Published Paper Details:

Developing an effective model to identify uncertainties in real-world situations has been challenging due to incomplete, inconsistent, or ambiguous data. This research paper presents a new framework (FPNSM) basedon a five-component membership structure that extends current neutrosophic soft matrixes by enhancing and/or increasing uncertainty breakdown into levels of truth, contradiction, uncertainty, hesitation or falsity; enables users to create a more detailed and dynamic description of an undetermined (uncertain) piece of data. The FPNSM is developed by combining three theoretical components (fuzzy sets, neutrosophic sets and soft set theory). It systematically defines the matrix structure, fundamental operations, and applicable properties of the various algebraic structures associated with the FPNSM. It provides an evaluative basis through the establishment of score/accuracy functions that allow an evaluator to rank their alternatives in a variety of uncertain, dynamic environments. A decision algorithm based on the FPNSM is provided to assist in making decisions based on multiple criteria. In addition, the effectiveness of the FPNSM methodology has been demonstrated through a case study; therefore, this research demonstrates how using the FPNSM can enhance the precision of the output produced when evaluating alternative options relative to an objectively and systematically determined standard of performance compared with existing processes. Subsequently, this research represents an important advancement in uncertainty modeling and provides an avenue for further research into the fields of decision support systems, engineering applications, and data analysis under variable/complex conditions.