Preterm births affect around 15 million children a year worldwide. Current medical efforts
focus on mitigating the effects of prematurity, not on preventing it. Diagnostic methods are …

A deep belief network (DBN) is effective to create a powerful generative model by using
training data. However, it is difficult to fast determine its optimal structure given specific …

Anomaly detection during milk processing (such as changes in fat or temperature, added
water or cleaning solution) can assure a satisfactory final product quality, including …

The objective of this paper is to present a method for predicting blast-induced ground
vibration in open-pit mines that is based on the use of self-organizing neural networks …

This article attempts to increase the prediction accuracy of residue solvent accessibility and
real‐value backbone torsion angles of proteins through improved learning. Most methods …

In this paper, a self-organizing deep belief network (SODBN) with growing and pruning
algorithms is proposed for nonlinear system modeling. Although deep learning-based DBN …

Soil nutrients, including available nitrogen (N), phosphorous (P), and potassium (K), are
critical properties for monitoring soil fertility and function. Spectroscopy analysis has proven …

There has been some improvement in the treatment of preterm infants, which has helped to
increase their chance of survival. However, the rate of premature births is still globally …

The estimation of nitrogen status non-destructively in rice was performed using canopy
spectral reflectance with visible and near-infrared reflectance (Vis/NIR) spectroscopy. The …

In this paper, an artificial neural network (ANN) model is proposed to predict the first
lactation 305-day milk yield (FLMY305) using partial lactation records pertaining to the …