Self-Machine Learning Data Intelligence Research Section
The self-machine learning data intelligence research section has been developing the data intellectualization technology dealing with multi-modal (sensor stream, structured, unstructured) data obtained from various IoE networks at a peta-byte scale, in order to generate the refined high-quality data for enabling self-machine learning and profound data analysis effectively.
Towards a high-leveled data intellectualization and reliability, the developing technology provides not only the customized classification of multi-modal IoE data and the automated recommendation of filtering methods, but also a new privacy preserving and polluted-reliability prevention method by filtering and/or manipulating corrupted/errored and sensitive privacy data.
Also, our section has been developing “IoE based elderly health monitoring technology” which contains self-machine learning engine and data intelligence technology. This technology includes an intelligent elderly stroke prediction services, silver life advisor technology and a self-machine learning based disease prediction algorithm which utilizes expert knowledge of standardized action/movement patterns and physiological signals.
Director Kim, Sun Jin