Artificial Neural Networks (ANNs) have been applied to predict many complex problems. In this paper ANNs are applied to horse racing prediction. We employed Back-Propagation, Back-Propagation with Momentum, QuasiNewton, Levenberg-Marquardt and Conjugate Gradient Descent learning algorithms for real horse racing data and the performances of five supervised NN algorithms were analyzed. Data colle...

Remote sensing data provides much essential and critical information for monitoring many applications such as image fusion, change detection and land cover classification. This paper proposed about the classification and extraction of spatial features in urban areas for high resolution multispectral satellite image. Spectral information is the foundation of remotely sensed image classification....

The possibility of applying artificial neural networks in different areas determined the discovery of more complex structures. This chapter describes the characteristic aspects of using a back-propagation neural network algorithm in making financial forecasting improved by a different technology: genetic algorithms. These can help build an automatic artificial neural network by two adaptive pro...

The knowledge of the contribution bd(d) to the artificial sky luminance in a given point of the sky of a site produced by the sources beyond a given distance d from it is important to understand the behaviour of light pollution in diffusely urbanized areas and to estimate which fraction of the artificial luminance would be regulated by norms or laws limiting the light wasted upward within prote...

This research implements a novel method of gender classification using fingerprints. Two methods are combined for gender classifications. The first method is the wavelet transformation employed to extract fingerprint characteristics by doing decomposition up to 5 levels. The second method is the back propagation artificial neural network algorithm used for the process of gender identification. ...

The performance of artificial neural networks (ANNs) is highly influenced by the selection input variables and architecture defined hyper parameters such as number neurons in hidden layer connections between network variables. Although there are some black-box trial error based studies literature to deal with these issues, it fair state that a rigorous systematic method providing global unique ...