new statistic based models provide a wide area of prediction equipments for different science areas. among these fields biology have just entered the contest of interdisciplinary sciences. drug discovery is a long and expensive process which could be decreased with theoretical approaches. in this study, 500 reported assayed anti cancer molecules were extracted from science direct articles, sket...

solar radiation data play an important role in solar energy relevant researches. these data are not available for some locations due to the absence of the meteorological stations. therefore, solar radiation data have to be predicted by using solar radiation estimation models. this study presents an integrated artificial neural network (ann) approach for estimating solar radiation potential over...

abstract: this paper represents a novel use of artificial neural networks in medical science. the proposed technique involves training a multi layer perceptron (mlp) (a kind of artificial neural network) with a bp learning algorithm to recognize a pattern for the diagnosing and prediction of five blood disorders, through the results of blood tests from h1 machine. the blood test parameters and ...

Increasing efficiency in hospitals is of particular importance. Studies that combine data from multiple hospitals/data holders can tremendously improve the statistical outcome and aid in identifying efficiency markers. However, combining data from multiple sources for analysis poses privacy risks. A number of protocols have been proposed in the literature to address the privacy concerns; howeve...

aim : to determine the associated factors of reflux duration, using quantile regression model. background : reflux is one of the most prevalent gastrointestinal disorders.  statistical tools are widely used to assess the associated factors on reflux severity and frequency. patients and methods : a door to door questionnaire has been used to evaluate gastrointestinal symptoms including reflux an...