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@article{206833,
        author = {Nishan and Athmaranjan K and Bangera Ujwal Ganesh and Rohit and Shriharsha},
        title = {Neuro-Behavioral Stress Prediction Via Daily Routine and Online Activity},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {624-632},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206833},
        abstract = {The traditional approaches for detecting stress mainly focus on using expensive clinical testing or intrusive devices in the healthcare sector. Stress usually goes undetected until it manifests through significant psychological or physiological symptoms. An automatic approach is developed in this study to detect stress based on behavioral patterns in digital data. The digital behavioral pattern is generated by compiling multiple behavioral factors, including the screen time, application usage, and emotions displayed in social networking. The combined behavioral pattern is assessed against a predefined baseline pattern using Machine Learning algorithms, which include Logistic Regression for text analysis and Random Forest for numerical features. The predictive model can provide timely alerts before burnout occurs.},
        keywords = {Behavioral Analysis, Machine Learning, Screen Time, Sentiment Analysis, Stress Prediction.},
        month = {July},
        }