Neutrosophic Cognitive Maps for Violence Cause Analysis

Abstract

Among the most valuable AI methods for modeling complicated things at the moment is the use of fuzzy cognitive mapping (FCMs). Conventional FCMs, on the other hand, can't handle the ambiguity that often arises in decision-making scenarios. A novel expansion of conventional FCMs called neutrosophic cognitive mapping (NCMs) was developed to address this shortcoming. However, the indeterminacy is not well handled by the NCMs stated in the citations since the level of indeterminacy is not quantified. In certain cases, choices should be seen as a series of steps that are only loosely related to one another. This occurs in project assessment when several activities depend on one another. Another difficult aspect of FCMs is that there isn't an appropriate topology for representing these types of decision-making difficulties. To aid in making decisions over several time periods, this research introduces a neutrosophic cognitive map built on triangular neutrosophic values (MS-TrNCM) for violence analysis. Through the use of triangular neutrosophic numbers, the suggested model allows experts to express their choices while considering various extents of truth, indeterminacy, and falsity in the underlying map linkages.