Welcome to Final Year Projects!!
  • Newsletter
  • +91 90254 34960
  • Contact Us
  • FAQs
Select category
  • Select category
  • Artificial Intelligence
  • Biomedical
  • Block Chain
  • Cloud Computing
  • Cyber Security
  • Data mining
  • Deep Learning
  • Embedded Components
  • Generative AI
  • IoT
  • LORA
  • Machine Learning
  • Mini Projects
    • Embedded
    • Java
    • Matlab
    • Python
    • VLSI
      • pipeline
  • Natural Language Processing
  • Projects
    • Embedded
      • Agriculture
      • Artificial Intelligence(AI)
      • Biomedical
      • Digital Twin
      • Image Processing
      • Internet of Things(IoT)
      • LoRaWAN
      • Raspberry PI
      • Robotics
      • Social Cause
    • Java
      • Android
      • Augmented Reality
      • Blockchain
      • Cloud Computing
      • Data Mining
      • Internet of Things (IoT)
      • Machine Learning
      • Secure Computing
    • Matlab
      • Cryptography- Authentication
      • Cyber Security
      • Deep Learning
      • Digital Image Processing
      • Machine Learning
      • Natural Language Processing
    • Python
      • Blockchain
      • Cybersecurity
      • Deep Learning
      • Explainable AI
      • Generative AI
      • GPT
      • Machine Learning
      • OpenCV
    • VLSI
      • Low Power VLSI Design
      • On-Chip Cryptography
      • Self Repairing Technology
  • Robotics
  • Secure Computing
Login / Register
0 Wishlist
0 Compare
0 items ₹0.00
Menu
0 items ₹0.00
Browse Categories
  • Java
  • Python
  • Embedded
  • Machine Learning
  • Mechanical
  • Matlab
  • VLSI
  • Raspberry PI
  • Artificial Intelligence
  • Home
  • Shop
    • PROJECTS
      • PROJECTS
        • Java
        • Python
        • Embedded
        • Matlab
        • VLSI
        • Mechanical
    • MINI PROJECTS
      • PROJECTS
        • Java
        • Python
        • Matlab
        • VLSI
        • Embedded
    • WORKSHOPS
      • Workshops
        • Python
        • Robotics
        • Industry Visit
        • Raspberry Pi
        • Image Processing
        • Mechanical Engineering
        • VLSI
        • Arduino
        • Matlab
        • Machine Learning
        • Embedded
        • Android
        • IoT
    • INTERNSHIPS
      • Internships
        • Python
        • Machine learning
        • Artificial intelligence
        • Web development
        • Android
        • IoT / internet of things
        • Cloud Computing
        • Digital Marketing
        • Big Data
  • Journal paper
  • Blog
  • About us
  • Contact us
Transfer Learning Strategies for Credit Card Fraud Detection
Click to enlarge
Home Projects Java Secure Computing Transfer Learning Strategies for Credit Card Fraud Detection
d-BAME Distributed Blockchain-Based Anonymous Mobile Electronic Voting
d-BAME Distributed Blockchain-Based Anonymous Mobile Electronic Voting ₹5,500.00
Back to products
Secure and Efficient Outsource k-Means Clustering
Secure and Efficient Outsourced k-Means Clustering using Fully Homomorphic Encryption With Ciphertext Packing Technique ₹5,500.00

Transfer Learning Strategies for Credit Card Fraud Detection

₹5,500.00

Compare
Add to wishlist
SKU: Java - Secure Computing Categories: Projects, Secure Computing, Secure Computing Tags: Credit Card, Fraud Detection, Secure Computing
Share:
  • Description
  • Reviews (0)
  • Software Download
  • Download Abstract
  • Shipping & Delivery
Description

Aim:

Ā Ā Ā Ā Ā Ā Ā  The main aim of the project is to identify the Credit card Fraud transaction that has been initiated by the any user and to avoid that type of transaction.

