This systematic review aims to provide deep insights on emerging trends in, and the
potential of, advanced deep learning techniques, such as machine learning algorithms …

This paper engages in a comprehensive investigation concerning the application of
Explainable Artificial Intelligence (xAI) within the context of deep learning and Artificial …

Artificial Intelligence (AI) systems have grown commonplace in modern life, with various
applications from customized suggestions to self-driving vehicles. As these systems get …

In this paper, we present a modular methodology that combines state-of-the-art methods in
(stochastic) machine learning with well-established methods in inductive logic programming …

ABSTRACT Artificial Neural Network (ANN) is one of the powerful techniques of machine
learning. It has shown its effectiveness in both prediction and classification problems …

While Convolutional Neural Networks (CNN) achieve state-of-the-art predictive performance
in applications such as computer vision, their predictions are difficult to explain, similar to …

This document reports the interim results (as of M18) of SAFEXPLAIN's FUSA-aware DL
solution within the scope of WP3-Deep Learning. The work has been performed in …

Explainability is a key aspect of machine learning, necessary for ensuring transparency and
trust in decision-making processes. As machine learning models become more complex, the …

Когнитивные технологии входят в один из самых «интеллектуальных» разделов
теории искусственного интеллекта. Особое место в интеллектуальных системах …

With the expanding role of neural networks, the need for formal verification of their behavior,
interpretability and human post-processing has become critical in many applications. In …