The spatial heterogeneity of landslide influencing factors is the main reason for the poor
generalizability of the susceptibility evaluation model. This study aimed to construct a …

Many clinicians remain wary of machine learning because of longstanding concerns about
“black box” models.“Black box” is shorthand for models that are sufficiently complex that they …

Abstract Machine Learning (ML) applications are making a considerable impact on
healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the …

This paper investigates the applications of explainable AI (XAI) in healthcare, which aims to
provide transparency, fairness, accuracy, generality, and comprehensibility to the results …

Nowadays, deep neural networks are widely used in mission critical systems such as
healthcare, self-driving vehicles, and military which have direct impact on human lives …

Deep learning models have achieved high performance across different domains, such as
medical decision-making, autonomous vehicles, decision support systems, among many …

There is a need of ensuring that learning (ML) models are interpretable. Higher
interpretability of the model means easier comprehension and explanation of future …

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals
have a critical impact on human health. Traditional animal models to evaluate chemical …

The lack of transparency of powerful Machine Learning systems paired with their growth in
popularity over the last decade led to the emergence of the eXplainable Artificial Intelligence …

Waste biomass pyrolysis is a promising thermochemical conversion process for the
production of biofuels and sustainable materials. However, it is challenging to accurately …