MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641107336 A) filed by Hindustan Institute Of Technology And Science on September 07, 2026, for Ai In Mental Stress Detection.
Inventors include Rohit Kumar S; and Priya M.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: In the present generation student stress has become a common issue because of the factors like high academic pressure,increase in workload,their lifestyle changes and psychological challenges.If the stress is not found at the early stage,it may affect students in both academic performance and their overall mnetal well- being.Therefore,detecting the stress early is important so that the necessary support and the preventive measures can be provided to the students at the earliest.The study proposes a machine learning based model to identify whether a student has stress or not by analyzing different stress-realted factors by using the proposed algorithm- Naïve Bayes classification algorithm.This research uses survey based datasets that include the factors that are related to students stress, such as mental pressure,overthinking,physical fatigue,sleep disturbance and the caffeine intake.Before the model training,the data which we collected will be preprocessed for removing null values and unwanted objects and then transforming categorical responses into numerical values to make the data understandable for the machine learning analysis.Feature engineering methods are then applied to extract only the important indicators that are associated with the stress.To evaluate the performance of this model ,we have tested the Naives Bayes classifier using the Stratified 5-Fold Cross-Validation method,the experimental results show that the proposed model achieves an accuracy level of approximately 96% in indication students as stressed or non-stressed.This system can help educational institutions in monitoring the students well- being and take timely measures to reduce stress and improve the students academic performances. features are independent in nature.This research proposes a stress prediction system using the Naive Bayes classifier. The model analyzes survey-based responses related to mental pressure, overthinking, tiredness, sleep patterns, and caffeine consumption. These factors are commonly associated with stress in students. Data preprocessing techniques such as cleaning, transformation, and feature extraction are applied to improve the quality of input data,then the Naive Bayes algorithm is trained to classify students into stressed and non stressed categories.The objective of this research is to develop a simple and efficient model which can predict stress levels accurately without any complex computations.
Disclaimer: Curated by HT Syndication.
