Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many
technology products and their fields of application. Machine learning, as a major part of the …

Purpose This study aims to explain the state-of-the-art machine learning models that are
used in the intrusion detection problem for human-being understandable and study the …

In the last decade neural network have made huge impact both in industry and research due
to their ability to extract meaningful features from imprecise or complex data, and by …

Abstract Intrusion Detection Systems (IDSs) is an important research topic in security
engineering. The role of an IDS is to detect the malicious incoming network flows, hence it …

Deep neural networks are one of the most successful classifiers across different domains.
However, their use is limited in safety-critical areas due to their limitations concerning …

The Brazilian judicial system is currently one of the largest in the world with more than 77
million legal cases awaiting decision. The use of machine learning could help to improve …

In uncertain times, risk management is critical in keeping companies from acting rashly and
wrongly, allowing them to become more flexible and resilient. International cooperative …

The Likelihood Ratio (LR) is fundamental in presenting foren-sic speaker recognition (FSR)
results. Despite its theoretical benefits, conventional LR estimation lacks transparency …

The exponential growth of scientific production makes secondary literature abridgements
increasingly demanding. We introduce a new open-source framework for systematic reviews …

Automatic speaker recognition (ASpR) has been integrated into critical applications, ranging
from customised assistant services to security systems and forensic investigations. It aims to …