Presents the introductory editorial for the inaugural issue of this title. More >

The paper carries out a comparative study that is based on the use of credibility theory to examine pentagonal and hexagonal fuzzy membership functions of machine learning systems. These fuzzy memberships can be used to manage the uncertainty and imprecision of a data driven-model which allows better decision-making in the case of vague or incomplete information. The credibility theory is used to determine quantitatively the reliability of the inferences obtained through each function. Both the membership functions are modelled, incorporated in machine learning framework and tested on randomly generated as well as application specific datasets. The results obtained indicate that the performa... More >

Neutrosophic sets play a significant role for handling indeterminacy. In this paper, we introduce a novel fuzzy non-linear regression model to find the minimum spread of neutrosophic fuzzy sets. Kuhn-Tucker's necessary conditions are employed to estimate the parameters for non-linear regression models, which can be applied to any data set. The resulting hybrid model possesses the ability to minimise the spread of uncertainty in a much better fashion than the existing non-linear regression contenders which rely on KKT- based model. The hybrid approach reduces the maximum spread by 22.09% and improves prediction accuracy, as shown by a 22.23% reduction in RMSE. The study’s findings highligh... More >

Machine intelligence has evolved from being a purely theoretical idea into a fundamental element of contemporary technology, transforming industries and influencing society on a broad scale. This editorial delves into its historical development, recent advancements, and prospective future directions. It highlights the dynamic interaction between technological progress, innovative algorithms, and the ethical challenges that shape the field, offering a thorough and insightful overview. More >