the biologists now face with the masses of high dimensional datasets generated from various high-throughput technologies, which are outputs of complex inter-connected biological networks at different levels driven by a number of hidden regulatory signals. so far, many computational and statistical methods such as pca and ica have been employed for computing low-dimensional or hidden representat...

Neural Networks (NN) hold the potential for improving a variety of tasks in remote sensing and image processing. They represent a different approach to problems, as they do not rely on statistical relationships. Instead, neural networks adaptively estimate continuous functions from data without specifying mathematically how outputs depend on inputs. This paper evaluates the effect of metrics se...

The field of neurocontrol, in which neural networks are used for control of complex systems, has many potential applications. One of the biggest hurdles to developing neurocontrollers is the difficulty in establishing good training data for the neural network. We propose a hybrid approach to the development of neurocontrollers that employs both evolutionary computation (EC) and neural networks ...

The objective of this work was to employ artificial neural networks (NN) to solve inverse problems in different engineering fields, overcoming various obstacles in applying NN to different problems and benefiting from the experience of solving different types of inverse problems. The inverse problems investigated are: 1) damage detection in structures, 2) detection of an anomaly in a light-diff...

This paper examines the circuit complexity of feedforward neural networks having sigmoid activation function. The starting point is the complexity class NN defined in [18]. First two additional complexity classes NN∆ k and NN∆,ε k having less restrictive conditions (than NN) concerning fan-in and accuracy are defined. We then prove several relations among these three classes and well establishe...

We present NN-grams, a novel, hybrid language model integrating n-grams and neural networks (NN) for speech recognition. The model takes as input both word histories as well as n-gram counts. Thus, it combines the memorization capacity and scalability of an n-gram model with the generalization ability of neural networks. We report experiments where the model is trained on 26B words. NN-grams ar...

In this paper we propose the image segmentation incthod by the neural networks (NN) to extract text, continuous-tone and screened-halftone region in the document image. Each feature extractor for text, continuons-tone and screened-halftone is composed of the 3-layer NN which is upper part of 5-layer lio~irglass model NN; each hourglass model NN is trained by different learning samples which spe...

The mixture of two already known soft computing techniques, like Genetic Algorithms and Neural Networks (NN) in Financial modeling, takes a new approach in the search for the best variables involving an Econometric model using a Neural Network. This new approach helps to recognize the importance of an economic variable among different variables regarding econometric modeling. A Genetic algorith...

Deep Neural Networks are becoming increasingly popular in always-on IoT edge devices performing data analytics right at the source, reducing latency as well as energy consumption for data communication. This paper presents CMSIS-NN, efficient kernels developed to maximize the performance and minimize the memory footprint of neural network (NN) applications on Arm Cortex-M processors targeted fo...