Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

The objective of this research is to present the state of the art of the safety assurance of
Artificial Intelligence (AI)-based systems and guidelines on future correlated work. For this …

Deep Reinforcement Learning (DRL) has made significant advancements in various fields,
such as autonomous driving, healthcare, and robotics, by enabling agents to learn optimal …

Machine learning is expected to fuel significant improvements in medical care. To ensure
that fundamental principles such as beneficence, respect for human autonomy, prevention of …

This article proposes an approach named SafeML II, which applies empirical cumulative
distribution function-based statistical distance measures in a designed human-in-theloop …

Data-driven optimal power flow (OPF) approach has been a research focus in recent years.
However, the current data-driven OPF approaches face the following difficulties: 1) the data …

Malicious traffic on the Internet has become an increasingly serious problem, and several
artificial intelligence (AI)-based malicious traffic detection methods have been proposed …

Abstract The use of Unmanned Arial Vehicles (UAVs) offers many advantages across a
variety of applications. However, safety assurance is a key barrier to widespread usage …

With the advancements in utilizing Artificial Intelligence (AI) in embedded safety-critical
systems based on Field-Programmable Gate Arrays (FPGAs), assuring that these systems …

Despite successful use in a wide variety of disciplines for data analysis and prediction,
machine learning (ML) methods suffer from a lack of understanding of the reliability of …