Synopsis:

Ā Ā Ā Ā Ā Ā Ā  The rapid increase of online transaction has given rise to a significant amount. However, the Internet environment is open, online shopping systems have bugs, and criminals can use some bad techniques such as Trojan and pseudo base-station. All these result in a serious increasing of credit card fraud events. When a criminal steals or cheats the information of the credit card of a cardholder, the criminal can use the credit card to consume. According to the Nilsson Report in October 2016, more than $31 trillion were generated worldwide by online payment systems in 2015, increasing 7.3% than 2014. Worldwide losses from credit card fraud rose to $21 billion in 2015, and will possibly reach $31 billion by 2020. Our proposed model will used in order to avoid these fraud transactions.

Existing System:

Ā Ā Ā  In Existing the transaction will be successful once if the user enters the correct pin pass of that particular card but there is a drawback as server will not identify the user and process the user transaction but that transaction might be done any false user as in online transaction there is lot of security issues. As there is advanced technology that user to hack the user credit card detail including user pin number.

Problem Definition:

  • It is very hard for server to identify the transaction done by authorized user or not
  • Server will only validate the user based on the user entered pin number.

Ā Challenges:

  • Maintain user transaction detail for each user.
  • Identify the Transaction based on the history

Proposed System:

Ā Ā Ā Ā  To Identify the Fraud transaction server need to maintain user transaction data like transaction processed location, merchant name, and amount of transaction that process based on these server will maintain the transaction data. During transaction the server will save the information like amount and location from where the transaction takes place. If a transaction takes place from a new location the server sends the information to credit card user for second level authentication. After second level authentication is done the transaction takes place so that we can avoid credit card fraud.

Reviews (0)

Reviews

There are no reviews yet.

Be the first to review “Transfer Learning Strategies for Credit Card Fraud Detection” Cancel reply

Your email address will not be published. Required fields are marked *


The reCAPTCHA verification period has expired. Please reload the page.

Software Download

You must be logged in to download the software.

Download Abstract

You must be logged in to download the abstract.

Shipping & Delivery
wd-ship-1
wd-ship-2

MAECENAS IACULIS

Vestibulum curae torquent diam diam commodo parturient penatibus nunc dui adipiscing convallis bulum parturient suspendisse parturient a.Parturient in parturient scelerisque nibh lectus quam a natoque adipiscing a vestibulum hendrerit et pharetra fames nunc natoqueĀ dui.

ADIPISCING CONVALLIS BULUM

  • Vestibulum penatibus nunc dui adipiscing convallis bulum parturient suspendisse.
  • Abitur parturient praesent lectus quam a natoque adipiscing a vestibulum hendre.
  • Diam parturient dictumst parturient scelerisque nibh lectus.

Scelerisque adipiscing bibendum sem vestibulum et in a a a purus lectus faucibus lobortis tincidunt purus lectus nisl class eros.Condimentum a et ullamcorper dictumst mus et tristique elementum nam inceptos hac parturient scelerisqueĀ vestibulum amet elit ut volutpat.

Related products

A VANET-IoT based Accident Detection and
Compare

A vanet-iot based accident detection and Management system for the emergency rescue

Projects, Embedded, Internet of Things(IoT), IoT
₹10,000.00
Aim: Ā Ā Ā Ā Ā Ā Ā Ā Ā  The aim of this project is biometric sensor based accident speed dial system for te emergency Rescue service
Add to wishlist
Add to cart
Quick view
Compare

Cryptographic Requirements of Verifiable Credentials for Digital Identification Documents

Projects, Java, Secure Computing
₹5,500.00
Aim: Ā Ā Ā Ā Ā  Ā Ā Ā  The aim of this paper is to define stringent cryptographic requirements for government-issued digital IDs and provide
Add to wishlist
Add to cart
Quick view
Compare

Deep Learning Model for Driver Behavior Detection in Cyber-Physical System-Based Intelligent Transport Systems

