in this paper, the artificial neural network (ann) approach is applied for forecasting groundwater level fluctuation in aghili plain,southwest iran. an optimal design is completed for the two hidden layers with four different algorithms: gradient descent withmomentum (gdm), levenberg marquardt (lm), resilient back propagation (rp), and scaled conjugate gradient (scg). rain,evaporation, relative...

in this paper, the artificial neural network (ann) approach is applied for forecasting groundwater level fluctuation in aghili plain,southwest iran. an optimal design is completed for the two hidden layers with four different algorithms: gradient descent withmomentum (gdm), levenberg marquardt (lm), resilient back propagation (rp), and scaled conjugate gradient (scg). rain,evaporation, relative...

Granular computing is a computational paradigm in which a granule represents a structure of patterns evolved by performing operations on the individual patterns. Two granular neural networks are described for performing the pattern analysis tasks like classification and clustering. The granular neural networks are designed by integrating fuzzy sets and fuzzy rough sets with artificial neural ne...

In this paper, a Rough-Neuro hybrid methodology of the diagnostic process is proposed as a means to achieve accurate diagnosing of Erythemato-Squamous diseases. The methodology incorporates a two-stage hybrid mechanism. Rough sets Johnson Reducer for reduction of relevant attributes and artificial neural network Levenberg-Marquardt algorithm for the classification of the diseases. The model ach...

A method for constructing classiication (decision) systems is presented. The use of decision rules derived using rough set methods as new attributes is considered. Neural networks are applied as a tool for construction of classiier over reconstructed dataset. Possible profits of such an approach are brieey presented together with results of preliminary experiments.

Humans can easily recognize objects as complex as faces even if they have not seen them in such conditions before. We would like to find out computational basis of this ability. As an example of our approach we use the neurophysiological data from the visual system. In the retina and thalamus simple light spots are classified, in V1 oriented lines and in V4 simple shapes. The feedforward (FF) p...

Semantic labelling and instance segmentation are two tasks that require particularly costly annotations. Starting from weak supervision in the form of bounding box detection annotations, we propose to recursively train a convnet such that outputs are improved after each iteration. We explore which aspects affect the recursive training, and which is the most suitable box-guided segmentation to u...