The large-scale deployment of machine learning models in a wide variety of AI-based
systems raises major security concerns related to their integrity, confidentiality and …

Deep learning is a key driver that puts artificial intelligence (AI) on the radar screen for
technology investment. Deep Neural Network (DNN) automatically learns high-level features …

Machine learning (ML), and deep learning in particular, have become a critical workload as
they are becoming increasingly applied at the core of a wide range of application spaces …

Machine learning has become prevalent in transforming diverse aspects of our daily lives
through intelligent digital solutions. Advanced disease diagnosis, autonomous vehicular …

Abstract Machine Learning deployment in Embedded Systems and Edge devices offer
interesting advantages compared with the Cloud-based approaches, especially from a …

Artificial neural networks (ANNs) have gained significant popularity in the last decade for
solving narrow AI problems in domains such as healthcare, transportation, and defense. As …

Utilization of Machine Learning (ML) algorithms, especially Deep Neural Network (DNN)
models, becomes a widely accepted standard in many domains more particularly IoT-based …

Deep learning has gained tremendous success and great popularity in the past few years.
However, deep learning systems are suffering several inherent weaknesses, which can …

During the last decade, there has been a stunning progress in the domain of AI with
adoption in both safety-critical and security-critical applications. A key requirement for this is …

Machine learning (ML) models deployed in many safety-and business-critical systems are
vulnerable to exploitation through adversarial examples. A large body of academic research …