Wood density is a key indicator for tree functionality and end utilization. Appropriate chemometric methods play an important role in the successful prediction of wood by visible near infrared (Vis-NIR) spectroscopy. The objective this study was to select appropriate pre-processing, variable selection multivariate calibration techniques improve accuracy Chinese white poplar (Populus tomentosa c...

In this paper, researchers estimated the stock price of activated companies in Tehran (Iran) stock exchange. It is used Linear Regression and Artificial Neural Network methods and compared these two methods. In Artificial Neural Network, of General Regression Neural Network method (GRNN) for architecture is used. In this paper, first, researchers considered 10 macro economic variables and 30 fi...

Data Mining is study of how to determine underlying patterns in the data. Data mining techniques like machine learning, alongside the conventional methods are deployed. Different Data mining techniques like GRNN, MLP, NNARX, CART, RBF, ARIMA and so on are used for the prediction of Rainfall. In this paper, analysis of various algorithms of data mining is used for rainfall prediction model. It i...

To the shortcoming of general particle filter, an improved algorithm based on neural network is proposed and is shown to be more efficient than the general algorithm in the same sample size. The improved algorithm has mainly optimized the choice of importance density. After receiving the samples drawn from prior density, and then adjust the samples with general regression neural network (GRNN),...

In this work, we present the Grounded Recurrent Neural Network (GRNN), a recurrent neural network architecture for multi-label prediction which explicitly ties labels to specific dimensions of the recurrent hidden state (we call this process “grounding”). The approach is particularly well-suited for extracting large numbers of concepts from text. We apply the new model to address an important p...

Remote patient tracking has recently gained increased attention, due to its lower cost and non-invasive nature. In this paper, the performance of Support Vector Machines (SVM), Least Square Support Vector Machines (LS-SVM), Multilayer Perceptron Neural Network (MLPNN), and General Regression Neural Network (GRNN) regression methods is studied in application to remote tracking of Parkinson’s dis...

Diabetes has become a major threat to the life and it is getting common day by day and is having a fast increasing trend .Unhealthy practices in consumption of food have on a major side contributed to the rise of type 2 diabetes. In this paper we have tried to develop a method for the prediction of type 2 diabetes using adaptive neuro-fuzzy interface system (ANFIS) with genetic algorithms (GA)....

This paper presents the application of neural networks in software maintainability estimation using objectoriented metrics. Maintenance effort can be measured as the number of lines changed per class. In this paper, the number of lines changed per class (modification volume) is predicted using Ward neural network and General Regression neural network (GRNN). Object-oriented design metrics conce...

In the research of heat transfer, exchanger plays an important role in highly integrated and high-precision thermal management to ensure balance. previous studies, traditional experiments CFD simulations consume lots time computational resources, while transfer correlations have large errors. Hence, this aims establish a reliable method predict Heat Transfer Coefficient (HTC) exchange channels ...

The growth in electricity consumption the world forces countries to have a well-structured planning relation forecasting demand for their most diverse sectors. Several techniques are used predict electrical loads, such as artificial intelligence models, statistical models and hybrid models. This work aims present model based on combination of method, SARIMA, an neural network, GRNN, improve acc...