The field of Explainable Artificial Intelligence (XAI) is a relatively new approach to AI, with the aim provide black box algorithms human intelligible narrative functionality. It most often in end-of-life considerations asset lifecycle that sustainability issues are encountered. Modern maintenance practice requires holistic understanding and options for sustainable treatments. loop solutions o...

BACKGROUND The well-established left hemisphere specialisation for language processing has long been claimed to be based on a low-level auditory specialization for specific acoustic features in speech, particularly regarding 'rapid temporal processing'. METHODOLOGY A novel analysis/synthesis technique was used to construct a variety of sounds based on simple sentences which could be manipulat...

Traditionally, explainable artificial intelligence seeks to provide explanation and interpretability of high-performing black-box models such as deep neural networks. Interpretation remains difficult, because their high complexity. An alternative method is instead force a deep-neural network use human-intelligible features the basis for its decisions. We tested this approach using natural categ...

Generalized intuitionistic fuzzy soft set theory, proposed by Park et al. [Journal of Korean Institute of Intelligent Systems 21(3) (2011) 389-394], has been regarded as an effective mathematical tool to deal with uncertainties. In this paper, we prove that certain De Margan’s law hold in generalized intuitionistic fuzzy soft set theory with respect to union and intersection operations on gener...