The collection of massive Global Positioning System (GPS) data from travel surveys has increased exponentially worldwide since the 1990s. A number of methods, which range from rule-based to advanced classification approaches, have been applied to detect travel modes from GPS positioning data collected in travel surveys based on GPS-enabled smartphones or dedicated GPS devices. Among these appro...

This study reports a quantitative and qualitative analysis of the use of discourse markers (DMs) in personal narrative and argumentative papers written by 30 undergraduate students: 15 native speakers (NS) and 15 nonnative speakers (NNS). The study also ascertains whether the frequency and the incorrect use of DMs plays a role in determining the quality of ESL writings. Findings showed that the...

Predictive models are generally fitted directly from the original noisy data. It is well known that noise can seriously limit the prediction performance on time series. In this study, we apply the nonlinear noise reduction methods to the problem of foreign exchange rates forecasting with neural networks (NNs). The experiment results show that the nonlinear noise reduction methods can improve th...

OBJECTIVE The aim of the present work was to assess the value of non-nutritive sucking (NNS) measures as predictors of oral feeding performance in comparison to other putative predictors of feeding skills: respiratory support, post-menstrual age (PMA) at birth and the neonatal oral motor assessment score (NOMAS). METHODS This was a prospective, observational study. Cox proportional hazards an...

Institute for instrumentation in Molecular Imaging, CSIC-UPV, Valencia, Spain Neural Networks (NNs) have been suggested to fit experimental data in an efficient way when compared to other methods such as least squares fitting. The advantage of NNs is the avoidance of iterative processes and that it does not require initialization parameters. Furthermore, it is possible to implement the network ...

In this paper, we propose a universal model for high-dimensional data, called the Hybrid Orthogonal Projection and Estimation (HOPE) model, which combines a linear orthogonal projection and a finite mixture model under a unified generative modelling framework. The HOPE model itself can be learned unsupervisedly from un-labelled data based on the maximum likelihood estimation as well as trained ...

The microbial community compositions of surface and subsurface marine sediments and sediments lining burrows of marine polychaetes and hemichordates from the North Inlet estuary (near Georgetown, S.C. ) were analyzed by comparing ester-linked phospholipid fatty acid (PLFA) profiles with a back-propagating neural network (NN). The NNs were trained to relate PLFA inputs to sediment type outputs (...

Neural Networks (NN) have demonstrated remarkable time series fitting and prediction abilities, outperforming in several applications other methods and particularly linear models, such as dynamic linear regression. However, due to their nature, NNs are not easy to interpret and are often considered as black box models. The importance of each independent variable is hard to estimate and therefor...

This paper proposed a novel model for short term load forecast (STLF) in the electricity market. The prior electricity demand data are treated as time series. The model is composed of several neural networks whose data are processed using a wavelet technique. The model is created in the form of a simulation program written with MATLAB. The load data are treated as time series data. They are dec...

OBJECTIVE To describe the methods and analyses of the 1999 National Nutrition Survey (NNS-99). MATERIAL AND METHODS The 1999 National Nutrition Survey (NNS-99) is a probabilistic survey with nationwide representativity. The NNS-99 included four regions and urban and rural areas of Mexico. The last sampling units were households, selected through stratified cluster sampling. The study populati...