Mohammed Awad

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Introduced species offer unique opportunities to study evolution in new environments, and some provide opportunities for understanding the mechanisms underlying macroecological patterns. We sought to determine how introduction history impacted genetic diversity and differentiation of the house sparrow (Passer domesticus), one of the most broadly distributed(More)
Epigenetic mechanisms impact several phenotypic traits and may be important for ecology and evolution. The introduced house sparrow (Passer domesticus) exhibits extensive phenotypic variation among and within populations. We screened methylation in populations from Kenya and Florida to determine if methylation varied among populations, varied with(More)
Patients with medial temporal lobe damage and diencephalic damage were compared on two tests of verbal temporal order memory: between-list discrimination and within-list discrimination. Both patient groups were impaired relative to a group of healthy control participants. In addition, despite comparable levels of item recognition, the diencephalic group was(More)
In this paper, the multigrid-based fuzzy system (MGFS) approach is applied for the CATS time series prediction benchmark. The MGFS architecture overcomes the problem inherent to all grid-based fuzzy systems when dealing with high dimensional input data, thus keeping low computational cost and high performance. A greedy algorithm for MGFS structure(More)
In this paper, we deal with the problem of time series prediction from a given set of input/output data. This problem consists of the prediction of future values based on past and/or present data. We present a new method for prediction of time series data using radial basis functions. This approach is based on a new efficient method of clustering of the(More)
In this paper we make use of a modified Grid Based Fuzzy System architecture, which may provide an exponential reduction in the number of rules needed. We also introduce an algorithm that automatically, from a set of given I/O training points, is able to determine the pseudo-optimal architecture proposed as well as the optimal parameters needed (number and(More)
In this paper, we deal with the problem of function approximation from a given set of input/output data. This problem consists of analyzing these training examples so that we can predict the output of the model given new inputs. We present a new method for function approximation of the I/O data using radial basis functions (RBFs). This approach is based on(More)