The goal of this research is to explore whether network structures common in social networks also emerge in online distance learning networks. We compare the observed values of reciprocity and transitivity in 95 online distance learning networks and 40 social networks with predictions of Random Graph models. All the networks tested exhibit reciprocities that significantly deviate from the predi...

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 ...

this paper presents an accurate differential global positioning system (dgps) using multi-layered neural networks (nns) based on the back propagation (bp) and imperialistic competition algorithm (ica) in order to predict the dgps corrections for accurate positioning. simulation results allowed us to optimize the nn performance in term of residual mean square error. we compare results obtained b...

this study deals with teaching the verbal meaning networks in second/foreign language, and evaluates the effects of this kind of teaching on the learning process of foreign persian language learners. for this purpose, a pretest was carried out among 50 foreign language learners, from which 30 individuals were selected for this research based on their scores. the subjects were divided into two g...

The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing generators learn to “linearize semantics” in the latent space of such models. Intuitively, such latent spaces may serve as useful feature representation...