Ambulatory Blood Pressure Monitoring: Modeling and Data Mining

Philippe Esling, Carlos Agon. Time-series data mining.. In almost every scientific field, measurements are performed over time. These observations lead to a collection of organized

This workshop brings together researchers from academia and industry practitioners with special interest in statistics/machine learning, information retrieval, data

The main objective of this present research is to build a predictive model for supporting educators in the hard task of identifying prospective students with dropout profiles in

As discussed in the previous section, it is believed that the optimal solution of most real-world optimization problems is found on the edge of a feasible area of the search space

Section 9.3 is devoted to general scaling predictions (including finite-size scaling) for thermal and quantum fluctuations both in the vicinity of a thermal and of a quantum

We find that beyond the narrow regional and geographical arguments that apply to the study of Forni (2004), it would seem difficult to concede that stationarity tests applied

It included a consecutive series of 118 patients, who had benefited from ABPM for the evaluation of treatment for known and treated hypertensive patients with an

A typical processing chain for generating InSAR time series products consists of (1) assembling a stack of phase‐unwrapped interferograms; (2) optionally applying corrections,