Citation

Deivanayagampillai, Nagarajan and Bhuvaneswari, Thangavel and Suppiah, Yasothei and Anbalagan, Kanchana (2025) Optimizing Neutrosophic Inventory Management: A Comparative Analysis of XGBoost and Random Forest Models. International Journal of Neutrosophic Science, 26 (4). pp. 122-136. ISSN 2692-6148

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Abstract

Fuzzy sets and probabilistic methodologies have been integrated with forecasting but do not simultaneously capture the truth, indeterminacy, and falsity—really the crux of Neutrosophic Logic (NL). There is no literature investigating the incorporation of neutrosophic numbers into deep architectures, in particular into Neutrosophic Neural Networks (NNNs) for demand forecasting. This contribution fills the gap with the presentation of a Neutrosophic Neural Network (NNN) model with uncertainty explicitly included, enhancing the reliability and explain ability of demand forecasting. Deep learning-based demand forecasting strategies involving the use of Random Forest regression and XGBoosting algorithms generally do not deal with uncertainty and imprecision related with real-world demand data. The current work introduces a new model Neutrosophic Neural Network (NNN) where Neutrosophic Logic (NL) is integrated into deep learning demand forecasting. A novel neutrosophic activation function and a Neutrosophic Mean Squared Error (NMSE) loss function are proposed study, is implemented with the Random Forest regression and XGBoosting algorithms, and trained using synthetic and real-world demand data. Experimental results establish the better performance of the NNN approach about forecasting accuracy, robustness, and uncertainty handling. The sensitivity analysis also confirms the flexibility of the model with different demand patterns. The work contributes significantly towards neutrosophic deep learning and the possibility of robust and interpretable demand forecasting for supply chain and business intelligence.

Item Type:	Article
Uncontrolled Keywords:	Demand Forecasting; Deep Learning; Neutrosophic Neural Network; Uncertainty Modeling; LSTM; Supply Chain Optimization
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	27 Aug 2025 05:48
Last Modified:	27 Aug 2025 06:29
URII:	http://shdl.mmu.edu.my/id/eprint/14472

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