Published Paper Details:

Attendance management within academic institutions has historically relied on passive recording mechanisms that merely capture presence and absence without extracting deeper insights or enabling proactive decision making. The proposed Smart Attendance Prediction and Alert System introduces a comprehensive analytical layer on top of conventional tracking by examining the dynamics of total classes conducted, classes attended, absence frequency, and recurring behavioral tendencies such as selective absenteeism and clustered bunk patterns. By synthesizing historical trajectories with current progression, the system estimates the probability of a student falling below institutional thresholds and surfaces this risk early enough for meaningful intervention. The design assumes conceptual integration with institutional ERP platforms through secure API mediated data exchange so that attendance streams are ingested automatically and processed continuously. The output is not limited to a percentage but expands into interpretable risk categories accompanied by contextual explanations and recommended corrective actions. A dual notification mechanism communicates these insights to both the student and the parent, thereby strengthening accountability loops and reducing information asymmetry. In effect, the system repositions attendance from a retrospective metric into a forward looking signal that can influence behavior, improve consistency, and minimize last minute academic penalties.