International Research Journal of Education and Technology

           Timetable generation in educational institutions is a complex combinatorial optimization problem involving multiple constraints such as faculty availability, classroom allocation, course requirements, and institutional policies. Traditional manual scheduling is time-consuming, error-prone, and inefficient for large institutions. This paper proposes an Artificial Intelligence (AI)-based automated timetable generation system that utilizes constraint satisfaction techniques and optimization algorithms to produce feasible and optimized schedules. The system incorporates hard and soft constraints, ensuring conflict-free timetables while maximizing resource utilization. Experimental results demonstrate significant improvements in efficiency, scalability, and adaptability compared to manual scheduling methods.datasets can categorize images into healthy, mild, moderate, severe, or proliferative stages of the disease. Early prediction enables timely medical intervention and continuous patient monitoring, thereby preventing severe complications. Such intelligent healthcare solutions support large-scale screening programs, telemedicine applications, and improved accessibility to eye care services, contributing to better patient outcomes and reduced healthcare burden.

Keywords : Timetable Scheduling, Artificial Intelligence, Constraint Satisfaction Problem, Genetic Algorithm, Optimization, Resource Allocation