Volume 26 , Issue 4 , PP: 28-41, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Muhammad Saqlain 1 * , Poom Kumam 2 , Wiyada Kumam 3
Doi: https://doi.org/10.54216/IJNS.260404
This paper proposes a novel smart farming decision-making framework that integrates machine learning (ML) techniques Support Vector Machine (SVM), Fuzzy C-Means (FCM) clustering, with the generalized distance and similarity measures in a linguistic neutrosophic hypersoft set environment. ML processes real-time sensor data to predict weather patterns, while linguistic neutrosophic terms capture uncertainty, indeterminacy, and falsity, allowing for a more precise analysis of imprecise information. Through the application of generalized similarity measures, the framework ranks the cities suitable for farming strategies based on multiple criteria such as temperature, wind speed, and humidity. The use of linguistic neutrosophic terms offer enhanced flexibility in managing weather-related uncertainty compared to existing methods. The outcomes demonstrate that this integrated approach optimizes decision-making under uncertain environmental conditions, enabling more efficient resource management and improving resilience in farming practices. Future research will further explore the inclusion of additional environmental factors and improve similarity measures to increase decision accuracy among broader agricultural contexts. This model also has the potential to be applied to other domains where uncertainty management is crucial, such as climate resilience and environmental sustainability.
Decision-making , Fuzzy Set , Neutrosophic Set , Hypersoft set , Machine learning , Weather uncertainty , Agriculture farming , MCDM
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