This research utilizes an Object-Oriented Bayesian Network (OOBN) to model the
relationships between the Sustainable Development Goal (SDGs) and resilience and …

Abstract The CESAM FP7 project (Van Dorsselaere et al., 2015) of EURATOM has been
conducted from April 2013 until March 2017 in the aftermath of the Fukushima Dai-ichi …

Bayesian Networks (BN) are robust probabilistic graphical models mainly used with discrete
random variables requiring discretization and quantization of continuous data. Quantization …

The majority of Bayesian networks learning and inference algorithms rely on the assumption
that all random variables are discrete, which is not necessarily the case in real-world …

While deep learning has achieved huge success across different disciplines from computer
vision and natural language processing to computational biology and physical sciences …

Smart manufacturing is a promising research area for the improvement of productivity and
competitiveness in industry, by exploiting the manufacturing digital data (Tao et al., 2018) …

The last decades improvements in processing abilities have quickly led to an increasing use
of data analyses implying massive data-sets. To retrieve insightful information from any data …

Uncertain reasoning over both continuous and discrete random variables is important for
many applications in artificial intelligence. Unfortunately, dealing with continuous variables …

La fabrication intelligente est un domaine de recherche prometteur pour l'amélioration de la
productivité et de la compétitivité dans l'industrie, par l'exploitation des données numériques …

Abstract Models are becoming invaluable instruments for comprehending and resolving the
problems originating from the interactions between humans, mainly their social and …