International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Implementation Paper on MindGuard: Implementation of Early Burnout Prediction and Advisory System Using Machine Learning |
|---|---|
| ABSTRACT | This paper presents the implementation of MindGuard, an AI-based early burnout prediction and advisory system designed to identify stress and burnout risk in individuals. The system collects user inputs such as working hours, sleep patterns, stress levels, and emotional state, and processes them using a machine learning model to predict burnout levels. The implementation integrates a frontend interface, a Python-based backend, and a machine learning model for classification. Additionally, an AI-powered chatbot is incorporated to provide real-time suggestions and mental wellness guidance. The system also stores previous interactions to improve future predictions. The implemented system demonstrates effective prediction of burnout risk and provides timely recommendations, helping users maintain mental well-being and prevent burnout. |
| AUTHOR | OM JADHAV, PRAVIN GAIKWAD, MANASI KATKAR, V.G.SONAWANE Department of Artificial Intelligence & Machine Learning, AISSMS's Polytechnic, Pune, Maharashtra, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403065 |
| pdf/65_Implementation Paper on MindGuard Implementation of Early Burnout Prediction and Advisory System Using Machine Learning.pdf | |
| KEYWORDS | |
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