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...

3 Introduction 3 Materials and Methods 5 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Deep Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Deep Neural Network Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Experimental Setup . . . . ...

Mass detection from mammogram plays an crucial role as a pre-processing stage for mass segmentation and classification. In this paper, we present a novel approach for detecting masses from mammograms using a cascade of deep learning and random forest classifiers. The deep learning classifier consists of a multi-scale deep belief network classifier that selects regions to be further processed by...

In this paper, we present a new i-vector based speaker adaptation method for automatic speech recognition with deep neural networks, focusing on in-vehicle scenarios. Our proposed method is, rather than augmenting i-vectors to acoustic feature vectors to form concatenated input vectors for adapting neural network acoustic model parameters, is to perform featurespace transformation with smaller ...

Features play crucial role in the performance of classifier for object detection from high-resolution remote sensing images. In this paper, we implemented two types of deep learning methods, deep convolutional neural network (DNN) and deep belief net (DBN), comparing their performances with that of the traditional methods (handcrafted features with a shallow classifier) in the task of aircraft ...

To what extent is the success of deep visualization due to the training? Could we do deep visualization using untrained, random weight networks? To address this issue, we explore new and powerful generative models for three popular deep visualization tasks using untrained, random weight convolutional neural networks. First we invert representations in feature spaces and reconstruct images from ...