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 | An Intelligent Workplace Safety Assessment Using Machine Learning |
|---|---|
| ABSTRACT | Workplace safety is essential in industries where hazardous conditions can lead to injuries and financial losses. This work presents a machine learning-based system for assessing workplace incident severity using workplace data and visual monitoring. The proposed system collects information from surveillance cameras, sensors, wearable devices, and historical safety records. A Convolutional Neural Network (CNN) is used to detect unsafe worker behavior, missing safety equipment, and hazardous conditions. The system classifies workplace conditions as safe or unsafe and generates real-time alerts for timely action. Data preprocessing and feature extraction improve the performance of the model. The framework supports continuous monitoring and enhances workplace safety compliance. System performance is evaluated using accuracy, precision, recall, and F1-score. The proposed approach helps reduce workplace accidents and improves safety through intelligent real-time monitoring. |
| AUTHOR | G. REVATHI, P. SUCHITRA Asst. Professor, Dept. of MCA, NSRIT, Visakhapatnam, AP, India Dept. of MCA, NSRIT, Visakhapatnam, AP, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407062 |
| pdf/62_An Intelligent Workplace Safety Assessment Using Machine Learning.pdf | |
| KEYWORDS | |
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