This paper proposes a fast block-matching algorithm that uses three fast matching error measures, besides the conventional mean-absolute error (MAE) or mean-square error (MSE). An incoming reference block in the current frame is compared to candidate blocks within the search window using multiple matching criteria. These three fast matching error measures are established on the integral project...

This paper focuses on the methodology used in applying the Time Series Data Mining techniques to financial time series data for calculating currency exchange rates of US dollars to Indian Rupees. Four Models namely Multiple Regression in Excel, Multiple Linear Regression of Dedicated Time Series Analysis in Weka, Vector Autoregressive Model in R and Neural Network Model using NeuralWorks Predic...

background: water is considered as the main source of life but water resources are limited and nonrenewable. different factors have caused groundwater to decrease. therefore, modeling and predicting groundwater level is of great importance. methods: monthly groundwater level data of about 20 years (october 1991 to february 2012) from the hamadan-bahar plain, west of iran were used based on peiz...

accurate prediction of municipal solid waste’s quality and quantity is crucial for designing and programming municipal solid waste management system. but predicting the amount of generated waste is difficult task because various parameters affect it and its fluctuation is high. in this research with application of feed forward artificial neural network, an appropriate model for predicting the...

PURPOSE To compare the accuracy of four different intraocular lens (IOL) power calculation formulas for eyes with mean keratometry values greater than 46 diopters (D). METHODS Forty five eyes from 45 patients who were candidates for senile cataract surgery with mean keratometry values greater than 46 D were included. Calculation of the IOL power was performed by the Lenstar. The implanted IOL...

The problem of fuzzy time series forecasting plays an important role in many scientific areas such as statistics and neural networks. While forecasting fuzzy time series, most of forecasting applications use the same length of intervals. The determination of length of intervals is significant and critical in fuzzy time series forecasting. The usage of convenient performance measure may also hav...

Forecasting is a systematic attempt to examine the future by inference from known facts. Sales forecasting is an ballpark figure of sales during a specified future period. Formerly, it was a manual process using the mathematical formulas. Due to the advent of computer the process of sale forecasting is fast and accurate. Machine learning, a subfield of Artificial Intelligence, has many algorith...

Alpha-galactosidase production in submerged fermentation by Acinetobacter sp. was optimized using feed forward neural networks and genetic algorithm (FFNN-GA). Six different parameters, pH, temperature, agitation speed, carbon source (raffinose), nitrogen source (tryptone), and K2HPO4, were chosen and used to construct 6-10-1 topology of feed forward neural network to study interactions between...

This study investigated the predictive ability of neural networks in the estimation of methane yield (MY) and effluent substrate (Se as mg/L COD) produced by two anaerobic filters, one mesophilic (35°C) and one thermophilic (55°C), which were operated to treat paper-mill wastewater at varying organic loadings. An artificial neural network (ANN) architecture was optimized to obtain a three-layer...

Accurate electricity price forecasting plays an important role in the profits of electricity market participants and the healthy development of electricity market. However, the electricity price time series hold the characteristics of volatility and randomness, which make it quite hard to forecast electricity price accurately. In this paper, a novel hybrid model for electricity price forecastin...