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@article{189702,
        author = {S.Kannaki},
        title = {A q-Rung Orthopair Fuzzy DEA–Machine Learning Framework for Efficiency Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {7106-7112},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189702},
        abstract = {Assessing the efficiency of rice farmers is really important for making good decisions. The traditional way of doing this called Data Envelopment Analysis does not work well for agriculture because it assumes we have numbers, which is not possible. There is always some uncertainty when we measure things or ask people for their opinions. This paper is about a way of doing things it combines something called q-Rung Orthopair Fuzzy DEA and Machine Learning to see how well rice farmers in Tamil Nadu are doing even when we are not totally sure, about the numbers. The q-ROF DEA handles vagueness in input–output data, while ML models learn complex relationships between farm attributes and efficiency scores for prediction. A numerical case study based on primary survey data demonstrates the effectiveness of the proposed approach. Results highlight key determinants of efficiency and show that ML model-Random Forest model accurately predict efficiency scores. The integrated framework enhances decision support for agricultural stakeholders.},
        keywords = {Decision Making Units, DEA, Machine Learning, q-Rung Orthopair Fuzzy Sets, Rice Farmers, Technical Efficiency},
        month = {December},
        }