Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

Accepted: 18/Apr/2019, Published: 30/Apr/2019 Abstract—Today technology is increasing at
very rapid pace, which can be used for good as well as for bad purposes. So with this …

Unsupervised Learning is widely used approach for outlier detection because non-
availability of training dataset in various domains (especially, in evolving domains) …

This paper addresses the problem of the unsupervised approach of credit card fraud
detection in unbalanced datasets using the ARIMA model. The ARIMA model is fitted to the …

Outlier detection (OD) is a key problem, for which numerous solutions have been proposed.
To deal with the difficulties associated with outlier detection across various domains and …

Outliers are the data points which deviate significantly from the majority of the data points.
Finding outliers is an important task in various applications, especially in data mining. The …

The invention of web 2.0 and the advancement of information and communication
technology have provided consumers and businesses with the opportunity to utilise online …

Fraud identification is a crucial issue facing large economic institutions, which has caused
due to the rise in credit card payments. This paper brings a new approach for the predictive …

Losses incurred due to fraud on e-commerce transactions, especially those based on credit
cards, continue to increase, resulting in large losses each year. One mechanism to minimize …

Outlier detection is the task responsible for finding novel or rare phenomena that provide
valuable insights in many areas of the industry. The neighborhood-based algorithms are …