Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI).
In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Reasoning is applied on such sensor data in order to compute prediction. Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT).
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