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@article{184182,
        author = {Dr. SAMPATH KUMAR S and YASHASWINI.K.V},
        title = {A Case Study On Stress And Resilience Among Teaching Faculty In Higher Education Using Random Forest},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {510-514},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184182},
        abstract = {Now a days College and university professor face many responsibilities. Handling these kinds of initiations allotted by college or university includes workloads, administrative tasks. In improvement of technologies aids to develop teaching methods. While some teachers manage these pressures well others find it hard to manage that leading to stress, burnout, and decrease in performance. Traditional methods like surveys and interviews are formerly study stress, however very accurate or useful on a large scale. This study looks at how Machine Learning (ML) can help understand and measure stress and resilience in professors. It uses software that can learn from data to find patterns. This research aims to works with different types of data such as survey results, teacher feedback. We use random forest algorithm in our project to forecast stress in higher professors. By comparing random forest algorithm with support vector machine algorithm, the random forest algorithm gives the best result. This can assist universities in developing more encouraging work environments and health initiatives.
Index Terms: Stress, Resilience, Teaching Faculty, Higher Education, Computational Learning, Forest- Based Classifier, Support Vector Machine (SVM), Predictive Analytics, Mental Health, Burnout.},
        keywords = {},
        month = {October},
        }