Prediction of chemical bioactivity and physical properties has been one of the most
important applications of statistical and more recently, machine learning and artificial …

Advances in nanomedicine, coupled with novel methods of creating advanced materials at
the nanoscale, have opened new perspectives for the development of healthcare and …

Decades of nanotoxicology research have generated extensive and diverse data sets.
However, data is not equal to information. The question is how to extract critical information …

Abstract Machine learning (ML) has proven to be a useful technology for data analysis and
modeling in a wide variety of domains, including food science and engineering. The use of …

Materials at the nanoscale exhibit specific physicochemical interactions with their
environment. Therefore, evaluating their toxic potential is a primary requirement for …

Fault tree analysis is a widely used method of risk assessment in process industries.
However, the classical fault tree approach has its own limitations such as the inability to deal …

Risk assessment (RA) plays a vital role in safety engineering. The conventional RA
approaches have limited capabilities in handling time dependence and data uncertainty …

Machine Learning (ML) techniques have been applied in the field of nanotoxicology with
very encouraging results. Adverse effects of nanoforms are affected by multiple features …

Effective food safety (FS) management relies on the understanding of the factors that
contribute to FS incidents (FSIs) and the means for their mitigation and control. This review …

The presence and development of many food safety risks are driven by factors within and
outside the food supply chain, such as climate, economy and human behaviour. The …