SEAMS: A Smart and Efficient Attendance Management System with Attendance Prediction and Forecasting

The conventional approaches of managing attendance are going through an essential shift in this era of rapid technological growth. The Smart Attendance and Efficient Management System (SEAMS), a state-of-the-art system, is presented in this study. SEAMS revolutionizes attendance monitoring and forecasting in corporate and educational settings by utilizing cutting-edge data analytics and prediction methodologies. The SEAMS uses facial recognition authentication technique to track attendance and simply integrates with the current infrastructure. The attendance logs of all employees in their respective departments are being transmitted using Local Aera Network (LAN) to the main server which is managed and controlled by the Human Resource Management Department making the consolidation of attendance reports and data at ease and without the need for physical interaction with every department head, this scheme is essential for rapid accounting and payroll preparations for the employees. This system uses machine learning methods to predict and forecast future attendance patterns in addition to real-time attendance tracking. SEAMS estimates attendance trends with a high degree of accuracy by examining previous data captured by SEAMS. In experimental applications, the use of SEAMS has produced encouraging outcomes that have improved human resource allocation and staffing, and organizational performance. The technology also has the potential to ease administrative costs, improve decision-making procedures, and promote an accountability culture. By offering a comprehensive, data-driven strategy, this research makes a contribution to the changing attendance management landscape. In addition to modernizing attendance monitoring, SEAMS also gives companies and educational institutions the ability to forecast attendance trends, which eventually results in more effective and efficient operations.