit cards, the capacity of credit card misuse has also enhanced. Credit card frauds cause significant financial losses for both credit card holders and financial companies. The main aim is to detect fraudulent transactions using credit cards with the help of ML algorithms and deep learning algorithms.


Abstract:

            Credit card fraud remains a pervasive issue, leading to substantial financial losses for both financial institutions and cardholders. To combat this threat effectively, this study presents a novel approach to credit card fraud detection using a deep learning ensemble coupled with data resampling techniques. The proposed system combines multiple deep learning models to enhance the classification of fraudulent transactions, while employing resampling methods to address the class imbalance prevalent in credit card transaction data. Through extensive experimentation and evaluation on diverse datasets, our ensemble demonstrates notable improvements in fraud detection accuracy, outperforming single-model approaches and conventional sampling techniques. The results reveal the system's robustness in identifying fraudulent activities while minimizing false alarms, providing a valuable tool for financial security and risk mitigation in today's digital transaction landscape.


Proposed System:

           Deep learning (DL) algorithms applied applications in computer network, intrusion detection, banking, insurance, mobile cellular networks, health care fraud detection, medical and malware detection, detection for video surveillance, location tracking, Android malware detection, home automation, and heart disease prediction. we explore DL Algorithms to identify credit card thefts in the banking industry in this model. It uses a number of deep learning algorithms for detecting CCF. However, in this model, we choose the CNN model and its layers to determine if the original fraud is the normal transaction of qualified datasets.


When you order from finalyearprojects.in, you will receive a confirmation email. Once your order is shipped, you will be emailed the tracking information for your order's shipment. You can choose your preferred shipping method on the Order Information page during the checkout process.

The total time it takes to receive your order is shown below:

The total delivery time is calculated from the time your order is placed until the time it is delivered to you. Total delivery time is broken down into processing time and shipping time.

Processing time: The time it takes to prepare your item(s) to ship from our warehouse. This includes preparing your items, performing quality checks, and packing for shipment.

Shipping time: The time for your item(s) to tarvel from our warehouse to your destination.

Shipping from your local warehouse is significantly faster. Some charges may apply.

In addition, the transit time depends on where you're located and where your package comes from. If you want to know more information, please contact the customer service. We will settle your problem as soon as possible. Enjoy shopping!

Download Abstract

Click the below button to download the abstract.

Package Includes

Software Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support


The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days

 

Hardware Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


The Delivery time for Hardware projects is 7-8 working days.

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

The Delivery time for software Miniprojects is 2 -3 working days.

 

Mini Projects - Hardware includes

  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.