Dynamic Reliability Kernels for Single-Valued Neutrosophic
Evidence Fusion: A Mathematical Model for Multi-Source
Market-State Classification
Samandarboy Sulaymanov1,∗, Maha Ibrahim1
1Tashkent state university of economics, Uzbekistan
Emails: sulaymanovsamandarboy@gmail.com ; maha.ahmed860@hgmail.com
Abstract
Multi-source decision systems require a representation in which supportive evidence, contradictory evidence, and weak evidence are
not collapsed into the same numerical channel. This paper develops a dynamic reliability-kernel model for single-valued
neutrosophic evidence fusion. Given a matrix of source signals, each source is transformed into a single-valued neutrosophic triplet
whose truth, indeterminacy, and falsity memberships are governed by signed evidence strength. A time-varying reliability kernel
then assigns larger mass to sources with lower recent instability, and a dispersion-augmented fusion operator produces a global
neutrosophic state. The final decision rule is formulated as a penalized neutrosophic score and as a regularized probabilistic classifier
over the fused triplet. The model is evaluated on a public weekly stock dataset containing six technology-market sources. The
results show that the proposed representation achieves competitive chronological classification performance while providing explicit
mathematical control over indeterminacy, disagreement, and reliability. Ablation and penalty-sensitivity analyses demonstrate
that indeterminacy is a functional component of the decision model rather than a cosmetic label. The paper offers a reproducible
mathematical framework for neutrosophic information fusion in uncertain intelligent decision-support systems.
Keywords: Single-valued neutrosophic sets; Neutrosophic evidence fusion; Reliability kernel; Indeterminacy penalty;
Multi-source classification; Uncertainty-aware decision support