in this paper, a model based on gmdh type neural network, is used to predict gas price in the spot market while using oil spot market price, gas spot market price, gas future market price, oil future market price and average temperature of the weather. the results suggest that gmdh neural network model, according to the root mean squared error (rmse) and direction statistics (dstat) statistics ...

In this paper, an Arabic letter recognition system based on Artificial Neural Networks (ANNs) and statistical analysis for feature extraction is presented. The ANN is trained using the Least Mean Squares (LMS) algorithm. In the proposed system, each typed Arabic letter is represented by a matrix of binary numbers that are used as input to a simple feature extraction system whose output, in addi...

Among the levers carried in era of Industry 4.0, there is that using Artificial Intelligence models to serve energy interests industrial companies. The aim this paper estimate active electrical power generated by units self-produce electricity. To do this, we conduct a case study historical data variables influencing parameter support construction three analytical based on Deep Learning algorit...

Ultra-wideband (UWB) time difference of arrival (TDOA)-based localization has recently emerged as a promising indoor positioning solution. However, in cluttered environments, both the UWB radio positions and obstacle-induced non-line-of-sight (NLOS) measurement biases significantly impact quality position estimate. Consequently, placement radios must be carefully designed to provide satisfactor...

We analyze two communication-efficient algorithms for distributed optimization in statistical settings involving large-scale data sets. The first algorithm is a standard averaging method that distributes the N data samples evenly to m machines, performs separate minimization on each subset, and then averages the estimates. We provide a sharp analysis of this average mixture algorithm, showing t...

The problem of estimating a random signal vector x observed through a linear transformation H and corrupted by an additive noise is considered. A linear estimator that minimizes the mean squared error (MSE) with a certain selected probability is derived under the assumption that both the additive noise and random signal vectors are zero mean Gaussian with known covariance matrices. Our approach...

In this paper, we showed a method to forecast the daily stock price using neural networks and the result of the Neural Network forecast is compared with the Statistical forecasting result. Stock price prediction is one of the emerging field in neural network forecasting area. This paper also presents the Neural Networks ability to forecast the daily Stock Market Prices. Stock market prediction ...

An approach was evaluated for the retrieval of soil moisture of bare soil surface using bistatic scatterometer data in the angular range of 200 to 700 at VVand HHpolarization. The microwave data was acquired by specially designed X-band (10 GHz) bistatic scatterometer. The linear regression analysis was done between scattering coefficients and soil moisture content to select the suitable incide...

A convolutional network architecture termed sparse convolutional neural network (SCNN) is proposed and tested on a real-world classification task (car classification). In addition to the error function based on the mean squared error (MSE), approximate decorrelation between hidden layer neurons is enforced by a weight orthogonalization mechanism. The aim is to obtain a sparse coding of the obje...

Abstract The purpose of this paper is to evaluate the performance spline interpolation method in predicting and mapping concentration Total Suspended Solids (TSS) surface water Pulau Tuba, Kedah. Thirty sampling points were set up geolocated using Geographic Positioning System (GPS). Gravimetric analyses used determine TSS level. Fifty percent total randomly chosen for developing spatial models...