Intellectual Property India Publishes Patent Application for 'Artificial Intelligence-Driven Framework For Stress Level Estimation And Academic Performance Forecasting In Social Media-Dependent Higher Education Student' Filed by Dr. Anil Kumar Menaria; Dr. Narendra Singh Ranawat; Renu Pandey; Nilakshi Teron; Archana Barman; Dr. Vanajaroselin Chirchi; and Dr Joel Osei-Asiamah

MUMBAI, India, Nov. 21 -- Intellectual Property India has published a patent application (202511093811 A) filed by Dr. Anil Kumar Menaria; Dr. Narendra Singh Ranawat; Renu Pandey; Nilakshi Teron; Archana Barman; Dr. Vanajaroselin Chirchi; and Dr Joel Osei-Asiamah, Udaipur, Rajasthan, on Sept. 30, for 'artificial intelligence-driven framework for stress level estimation and academic performance forecasting in social media-dependent higher education student.'

Inventor(s) include Dr. Anil Kumar Menaria; Dr. Narendra Singh Ranawat; Renu Pandey; Nilakshi Teron; Archana Barman; Dr. Vanajaroselin Chirchi; and Dr Joel Osei-Asiamah.

The application for the patent was published on Nov. 21, under issue no. 47/2025.

According to the abstract released by the Intellectual Property India: "The present invention relates to the development of the students in higher education are using social media more and more, which has sparked worries about how it may affect their stress levels and academic performance. This study suggests a framework powered by artificial intelligence (AI) for predicting academic achievement and estimating stress levels in students who are heavily dependent on social media. The framework uses deep learning and sophisticated machine learning techniques to create prediction models by integrating multimodal data sources, such as academic records, behavioral indications, and social media activity patterns. Regression and classification algorithms are used to predict academic results, and sentiment analysis, natural language processing (NLP), and behavioral analytics are used to measure stress levels. Additionally, the system integrates adaptive feedback and real-time monitoring to support tailored interventions that enhance academic performance and mental health. The framework offers a comprehensive method for detecting at-risk children and boosting their academic resilience by connecting psychological testing with AI-enabled predictive analytics. This helps to ensure sustainable educational growth in the digital age."

Disclaimer: Curated by HT Syndication.