A Neutrosophic Logic and Probability-Based Artificial Intelligence Model for Smart Elderly Care Services in Urban Ecosystems

Abstract

-Smart elderly care services in urban communities often face challenges involving 
uncertainty, incomplete data, and conflicting information. Traditional logic and 
probability frameworks typically fail to manage these complexities adequately. To 
address this issue, this study proposes an innovative, scientifically rigorous 
Neutrosophic-based Smart Elderly Care Model (NSECM) utilizing Neutrosophic Logic 
(NL) and Neutrosophic Probability (NP) from an Ecosystem Theory perspective. The 
model systematically quantifies uncertainty (indeterminacy), contradiction, and 
incompleteness inherent in elderly care data. The proposed model mathematically 
formulates the neutrosophic evaluation of elderly health risks through clearly defined 
neutrosophic equations, explicitly calculating degrees of truth (T), indeterminacy (I), and 
falsity (F). A practical urban community scenario illustrates the step-by-step application 
of the model, clearly demonstrating its effectiveness in managing complex care decisions. 
Results from the case study show that the NSECM significantly improves decision 
reliability compared to traditional probabilistic or fuzzy approaches, explicitly 
highlighting uncertainty and contradictions, thus facilitating better-informed 
interventions. The paper concludes that adopting neutrosophic methods substantially 
enhances the accuracy and responsiveness of smart elderly care services, emphasizing its 
practical and theoretical contributions. Future research directions involve expanding the 
model’s practical validation and exploring advanced neutrosophic learning algorithms. 

DOI: 10.5281/zenodo.15540364