Automation of Invigilator Duty Chart

Automation has become integral to optimizing various administrative processes, and its application extends to educational settings.This research explores the utilization of artificial intelligence techniques, particularly machine learnin algorithms, in the automation of invigilator duty chart generation for academic examinations.By leveraging datasets comprising historical exam schedules, invigilator availability, and venue logistics, this study aims to develop a robust system capable of efficiently assigning invigilators to examination sessions.Key techniques such as Support Vector Machine (SVM) will be employed for predictive analysis and optimization.