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@article{186835,
        author = {Rachana Parikh and Prof. Dr. Vishal Dahiya},
        title = {Machine Learning in Cognitive Stress Research: Bridging Minds and Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {2648-2655},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186835},
        abstract = {Stress remains a widespread concern that impairs cognitive psychology, workplace productivity, and overall well-being. This survey investigates how stress influences cognitive processes, with a primary focus on detection and analysis through machine learning approaches. Cognitive psychology offers critical frameworks for understanding stress appraisal, where individuals mentally classify stressors as threats or challenges. Machine learning techniques, especially those leveraging wearable sensors and physiological signals, enable non-invasive, real-time stress monitoring. The integration of artificial intelligence, machine learning, and psychological principles has further enabled the use of brain EEG signals to forecast cognitive stress instantaneously. Diverse data sources ranging from heart rate variability to multimodal physiological and psychological inputs support comprehensive stress modeling and accurate prediction. Ultimately, this work highlights the interdisciplinary synergy between cognitive science and advanced algorithms, paving the way for scalable, personalized stress management systems that enhance mental resilience, decision-making efficiency, and long-term health outcomes across occupational, educational, and clinical environments.},
        keywords = {Stress, Cognitive Psychology, Machine Learning, Artificial Intelligence},
        month = {November},
        }