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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.15353122 |
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| _version_ | 1866902049091747840 |
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| author | Jenefer Balraj, Dr M Monica |
| author_facet | Jenefer Balraj, Dr M Monica |
| contents | <p>This project presents an intelligent Human Resource Management System (HRMS) that integrates traditional HR functionalities with machine learning (ML) capabilities to enable personalized employee training recommendations. Built using Django, PostgreSQL, and HTML/CSS/JS, the system streamlines core HR operations such as attendance, leave, compensation, announcements, and self-reviews.</p> <p>A major enhancement is the ML-based training recommendation module, which leverages employee attributes like department and designation to suggest relevant training programs using a supervised learning model trained on historical data. The system includes functionality to dynamically handle unseen labels, ensuring adaptability to evolving roles.</p> <p>This hybrid HRMS not only automates administrative workflows but also drives employee development through data-driven insights, aligning individual growth with organizational goals. It is designed to be scalable, user-friendly, and suitable for real-world enterprise deployment.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15353122 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | ML-Augmented Human Resource Management System for Personalized Employee Upskilling Jenefer Balraj, Dr M Monica <p>This project presents an intelligent Human Resource Management System (HRMS) that integrates traditional HR functionalities with machine learning (ML) capabilities to enable personalized employee training recommendations. Built using Django, PostgreSQL, and HTML/CSS/JS, the system streamlines core HR operations such as attendance, leave, compensation, announcements, and self-reviews.</p> <p>A major enhancement is the ML-based training recommendation module, which leverages employee attributes like department and designation to suggest relevant training programs using a supervised learning model trained on historical data. The system includes functionality to dynamically handle unseen labels, ensuring adaptability to evolving roles.</p> <p>This hybrid HRMS not only automates administrative workflows but also drives employee development through data-driven insights, aligning individual growth with organizational goals. It is designed to be scalable, user-friendly, and suitable for real-world enterprise deployment.</p> |
| title | ML-Augmented Human Resource Management System for Personalized Employee Upskilling |
| url | https://doi.org/10.5281/zenodo.15353122 |