Optimizing biofortified crop selection: a novel feed-backward double hierarchy linguistic neural network approach with Yager-Dombi t-norms

Biofortified crops have gained significant attention as a sustainable solution to address malnutrition and under nutrition, particularly in developing countries. These crops are genetically enhanced to have higher levels of essential nutrients such as vitamins and minerals. It aims to improve the nutritional quality of staple foods and promote better health outcomes in populations that rely heavily on these crops. Biofortified crops play an important role in addressing global health challenges, providing a cost-effective and scalable approach to improving nutrition. But the selection of biofortified crops among various options is a complex task for decision-makers. Therefore, this paper introduces a novel approach called the feed-backward Double-hierarchy linguistic neural networks using double-hierarchy linguistic term fuzzy information to handle this issue. For this, we develop a series of weighted averaging Yager-Dombi aggregation operators and also discuss their desirable properties. The decision-making process becomes complex due to unknown weight vectors. Entropy distance measures are used to locate unknown weight vectors. The study addresses a real-world MADM problem by demonstrating that biofortified rice could potentially address vitamin A deficiency, a significant health concern in developing regions. The WASPAS approach is used to verify the proposed method, and its feasibility and efficacy are evaluated compared to other MADM techniques.