Feature selection plays an important role in machine learning in order to reduce model
complexity and extract more meaningful information. The recent studies indicate that not …

Goal: To establish Pulse2AI as a reproducible data preprocessing framework for pulsatile
signals that generate high-quality machine-learning-ready datasets from raw wearable …

Advancement in Artificial Intelligence (AI) aims to train the Machine Learning (ML) Models in
such a way that they would be able to take decisions spontaneously, however on the other …

In this research, we focus on investigating the effectiveness of different adversarial attacks
and robustness of deep learning-based Intrusion detection using different Neural networks …

Recently, many adversarial examples have emerged for Deep Neural Networks (DNNs)
causing misclassifications. However, indepth work still needs to be performed to …

Machine learning (ML) has emerged as the principal tool for performing complex tasks
which are impractical (if not impossible) to code by humans. ML techniques provide …

Due to the advancements in technology and microelectromechanical systems, there is an
exceptional development in the capabilities of sensors and smart devices. Nowadays …

One or more hardened machine learning models are secured against adversarial attacks by
adding adversarial protection to one or more previously trained machine learning models …

We present a novel approach to developing robust AI in light of context-varying situations.
This methodology harnesses a suite of indicators to establish a Robustness Assurance …

Convolutional Neural Networks have proven to be the state of the art approach for doing
image processing in the field of Deep Learning. CNN has proven to be particularly …