Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …

Currently, machine learning (ML) techniques are at the heart of smart cyber-physical
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …

Machine learning, in particular deep learning, is being used in almost all the aspects of life
to facilitate humans, specifically in mobile and Internet of Things (IoT)-based applications …

In the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to
their unmatchable performance in several applications, such as image processing, computer …

The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …

Deep Learning (DL) algorithms have gained popularity owing to their practical problem-
solving capacity. However, they suffer from a serious integrity threat, ie, their vulnerability to …

Spiking Neural Networks (SNNs), despite being energy-efficient when implemented on
neuromorphic hardware and coupled with event-based Dynamic Vision Sensors (DVS), are …

Adversarial examples have emerged as a significant threat to machine learning algorithms,
especially to the convolutional neural networks (CNNs). In this paper, we propose two …

The real-world use cases of Machine Learning (ML) have exploded over the past few years.
However, the current computing infrastructure is insufficient to support all real-world …

Significant progress has been made towards developing Deep Learning (DL) in Artificial
Intelligence (AI) models that can make independent decisions. However, this progress has …