Composites based on recycled high density polyethylene (RHDPE) and natural fibers were made through melt blending and compression molding. The effects of the fibers (wood and bagasse) and coupling agent type/concentration on the composite properties were studied. The use of maleated polyethylene (MAPE), carboxylated polyethylene (CAPE), and titanium-derived mixture (TDM) improved the compatibil...

The nature of consumer products causes the difficulty in forecasting the future demands and the accuracy of the forecasts significantly affects the overall performance of the supply chain system. In this study, two data mining methods, artificial neural network (ANN) and support vector machine (SVM), were utilized to predict the demand of consumer products. The training data used was the actual...

Predicting future sales is intended to control the number of existing stock, so the lack or excess stock can be minimized. When the number of sales can be accurately predicted, then the fulfillment of consumer demand can be prepared in a timely and cooperation with the supplier company can be maintained properly so that the company can avoid losing sales and customers. This study aims to propos...

We present new approaches for 5-minute ahead electricity load forecasting. They were evaluated on data from the Australian electricity market operator for 2006-2008. After examining the load characteristics using autocorrelation analysis with 4-week sliding window, we selected 51 features. Using this feature set with linear regression and support vector regression we achieved an improvement of ...

In order to determine if a popular summary statistic, the mean absolute percentage error (MAPE), is a valid measure of forecast error for the Census Bureau=s 1995 to 2000 state population projections, statistical tests and graphs were used to determine if the error distribution is strongly influenced by outliers. It was found that the absolute percentage error distribution is skewed and asymmet...

This paper presents a scheme using Differential Evolution based Functional Link Artificial Neural Network (FLANN) to predict the Indian Stock Market Indices. The Model uses Back-Propagation (BP) algorithm and Differential Evolution (DE) algorithm respectively for predicting the Stock Price Indices for one day, one week, two weeks and one month in advance. The Indian stock prices i.e. BSE (Bomba...

in this paper, the prediction of future stock close price of SENSEX & NSE stock exchange is found using the proposed Hybrid ANN model of Functional Link Fuzzy Logic Neural Model. The historic raw data’s of SENSEX & NSE stock exchange has been pre-processed to the range of (0 to 1). After pre-processing the inputs and forwarded to functional expansion function to perform neural operation. The ac...