Search Results for author:
Found 4 papers, 2 papers with code
Detecting Users Prone to Spread Fake News on Arabic Twitter
We construct an Arabic dataset on Twitter users, which consists of 1, 546 users, of which 541 are prone to spread fake news (based on our definition).
Incremental Online Learning of Randomized Neural Network with Forward Regularization
To alleviate these drawbacks and achieve progressive immediate decision-making, we propose a novel Incremental Online Learning (IOL) process of Randomized Neural Networks (Randomized NN), a framework facilitating continuous improvements to Randomized NN performance in restrictive online scenarios.
A Blackbox Model Is All You Need to Breach Privacy: Smart Grid Forecasting Models as a Use Case
This paper investigates the potential privacy risks associated with forecasting models, with specific emphasis on their application in the context of smart grids.
Audio-based drone detection and identification using deep learning techniques with dataset enhancement through generative adversarial networks
In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network.
