We consider autoregressive neural network (AR-NN) processes driven by additive noise and demonstrate that the characteristic roots of the shortcuts-the standard conditions from linear time-series analysis-determine the stochastic behavior of the overall AR-NN process. If all the characteristic roots are outside the unit circle, then the process is ergodic and stationary. If at least one charact...

Nearest neighbours (NN) approaches are a statistically-based pattern classification technique used extensively for computer-assisted decision making in tasks such as ski area and winter road management and backcountry avalanche forecasting. The essential hypothesis behind NN assumes that the available data (normally current meteorological and snowpack data) usefully select similar avalanche con...

BACKGROUND Doxorubicin (DOXO) is an effective anthracycline chemotherapeutic, but its use is limited by cumulative dose-dependent cardiotoxicity. Neuregulin-1β is an ErbB receptor family ligand that is effective against DOXO-induced cardiomyopathy in experimental models but is also proneoplastic. We previously showed that an engineered bivalent neuregulin-1β (NN) has reduced proneoplastic poten...

A new generic neural network (NN) application-improving computational efficiency of certain processes in numerical environmental models-is considered. This approach can be used to accelerate the calculations and improve the accuracy of the parameterizations of several types of physical processes which generally require computations involving complex mathematical expressions, including different...

Neural networks (NN) are new promising tools that can assist the clinicians in the diagnosis process and in therapy decision making, because they can deal with a great number of parameters, learning from examples and assessing any nonlinear relationships between inputs and outputs. In this paper, the problem of prostate cancer evolution prediction is approached using NN. The original database c...

Accurate and timely weather forecasting is a major challenge for the scientific community in hydrological research such as river training works and design of flood warning systems. Neural Network (NN) is a popular regression method in rainfall predictive modeling. This paper investigates the effectiveness of the hybrid Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) evolved neural ...

We evaluated the effect of the halothane (HAL) gene on the quality of pork in domestic pigs. Half-carcasses from two different commercial pig (Sus domestica) crossbreeds were analyzed, 46 of which were homozygous dominant (HAL(NN)) and 69 of which were heterozygous (HAL(Nn)) for the halothane gene. The measures included backfat thickness, lean meat percentage, carcass weight, pH 24 h after slau...

This paper revisits the k-nearest-neighbor (k-NN) Voronoi diagram and presents the first output-sensitive paradigm for its construction. It introduces the k-NN Delaunay graph, which corresponds to the graph theoretic dual of the k-NN Voronoi diagram, and uses it as a base to directly compute the k-NN Voronoi diagram in R. In the L1, L∞ metrics this results in O((n + m) log n) time algorithm, us...

− This paper describes an ultrasonic robot eye for shape recognition using ultrasound pressure data and a Genetic Algorithm. This type of robot eye has commonly used a Neural Network (NN) for shape recognition. However, a NN perform poorly when lacking learned data. In order to overcome this problem when using a NN, we here attempt to replace the NN with a Genetic Algorithm (GA). Unlike a NN, t...

Development of neural network (NN) emulations for fast calculations of physical processes in numerical climate and weather prediction models depends significantly on our ability to generate a representative training set. Owing to the high dimensionality of the NN input vector which is of the order of several hundreds or more, it is rather difficult to cover the entire domain, especially its "fa...