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@article{174448,
        author = {Nataraj Swetha and B.GowthamKumar and SK.Mahaboob Basha and Sk.Lal Ahamad and Damaraju Vara Prasada Padmaja},
        title = {An Integrated Network Analysis and Machine Learning Approach for Improved Financial Distress Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4089-4094},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174448},
        abstract = {This examination presents a cross breed model that consolidates network investigation and AI to work on the expectation of monetary pain. By using gathering learning techniques, for example, casting a ballot classifiers and Irregular Woods calculations, the model tends to the challenges of determining monetary misery with a dataset involving 86 elements and 3,672 examples. The joining of cutting edge AI methods with hearty troupe methodologies is intended to upgrade both the exactness and dependability of distinguishing associations in danger of monetary issues. The review surveys the presentation of these techniques in separating among upset and non-bothered elements, showing significant enhancements in expectation exactness. This headway gives important experiences to monetary investigators and leaders, offering a more complex way to deal with assessing monetary wellbeing pointers.},
        keywords = {Ensemble learning, voting classifiers, Random Forest algorithms.},
        month = {March},
        }