Neutrosophic and Information Fusion

Siting a solar or wind farm forces planners to reconcile criteria that disagree by nature: irradiation or wind resource, land cost, grid proximity, ecological sensitivity and social acceptance. The evidence for each criterion is heterogeneous and uncertain—remote-sensing estimates, cadastral records, and public-consultation sentiment—and experts often cannot commit to a definite rating. This paper proposes a neutrosophic information-fusion framework that encodes each expert rating as a single-valued neutrosophic number, derives objective criterion weights by neutrosophic entropy, fuses the ratings with a single-valued neutrosophic weighted geometric operator (which, unlike the arithmetic operator, penalises a poor score on any single criterion), and ranks candidate sites by a deneutrosophied score. Applied to six candidate sites and seven criteria, the framework selects a site that balances a strong resource against low ecological conflict, and a full sensitivity analysis over the weight scheme and the risk attitude shows the choice is robust. A comparison against fuzzy-AHP and SVN-TOPSIS indicates the neutrosophic geometric model better exposes sites whose ranking rests on contested, high-indeterminacy criteria.

Cluster-based routing extends the lifetime of an Internet-of-Things wireless sensor network (WSN), but the quality of a cluster head (CH) depends on several mutually uncertain factors—residual energy, link quality, centrality and behavioural trust—each measured imperfectly and reported as a range rather than a point. We model each candidate node with an interval-neutrosophic set (INS), so every factor carries an interval of truth, indeterminacy and falsity, and we fuse the factors with an interval-neutrosophic weighted aggregation operator into a single suitability score. Cluster heads are then elected by a possibility-degree ranking of the fused interval scores, subject to a minimum-trust guard. Simulation of a 200-node network shows that interval-neutrosophic CH selection extends first-node-death time by 18–24% over a standard energy-and-distance heuristic and cuts the share of misbehaving nodes elected as heads from 14.7% to 1.3%. The method degrades gracefully as measurement indeterminacy grows and adds only modest per-round overhead.

Classical Shewhart control charts assume each measurement is a single determinate number. In many real processes— automated gauges reporting tolerance bands, human inspectors giving ranges, or sensors whose readings are only trustworthy within an interval—each observation is better described by a lower and an upper value together with a degree of indeterminacy. We formulate process monitoring in the neutrosophic statistics framework, where a measurement is a neutrosophic number xN = xL+xUIN with indeterminacy interval IN ∈ [IL, IU], and we treat the reconciliation of the determinate and indeterminate parts as an information-fusion step. We derive neutrosophic control limits for the process mean, propose a three-state signalling rule (in-control / watch / out-of-control), and study the average run length (ARL) by simulation. The neutrosophic chart reduces to the Shewhart chart when indeterminacy vanishes, raises the in-control ARL modestly, and under moderate measurement indeterminacy detects a one-sigma mean shift with a smaller out-of-control ARL than a Shewhart chart applied to interval midpoints.

Modern screening rarely relies on a single source of evidence: a clinician weighs an imaging finding against laboratory markers and patient history, and these sources routinely conflict. We propose a neutrosophic informationfusion framework that represents each evidence channel as a neutrosophic triple (t, i, f )—the degree to which the channel supports disease, the degree to which it is indeterminate (noisy, borderline, missing), and the degree to which it argues against disease. Channels are combined with a conflict-aware neutrosophic fusion rule that routes disagreement into the indeterminacy component instead of silently averaging it away. A decision is issued only when fused indeterminacy falls below a referral threshold; otherwise the case is escalated for expert review. On a synthetic two-modality screening cohort the framework attains 91.8% accuracy while flagging 12% of cases as indeterminate, and it degrades gracefully when one modality is corrupted—losing only 2.3 accuracy points against 6.1 for score-level averaging. A cost-sensitive analysis shows the referral gate lowers expected clinical cost when a missed positive is much more expensive than a review.