Design of an Intelligent Framework for Risk Prediction and Alert Optimization in Smart University Environments

Abstract

This paper presents the design of an intelligent and integrated framework for predicting and managing both academic and operational risks in smart university environments. The proposed system conceptually combines machine learning and soft computing techniques to enable proactive and interpretable risk governance. The framework integrates data from multiple sources including Learning Management Systems (LMS), Internet of Things (IoT) sensors, and external environmental APIs to generate predictive insights and optimize alert dissemination.

The architecture leverages ensemble models such as Random Forests, Gradient Boosting, and Explainable Boosting Machines (EBMs), alongside metaheuristic algorithms including Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to enhance decision accuracy and delivery efficiency. Although the implementation is ongoing, a simulation-based validation plan demonstrates the feasibility and scalability of the framework within a smart campus context. The proposed design emphasizes transparency, adaptability, and real-time awareness, providing a foundation for future deployment in intelligent university risk management systems.