Sebastián Ventura

TL;DR: A distributed model to compute a score that measures the quality of each feature with respect to multiple labels on Apache Spark is proposed and results validated through statistical analysis indicate that ENM is able to outperform the reference methods by maximizing the relevance while minimizing the redundancy of the selected features in constant selection time.

TL;DR: A historical perspective and future lines of research concerning the adoption of many-objective optimisation within SBSE are provided, an emerging area that provides advanced techniques to cope with high-dimensional optimisation problems.

TL;DR: The results demonstrate that the spliceosome is drastically altered in PCa wherein SNRNP200, SRSF3 and SRRM1 could represent attractive novel diagnostic/prognostic and therapeutic targets for PCa and CRPC.

TL;DR: A neural network model is proposed that is able to simultaneously address the exploration of the inter-target dependencies and the modeling of complex input-output relationships and is competitive with respect to the state-of-the-art in MTR.

TL;DR: This research study exploits a temporal sequential classification problem to predict early withdrawal of students, by tapping the power of actionable smart data in the form of students' interactional activities with the online educational system, using the freely available Open University Learning Analytics data set by employing deep long short‐term memory (LSTM) model.