Volume 27 , Issue 1 , PP: 125-138, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Tanvir Mahmoud Hussein 1 * , Tojiyev Rakhmatilla 2 , Danish Ather 3 , Rubina Liyakat Khan 4 , Tiyas Sarkar 5 , Manik Rakhra 6
Doi: https://doi.org/10.54216/IJNS.270112
This paper proposes a novel hybrid framework that integrates Neutrosophic Logic with Artificial Intelligence (AI) for robust spatiotemporal modeling of urban land parcel transactions. The approach captures the uncertainty, inconsistency, and incompleteness often found in public land auction data through the application of neutrosophic triplets, defined by degrees of truth, indeterminacy, and falsity. Using longitudinal transaction records from Tashkent, the model transforms raw data into neutrosophic representations and feeds them into a Long Short-Term Memory (LSTM) network for forecasting. The enriched feature space enhances interpretability and prediction accuracy across administrative zones. Experimental evaluations demonstrate the superiority of the proposed Neutrosophic-AI model over conventional methods in terms of forecasting precision and uncertainty handling. This study offers a foundational contribution to neutrosophic-based urban analytics and supports transparent digital governance frameworks.
Neutrosophic logic , Artificial Intelligence , Spatiotemporal analysis , Land parcel transactions , E-AUKSION portal , Urban analytics , Tashkent , Digital governance
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