Optimization of Student Comfort Using Ambient Intelligence and Data Mining

Optimization of Student Comfort Using Ambient Intelligence and Data Mining

연구 분야: Databases

학회: International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability

The issue of comfort in school classrooms in Mexico has not been adequately addressed nor effectively managed with the available tools. This inadequacy stems from a lack of proper understanding regarding their implementation. This study conducts an analysis of the application of data mining techniques utilizing climatological variables within a classroom setting to identify patterns and behaviors that predict student comfort. A comparison of various data mining models is performed, and the most effective models are presented. The study employs a dataset encompassing variables such as Temperature, Humidity, Air Quality, Light, and Noise from a classroom environment. Patterns were identified and subsequently labeled into two clusters to develop a student comfort model. Two unsupervised learning models, hierarchical clustering and k-means, were utilized alongside supervised learning models including Neural Networks, Naive Bayes, Decision Trees, and K-Nearest Neighbors.