European Journal of Computer Science and Information Technology (EJCSIT)

EA Journals

linear regressor

Linear Regressor Model for Internet of Things Automated Thermal Conditioning of a Smart Classroom Environment Using Limited dataset (Published)

This research paper presents a Linear Regressor (LR) modeling approach for automated thermal conditioning of smart spaces in a classroom environment using limited data constraints.  Sensitive studies were performed in order to identify the breaking point of the LR model of polynomial-order-of-1 below which it will not be possible to obtain meaningful estimates and controllability actions, considering a range between 5% and 30% limited training data points. Based on several simulation experiments, it was found that the LR breaks at the 10% training data level. This result is valuable for recommender systems that may experience difficulty in obtaining enough primary dataset. Applications that provide early warning signals will also find this study useful.

Keywords: linear regressor, primary dataset., recommender systems, simulation experiment, smart classroom

A Smart Campus Internet-of-Things (IoT) Model for Smart Classroom Conditioning Using a Hybridized Technique (Published)

This research study presents Smart Campus (SC) Internet-of-Things (IoTs) enabled systems model that will support end-user and automatic functions for proper air conditioning of SC classrooms environment. It consists of a hybrid data learning predictor system using an emerging variant of Artificial Neural Network (ANN) called Neuronal Auditory Machine Intelligence (NeuroAMI) and a Linear Regressor (LR) of polynomial-order-of-1.The system was initially applied separately to the automated coordination of a smart bed in a laboratory sized classroom environment at a University Campus, and simulated using the high-level programming language – MATLAB, while end user interaction model was developed in the Java2ME programming language. Simulations results considering several trial runs showed that the ANN predictor generally performed better than the LR model with over 80% classification accuracy. While considering limited training data points, the LR predictor was found to be superior at one of the simulation trial runs. At 20% data point, the LR was activated while the NeuroAMI remains inactive, but above the 20% level, the NeuroAMI performed better. One advantage of this proposed hybrid system is the ability to deal with continuous data; exactly the same way human brains functions. This feat has not been possible in conventional ANN systems, especially in this area dealing with small data points.

Keywords: artificial neural network (ANN), internet of things, linear regressor, neuronal auditory machine intelligence, predictor system, smart campus

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