AI-assisted technology optimization in disability support systems using fuzzy rough MABAC decision-making

The selection of AI-assistive technologies for disability support systems involves a complex decision-making problem due to the presence of uncertain evaluation criteria. The traditional methods of decision-making often do not succeed in addressing the challenges leading to potential inefficiencies in resource allocation. Aggregation operators are the fundamental tool to manage overall information into a single value. This characteristic of aggregation operators helps in ranking processes and decision-making scenarios. To overcome the issues of uncertainty and keep in mind the advantages of AOs, in this article, we have proposed the notion of fuzzy rough Maclaurin symmetric mean (FRMSM) aggregation theory. The MSM AOs reduce the sensitivity in huge amounts of data due to symmetric formulation. As a result, more accurate and authentic results can be obtained. As the MABAC approach uses border approximation area, so this characteristic reduces the bias and improves the accuracy. Therefore, we have proposed the MABAC approach based on FRMSMS AOs. For the application of the proposed work, we have delivered an algorithm and initiated an illustrative example. We have utilized the proposed work for the optimization of AI-assisted technologies in disability support systems. Thus, it shows its potential for dealing with the problems arising from the selection process of inherent challenges and will offer a reliable tool to the stakeholders in the health and assistive technology design sectors. Additionally, we have proposed a comparative analysis of the initiated approach and discussed that the introduced approach is more reliable and trustable as compared to existing notions.