5 citations · 10 across the 6 of their papers we have counts for
7 papers · 1 filter
Adaptive Learning for Service Monitoring Data
Farzana Anowar, Samira Sadaoui, Hardik Dalal
Service monitoring applications continuously produce data to monitor their availability. Hence, it is critical to classify incoming data in real-time and accurately. For this purpo…
Incremental Feature Learning For Infinite Data
Armin Sadreddin, Samira Sadaoui
This study addresses the actual behavior of the credit-card fraud detection environment where financial transactions containing sensitive data must not be amassed in an enormous am…
Optimizing Neural Network Weights using Nature-Inspired Algorithms
Wael Korani, Malek Mouhoub, Samira Sadaoui
This study aims to optimize Deep Feedforward Neural Networks (DFNNs) training using nature-inspired optimization algorithms, such as PSO, MTO, and its variant called MTOCL. We show…
Cost-sensitive Semi-supervised Classification for Fraud Applications
Sulaf Elshaar, Samira Sadaoui
This research explores Cost-Sensitive Learning (CSL) in the fraud detection domain to decrease the fraud class's incorrect predictions and increase its accuracy. Notably, we concen…
Building High-Quality Auction Fraud Dataset
Sulaf Elshaar, Samira Sadaoui
Given the magnitude of online auction transactions, it is difficult to safeguard consumers from dishonest sellers, such as shill bidders. To date, the application of Machine Learni…
Clustering and Labelling Auction Fraud Data
Ahmad Alzahrani, Samira Sadaoui
Although shill bidding is a common auction fraud, it is however very tough to detect. Due to the unavailability and lack of training data, in this study, we build a high-quality la…
Scraping and Preprocessing Commercial Auction Data for Fraud Classification
Ahmad Alzahrani, Samira Sadaoui
In the last three decades, we have seen a significant increase in trading goods and services through online auctions. However, this business created an attractive environment for m…