We propose a new computational method of input selection for stock market forecasting with neural networks. The method results from synthetically considering the special feature of input variables of neural networks and the special feature of stock market time series. We conduct the experiments to compare the prediction performance of the neural networks based on the different input variables b...

the objective of this paper is to develop an artificial neural network (ann) model which can beused to predict temperature rise due to climate change in regional scale. in the present work data recorded overyears 1985-2008 have been used at training and testing steps for ann model. the multilayer perceptron(mlp) network architecture is used for this purpose. three applied optimization methods a...

Introduction: Automatic arterial input function (AIF) selection has an essential role in quantification of cerebral perfusion parameters. The purpose of this study is to develop an optimal automatic method for AIF determination in dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) of glioma brain tumors by using a new preprocessing method.Material and Methods: For this study, ...

Rich text tasks are increasingly common on mobile devices, requiring the user to interleave typing and selection to produce the text and formatting she desires. However, mobile devices are a rich input space where input does not need to be limited to a keyboard and touch. In this paper, we present two complementary studies evaluating four different input modalities to perform selection in suppo...

With the advancement of metagenome data mining science has become focused on microarrays. Microarrays are datasets with a large number of genes that are usually irrelevant to the output class; hence, the process of gene selection or feature selection is essential. So, it follows that you can remove redundant genes and increase the speed and accuracy of classification. After applying the gene se...

Multistage network based input-buffered ATM switches, which have been studied extensively in the recent past, are cheaper compared to crossbar designs but suffer from elaborate cell selection methods or expensive network setup. In this paper, a fast cell selection method is proposed to avoid slow cell selection and costly network setup for these designs. In particular, we propose network hardwa...

This paper presents a method that combines Mutual Information and k-Nearest Neighbors approximator for time series prediction. Mutual Information is used for input selection. K-Nearest Neighbors approximator is used to improve the input selection and to provide a simple but accurate prediction method. Due to its simplicity the method is repeated to build a large number of models that are used f...

In this paper, a global methodology for the long-term prediction of time series is proposed. This methodology combines direct prediction strategy and sophisticated input selection criteria: k-nearest neighbors approximation method (k-NN), mutual information (MI) and nonparametric noise estimation (NNE). A global input selection strategy that combines forward selection, backward elimination (or ...