Human Stress Detection Through Sleep by Using Machine Learning
Keywords:
Human Stress Detection, Through Sleep, Using Machine Learning, AI researchers, Algorithm Using Python
Abstract
An individual's capacity to learn, concentrate, make sound decisions, and solve problems is all profoundly affected by stress. Recently, researchers in the fields of computer science and psychology have begun to focus on stress detection and modelling. Affective states, the sensation of the underlying emotional state, are used by psychologists to quantify stress. Human stress classification has mostly relied on user-dependent models, which can't adapt to different users' needs. This necessitates a substantial amount of effort from new users as they train the model to anticipate their emotional states. Urgent action is required to address prevalent childhood mental health concerns, which, if left untreated, can progress to more complex forms. Analysis of medical data and problem diagnosis are now areas where machine learning approaches shine. After running Features on the complete set of characteristics, we were able to minimise the number of attributes. We compared the accuracy of the chosen set of attributes on several ML methods.
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K. Sharma and R. Tripathi, “4 Intuitionistic fuzzy trigonometric distance and similarity measure and their properties,” in Soft Computing, De Gruyter, 2020, pp. 53–66.
K. Sharma, B. Singh, M. Anam, K. O. Villalba-Condori, A. K. Gupta, and G. K. Ali, “Slotting learning rate in deep neural networks to build stronger models,” in 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), 2021.
K. Sharma, B. Singh, M. Anam, R. Regin, D. Athikesavan, and M. Kalyan Chakravarthi, “Applications of two separate methods to deal with a small dataset and a high risk of generalization,” in 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), 2021.
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A. Ogunmola, M. E. Lourens, A. Chaudhary, V. Tripathi, F. Effendy, and D. K. Sharma, “A holistic and state of the art of understanding the linkages of smart-city healthcare technologies,” in 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), 2022.
Kannan, M. Pattnaik, G. Karthikeyan, Balamurugan, P. J. Augustine, and Lohith, “Managing the supply chain for the crops directed from agricultural fields using blockchains,” in 2022 International Conference on Electronics and Renewable Systems (ICEARS), Tuticorin, India, pp. 908-913, 2022.
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Published
2024-04-07
How to Cite
Rajasekaran G, P.Velavan, & B. Vaidianathan. (2024). Human Stress Detection Through Sleep by Using Machine Learning. International Journal of Human Computing Studies, 6(2), 9-23. Retrieved from https://journals.researchparks.org/index.php/IJHCS/article/view/5266
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