Scene classification in very high-resolution (VHR) remote sensing (RS) images is a challenging task due to the complex and diverse content of images. Recently, convolution neural networks (CNNs) have been utilized tackle this task. However, CNNs cannot fully meet needs scene clutters small objects VHR To handle these challenges, letter presents novel multilevel feature fusion (MLFF) network wit...

In industrial machine learning pipelines, data often arrive in parts. Particularly in the case of deep neural networks, it may be too expensive to train the model from scratch each time, so one would rather use a previously learned model and the new data to improve performance. However, deep neural networks are prone to getting stuck in a suboptimal solution when trained on only new data as com...

This work attempts to apply an artificial neural network in order to estimate the critical flashover voltage on polluted insulators. The artificial eural network uses as input variables the following characteristics of the insulator: diameter, height, creepage distance, form factor and equivalent alt deposit density, and estimates the critical flashover voltage. The data used to train the netwo...

Despite the state-of-the-art accuracy of Deep Neural Networks (DNN) in various classification problems, their deployment onto resource constrained edge computing devices remains challenging due to their large size and complexity. Several recent studies have reported remarkable results in reducing this complexity through quantization of DNN models. However, these studies usually do not consider ...

This paper deals with artificial neural network (ANN) architecture, the multilayer Feed-forward (MLFF) network with back propagation learning. The training of an artificial neural network involves two passes. In the forward pass, the input signals propagate from the network input to the output. In the reverse pass the calculated error signals propagate backwards through the network where they a...

We present a novel generic approach to the problem of Event Related Potential identification and classification, based on a competitive N eural Net architecture. The network weights converge to the embedded signal patterns, resulting in the formation of a matched filter bank. The network performance is analyzed via a simulation study, exploring identification robustness under low SNR conditions...

In this paper we introduce a new approach for wireless sensor network power management which is based on eural etworks. In this new approach an intelligent analysis is used to process the structure of a wireless sensor network (WS ) and produce some information which can be used to improve the performance of WS s’ management application. We applied our intelligent method to our previously propo...

The experimental results showed that the proposed method efficiently classifies heart sounds. Heart sound analysis is a b asic method for heart examination, w hich m ay s uggest t he pr esence of a c ardiac pa thology a nd a lso provide diagnostic information. In this study, a novel feature extraction method based on Independent Component Analysis is applied to classify nine different heart sou...

E(pithelial)-cadherin and N(eural)-cadherin are transmembrane cell-cell adhesion molecules, belonging to the subfamily of classical cadherins. The expression of E- and N-cadherin is spatiotemporally regulated and associated with a variety of normal morphogenetic events. The expression of E- and N- cadherin is also involved in carcinogenesis. E-cadherin functions as a tumor-suppressor. N-cadheri...