Recently, there has been a growing demand for the deployment of Explainable Artificial
Intelligence (XAI) algorithms in real-world applications. However, traditional XAI methods …

A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature
relevance techniques to explain a deep neural network (DNN) output or explaining models …

Deep learning networks powered by AI are essential predictive tools relying on image data
availability and processing hardware advancements. However, little attention has been paid …

To provide higher data rates, as well as better coverage, cost efficiency, security,
adaptability, and scalability, the 5G and beyond 5G networks are developed with various …

Applications of artificial intelligence (AI) for cybersecurity tasks are attracting greater
attention from the private and the public sectors. Estimates indicate that the market for AI in …

Ensuring both transparency and safety is critical when deploying Deep Neural Networks
(DNNs) in high-risk applications such as medicine. The field of explainable AI (XAI) has …

EXplainable AI (XAI) methods have been proposed to interpret how a deep neural network
predicts inputs through model saliency explanations that highlight the input parts deemed …

Despite artificial intelligence (AI)'s significant growth, its “black box” nature creates
challenges in generating adequate trust. Thus, it is seldom utilized as a standalone unit in …

While 5G is well-known for network cloudification with micro-service based architecture, the
next generation networks or the 6G era is closely coupled with intelligent network …

To design and develop AI-based systems that users and the larger public can justifiably
trust, one needs to understand how machine learning technologies impact trust. To guide …