Projects, Python, Deep Learning, Artificial Intelligence, Deep Learning
₹5,500.00
Aim: To develop a real-time system for detecting and alerting drowsiness in drivers using YOLOv8 object detection.
Add to wishlist
Add to cart
Quick view
Compare

Efficient Machine Learning Approach For Crime Detection In India

Python, Machine Learning, Projects, Machine Learning
₹5,500.00
The goal of this project is to create a reliable model for predicting droughts in regions that are vulnerable to them. Using Indian rainfall data, the project applies ARIMA and SARIMAX models to forecast droughts. The project aims to support better planning and response strategies, helping communities prepare for and mitigate the effects of droughts.
Add to wishlist
Add to cart
Quick view
Compare

Object Detection Method Using Image and Number of Objects on Image as Label

Projects, Python, Deep Learning, Deep Learning
₹5,500.00
To develop an object detection model using YOLOv8 to address the limitations of existing methods and improve detection accuracy, robustness, and efficiency. The aim is to design a system that reduces the dependency on extensive labelling while ensuring adaptability to unseen environments. The model will utilize YOLOv8’s capabilities to process data efficiently and deliver high-performance results for diverse applications in object detection.
Add to wishlist
Add to cart
Quick view
Compare

Predicting Heart Diseases Using Machine Learning and Different Data Classification Techniques

Python, Machine Learning, Projects, Machine Learning
₹5,500.00
Aim: This study develops a machine learning model to classify heart disease into different severity levels. It analyzes patient data to improve diagnostic accuracy and support medical decisions.
Add to wishlist
Add to cart
Quick view
Compare

Real Time Alert System based on Crime Area Mapping

Projects, Java, Android
₹5,500.00
Aim:Ā  The objective of this system is to provide real-time alerts for users based on crime-prone areas, allowing citizens to report complaints and enabling police verification through an integrated mapping and notification system.
Add to wishlist
Add to cart
Quick view
Compare

Whale and Dolphin Classification

Projects, Python, Deep Learning, Deep Learning
₹5,500.00
The proposed method involves a multi-step process to classify whale and dolphin species from images. First, the dataset is collected and pre-processed to ensure high-quality input data. The VGG16 model is used to extract features from the images, capturing complex patterns and details. These features are then used to train a Support Vector Machine (SVM) model, which excels in binary and multi-class classification tasks.
Add to wishlist
Add to cart
Quick view

    Global Techno Solutions - GTS, started by young engineering graduates to overcome a problem they faced during their academic years. That is "Providing Solutions". They kept it as the motto for their company.

    • Phone: (+91) 90254 34960
    • Mail: sales@finalyearprojects.in
    Our Category
    • Java
    • Python
    • Embedded
    • Matlab
    • VLSI
    • Mechanical
    USEFUL LINKS
    • Privacy Policy
    • Returns
    • Terms & Conditions
    • Contact Us
    • Latest News
    • FAQ
    Mini Projects
    • Java
    • Python
    • Embedded
    • Matlab
    • VLSI
    Copyright Finalyearprojects.In 2024
    payments
    • Menu
    • Categories
    • Java
    • Python
    • Embedded
    • Machine Learning
    • Mechanical
    • Matlab
    • VLSI
    • Raspberry PI
    • Artificial Intelligence
    • Home
    • Shop
    • Blog
    • About us
    • Contact us
    • Wishlist
    • Compare
    • Login / Register
    Shopping cart
    Close
    Sign in
    Close

    Lost your password?

    OR
    Don't have an account? Signup

    No account yet?

    Create an Account

    HEY YOU, SIGN UP AND CONNECT TO GLOBAL TECHNO SOLUTIONS

    Be the first to learn about our latest trends and get exclusive offers

    Will be used in accordance with ourĀ Privacy Policy

    Shop
    0 Wishlist
    0 items Cart
    My account

    Back