RETRACTED: Indoor human occupancy detection using Machine Learning classification algorithms & their comparison

Abstract

This paper presents a method to determine the occupancy in a room with the help of datasets whose data was collected from different sensors and using different ML algorithms. Features representing the occupancy level and the relative changes are taken from different sensors: Humidity sensors, light sensors,temperature sensors carbon dioxide (CO2) sensors. Different classification algorithms which are used for detection of occupancy are: Naive Bayes, classification via regression, random forest, simple logistic, multiclass classification, decision table. The result of the classifier can be classified in the class called - binary-class (which refers to the presence or absence of the person).