Thanks to the emerging technologies of e-Learning, the analysis of data offers professionals in the education sector, advantages with the ability to impact and revolutionize in the future the way we analyze and evaluate the learning experience.
Expert system of data automatic interpretation
Invalcor is a center specialized in the functional assessment of the locomotor system that by means of advanced technology objective aspects such as the measure of the mobility, strength, power, resistance, or fatigue of a damaged body region. The company is interested in designing a prediction system that, based on the historical data of its activity, attends the diagnosis work to doctors.
DataScience @ UB group has developed a predictive model by means of advanced regression algorithms with the aim of obtaining the maximum success in the diagnosis of the tests. This model allows the company to have the knowledge generated for a long time based on the experience accumulated by professionals who have treated hundreds of cases. The model can be used in different environments and situations: as a reference system in the case of expansion of the business through franchises, as a training system for newcomers, etc.
Practical knowledge accumulated during the practice of a profession is an intangible of high value that can be preserved in many cases and especially in those where professional practice has a high digitization component. The digital traces of biomechanical evaluations have been sufficient in this case to make a decision-making aid model that can improve the professional practice of the center’s employees and also open up new business opportunities.
Within the framework of the Cartographic Plan of Catalonia, the Cartographic and Geological Institute of Catalonia is working on the elaboration of the technical specifications of the land covers Map of Catalonia. The ICGC has commissioned us a study to explore the viability of the application of deep learning techniques in the generation of maps.
A company in the online sector has generated a large volume of data that provides a history to be able to extract information from clients and develop a predictive model based on the behavior of users. This behavior include the navigation through the platform, the interaction with others channels, messages to the community, etc. The ultimate goal is to provide a better customer service by recommending products based on the specific time of the customer journey.