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20261008160418
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American Scientific Publishing Group
International Journal of Neutrosophic Science
IJNS
2692-6148
2690-6805
2026
27
1
A Neutrosophic-AI Model for Spatiotemporal Analysis of Land Parcel Transactions
Tanvir Mahmoud
Hussein
College of Administrative & Financial Sciences, Gulf University, Bahrain
Tojiyev
Rakhmatilla
Tashkent State University of Economics, Uzbekistan
Danish
Ather
Amity University in Tashkent, Uzbekistan
Rubina Liyakat
Khan
Computer Science Department, Applied College, Imam Abdulrahman Bin Faisal University, Saudi Arabia
Tiyas
Sarkar
School of Computer Science and Engineering, Lovely Professional University, Punjab, India
Manik
Rakhra
School of Computer Science and Engineering, Lovely Professional University, Punjab, India
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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.
2026
125
138
3966
https://creativecommons.org/licenses/by/4.0/
10.54216/IJNS.270112
https://www.americaspg.com/journal/21/article/3966
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https://www.americaspg.com/storage/91753397676.pdf