SMART CAMPUS DEVELOPMENT: INTEGRATING HEALTHCARE, ENGINEERING, IT, BUSINESS, AND LEGAL FRAMEWORKS FOR FUTURE EDUCATION

Abstract

The smartening of educational institutions has been turned into a worldwide concern in order to facilitate future-oriented education. The study explores the combination of healthcare, engineering, information technology (IT), business and law to develop a holistic smart campus model. Four machine learning algorithms were implemented to assess student engagement, healthcare monitoring, energy efficiency, and regulatory compliance using four machine learning algorithms including the Decision Tree, the Random Forest, the K-Nearest Neighbors (KNN), and the Support Vector Machine (SVM) based on the secondary datasets and simulated situations. The findings indicate that the accuracy of the Random Forest was the highest at 91% and this makes it very reliable to make multi-domain predictions. The Support Vector Machine with an accuracy of 88% proved to be useful specifically in the compliance and risk management activities. Decision Tree scored 84, which has easy to interpret results that are useful to administrators. KNN with a 80 slightly lower, performed quite well in student engagement and personalized learning predictions. The comparative analysis of the current literature shows that the given study provides a multidisciplinary approach as opposed to the specific technological solutions. The results emphasise that the development of smart campuses is the process that needs not only technological innovativeness but also the ethical, legal, and sustainable models to be considered as the means to provide inclusivity and sustainability of the campuses.