The objective of this study is to develop a method based on multivariate relevance vector regression (MVRVR) as a kernel-based Bayesian model for the estimation of above-ground biomass (AGB) in the Hyrcanian forests of Iran. Field AGB data from the Hyrcanian forests and multi-temporal PALSAR backscatter values are used for training and testing the methods. The results of the MVRVR method are th...

Epileptic seizures are manifestations of epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. As EEG signals are non-stationary, the conventional method of frequency ana...

Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning irrigation systems, risk management, readiness, alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) Co-Active Neuro-Fuzzy Inference System (CANFIS), regression, model including Multiple Linear Regression (MLR), were in...

A robot-based three-dimensional (3D) measurement system is presented. In the presented system, a structured light vision sensor is mounted on the arm of an industrial robot. Measurement accuracy is one of the most important aspects of any 3D measurement system. To improve the measuring accuracy of the structured light vision sensor, a novel sensor calibration approach is proposed to improve the...

abstract recent interest in describing the spatial distribution patterns of weeds through using interpolation methods has increased to estimate weed seedling density from spatially refferenced data and evaluation of applicable to site-specific weed management. in this research, a multi layer perceptron neural network (mlpnn) model was developed to predict the spatial distribution of h. glaucum ...

This research investigates the performance of dynamic modelling using non-parametric techniques for identification of a flexible structure system for development of active vibration control. In this paper, the implementation details are described and the experimental studies conducted in this research are analysed. The input–output data of the system were first acquired through the experimental...

Abstract Weirs are one of the most common hydraulic structures used in water engineering projects. In this research, a group method data handling (GMDH) was developed to estimate energy dissipation flow passing over labyrinth weirs with triangular and trapezoidal plans. To compare performance model other types soft computing models, multilayer perceptron neural network (MLPNN) developed. The di...

Abstract
 Nowadays, businesses' forecasts to meet the demands have become more critical. This study aimed predict fifteen-day order demand for an fulfillment center using a Multilayer Perceptron Neural Network (MLPNN). The dataset used in was created from real database of large Brazilian logistics company and thirteen variables. Linear Regression Coefficients (LRC) were as feature selectio...

The aim of this study is to show the artificial neural network (ANN) on classification of mineral based on color values of pixels. Twenty two images were taken from the thin sections using a digital camera mounted on the microscope and transmitted to a computer. Images, under both plane-polarized and cross-polarized light, contain maximum intensity. To select training and test data, 5-fold-cros...

Abstract In this research, water's surface elevation in compound channels with converging and diverging floodplains using soft computing models including the Multi-Layer Perceptron Neural Network (MLPNN), Group Method of Data Handling (GMDH), Neuro-Fuzzy (NF-GMDH) Support Vector Machine (SVM) was modeled predicted. For purpose, laboratory data published field were used. Parameters convergence a...