A Neutrosophic-AI Model for Spatiotemporal Analysis of Land Parcel
Transactions
Tanvir Mahmoud Hussein1,∗, Tojiyev Rakhmatilla2, Danish Ather3,, Rubina Liyakat Khan4,
Tiyas Sarkar5, Manik Rakhra5
1College of Administrative & Financial Sciences, Gulf University, Bahrain
2Tashkent State University of Economics, Uzbekistan
3Amity University in Tashkent, Uzbekistan
4Computer Science Department, Applied College, Imam Abdulrahman Bin Faisal University, Saudi Arabia
5School of Computer Science and Engineering, Lovely Professional University, Punjab, India
Emails: dr.tanvir@gulfuniversity.edu.bh; r.tojiyev@tsue.uz; danishather@gmail.com; rlkhan@iau.edu.sa;
info.tiyasofficial11901657@gmail.com; rakhramanik786@gmail.com
Abstract
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 un-
certainty, 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 transac-
tion 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 in-
terpretability and prediction accuracy across administrative zones. Experimental evaluations demonstrate the
superiority of the proposed Neutrosophic-AI model over conventional methods in terms of forecasting pre-
cision and uncertainty handling. This study offers a foundational contribution to neutrosophic-based urban
analytics and supports transparent digital governance frameworks.
Keywords: Neutrosophic logic; Artificial Intelligence; Spatiotemporal analysis; Land parcel transactions;
E-AUKSION portal; Urban analytics; Tashkent; Digital governance