Volume 25 , Issue 4 , PP: 250-261, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Olga Loseva 1 , Bakhtiyar Ruzmetov 2 , Ildar Begishev 3 , Denis Shakhov 4 , Elena Klochko 5 , Elvir Akhmetshin 6 *
Doi: https://doi.org/10.54216/IJNS.250421
Neutrosophic set (NS) is a novel devise to handle uncertainty considering the memberships of truth T, indeterminacy I, and falsity F satisfying. It is employed to illustrate the indefinite data more appropriately and precisely than an intuitionistic fuzzy set. The search for cost information over the supply chain is very significant for controlling costs that aid in enhancing and beginning activities in organizations in the value chain. In today’s intricate supply networks, sharing data among suppliers and buyers is important for sustainable competitive benefit. Particularly, for both business partners, cost information is extremely appropriate in buying conditions. As per experimental analyses in literature, artificial neural networks (ANNs) are probable to have a great latent to expose cost structures by machine learning (ML). This study presents a novel Interpretation of Kernel Regression Neutrosophic Set using Enhanced Coati Optimization for Cost Estimation Model (KRNSECO-CEM). The main goal of the presented KRNSECO-CEM technique is to analyze and interpret the multi-product of Supply Chain Management Systems. At first, the KRNSECO-CEM approach applies Z-score normalization to pre-process the input data. For the regression process, the kernel regression based neutrosophic set (KRNS) model can be used. Eventually, the enhanced coati optimization algorithm (ECOA) has been applied for the fine-tuning of the best hyperparameter of the KRNS model. The experimental evaluation of the KRNSECO-CEM algorithm can be tested on a benchmark dataset. The extensive outcomes highlighted the significant solution of the KRNSECO-CEM approach over other recent approaches
Supply Chain Management , Enhanced Coati Optimization , Neutrosophic Set , Intuitionistic Fuzzy Set , Kernel Regression
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