scholarly journals Detecting Credit Card Fraud using Data Mining Techniques - Meta-Learning

2015 ◽  
Vol 8 (28) ◽  
Author(s):  
T. Abdul Razak ◽  
G. Najeeb Ahmed
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Credit Card Fraud ◽  
Data Mining Techniques ◽  
Meta Learning ◽  
Using Data

scholarly journals Mobile Phishing Websites Detection and Prevention Using Data Mining Techniques

2019 ◽  
Vol 13 (10) ◽  
pp. 205
Author(s):  
Huda Yousif Kadhim ◽  
Karim Hashim Al-saedi ◽  
Mustafa Dhiaa Al-Hassani
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Prediction Accuracy ◽  
Detection System ◽  
Smart Phones ◽  
Data Mining Techniques ◽  
Data User ◽  
Using Data ◽  
Phishing Detection ◽  
System Effectiveness

<strong>Abstract</strong>— The widespread use of smart phones nowadays makes them vulnerable to phishing. Phishing is the process of trying to steal user information over the Internet by claiming they are a trusted entity and thus access and steal the victim's data(user name, password and credit card details). Consequently, the need for mobile phishing detection system has become an urgent need. And this is what we are attempting to introduce in this paper, where we introduce a system to detect phishing websites on Android phones. That predicts and prevents phishing websites from deceiving users, utilizing data mining techniques to predict whether a website is phishing or not, relying on a set of factors (URL based features, HTML based features and Domain based features). The results shows system effectiveness in predicting phishing websites with 97% as prediction accuracy.

scholarly journals Machine Learning Techniques for Credit Card Fraud Detection

2020 ◽  
Vol 4 (2) ◽  
pp. 98-112
Author(s):  
Hossam Eldin M. Abd Elhamid ◽  
◽  
Wael Khalif ◽  
Mohamed Roushdy ◽  
Abdel-Badeeh M. Salem ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Credit Card ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Credit Card Fraud ◽  
Data Mining Techniques ◽  
Learning Techniques ◽  
Over Time

The term “fraud”, it always concerned about credit card fraud in our minds. And after the significant increase in the transactions of credit card, the fraud of credit card increased extremely in last years. So the fraud detection should include surveillance of the spending attitude for the person/customer to the determination, avoidance, and detection of unwanted behavior. Because the credit card is the most payment predominant way for the online and regular purchasing, the credit card fraud raises highly. The Fraud detection is not only concerned with capturing of the fraudulent practices, but also, discover it as fast as they can, because the fraud costs millions of dollar business loss and it is rising over time, and that affects greatly the worldwide economy. . In this paper we introduce 14 different techniques of how data mining techniques can be successfully combined to obtain a high fraud coverage with a high or low false rate, the Advantage and The Disadvantages of every technique, and The Data Sets used in the researches by researcher

Literature Study –Data Mining Techniques on Detecting Fradulent Activities in Credit Card

2017 ◽  
Vol 6 (10) ◽  
pp. 60
Author(s):  
S. K. Saravanan ◽  
G. N. K. Suresh Babu
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Data Transfer ◽  
Study Data ◽  
Time Requirement ◽  
Literature Study ◽  
Data Mining Techniques ◽  
Online Transactions ◽  
Secured Data ◽  
Credit Card Usage

In contemporary days the more secured data transfer occurs almost through internet. At same duration the risk also augments in secure data transfer. Having the rise and also light progressiveness in e – commerce, the usage of credit card (CC) online transactions has been also dramatically augmenting. The CC (credit card) usage for a safety balance transfer has been a time requirement. Credit-card fraud finding is the most significant thing like fraudsters that are augmenting every day. The intention of this survey has been assaying regarding the issues associated with credit card deception behavior utilizing data-mining methodologies. Data mining has been a clear procedure which takes data like input and also proffers throughput in the models forms or patterns forms. This investigation is very beneficial for any credit card supplier for choosing a suitable solution for their issue and for the researchers for having a comprehensive assessment of the literature in this field.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Sign in / Sign up

Export Citation Format

Share Document