unknown noise and artifacts present in medical signals with  non-linear fuzzy filter will be estimate and then removed. an adaptive neuro-fuzzy interference system which has a nonlinear  structure presented  for the noise function prediction by before samples. this paper is about a neuro-fuzzy method to estimate unknown noise of electrocardiogram (ecg) signal. adaptive neural combined with fuzz...

Elimination of data dependencies through value prediction can help to extract more parallelism. High accuracy value prediction is thus essential in achieving significant performance improvement by exposing more ILP. Various data value prediction methods have been studied [SS97],[WF97]. In this paper, the Shifting Locality Stride Predictor (SLSP) and Repeating Peak Predictor(RPP) are introduced,...

We present and test a sequential learning algorithm for the short-term prediction of human mobility. This novel approach pairs the Exponential Weights forecaster with a very large ensemble of experts. The experts are individual sequence prediction algorithms constructed from the mobility traces of 10 million roaming mobile phone users in a European country. Average prediction accuracy is signif...

Associative classification is a recent and rewarding technique which integrates association rule mining and classification to a model for prediction and achieves maximum accuracy. Associative classifiers are especially fit to applications where maximum accuracy is desired to a model for prediction. There are many domains such as medical where the maximum accuracy of the model is desired. Heart ...

The efficiency of solar tower power plants depends strongly on the ability to reflect sun light onto a defined point receiver. Due high demands heliostats achieve accuracy at low costs, regular calibration is necessary reduce tracking error. In this paper new method for improving existing methods using deep learning presented. results are validated by data recorded Solar Tower Jülich with examp...