Biometric systems are largely based on Machine Learning (ML) algorithms which are often
considered as a black-box. There is a need to provide them with explanations to make their …

Convolutional neural networks (CNNs) have lately proven to be extremely effective in image
recognition. Besides CNN, hidden Markov chains (HMCs) are probabilistic models widely …

Noncoding RNAs (ncRNAs) have recently attracted considerable attention due to their key
roles in biology. The ncRNA–proteins interaction (NPI) is often explored to reveal some …

Systems capable of analyzing and quantifying human physical or behavioral traits, known as
biometrics systems, are growing in use and application variability. Since its evolution from …

Palmprint as biometrics has gained increasing attention recently due to its discriminative
ability and robustness. However, existing methods mainly improve palmprint verification …

As two sides of the same coin, causality and explainable artificial intelligence (xAI) were
initially proposed and developed with different goals. However, the latter can only be …

Feature extractors significantly impact the performance of biometric systems. In the field of
hand gesture authentication, existing studies focus on improving the model architectures …

Recent image classification efforts have achieved certain success by incorporating prior
information such as labels and logical rules to learn discriminative features. However, these …

Face photo-sketch recognition plays an important role in law enforcement, particularly in
narrowing down the search for potential suspects based on limited sketch information …

Massive human-related data is collected to train neural networks for computer vision tasks. A
major conflict is exposed relating to software engineers between better developing AI …