CLAIRE

Internet of Things (IoT) has made smart environments, such as smart homes, smart buildings, and smart factories, a reality. These environments typically generate huge amount of data, referred to as big data. This data is required to be analysed and interpreted such that it can be utilized for automation at various levels. One of the ways of achieving this is through predictive analytics. Context plays an important role in IoT enabled smart environment as it allows more meaningful interpretation of collected data. Currently, Deep Learning approaches, in particular Deep Neural Networks (DNNs), have gained attention as they are suitable for big data. DNNs have shown their potential in various applications by learning the complex mappings between input and output data. However, their applicability is limited in the domains where the mappings are influenced by context information. This project aims to leverage context information towards developing a Contextual Deep Learning (CxDL) framework. It will attempt to investigate and develop learning mechanisms to realize CxDL, specifically, Contextual DNN that will take in to account context information. As a use case, the developed approach(es) will be applied for predictive analysis in a smart environment, such as in a smart factory for automated condition monitoring and predictive maintenance.