Forecasting financial markets is an important issue in finance area and research studies. On one hand, the importance of prediction, and on the other hand, its complexity, have led to huge number of researches which have proposed many forecasting methods in this area. In this study, we propose a hybrid model including Wavelet Transform, ARMA-GARCH and Artificial Neural Network (ANN) for single-...

abstract one of the techniques that has been used in different areas of sciences, and probably would be able to simulate complicated process of rainfall-run off is artificial neural network (ann). the purpose of this research is to evaluate the applicability of ann in rainfall- run off simulation and the comparison of the results to those produced by hec-hms model in azam river basin of herat i...

background: although experiments on water quality are time consuming and expensive, models are often employed as supplement to simulate water quality. artificial neural network (ann) is an efficient tool in hydrologic studies, yet it cannot predetermine its results in the forms of maps and geo-referenced data. methods: in this study, ann was applied to simulate groundwater quality and geographi...

In this work the electrooxidation half-wave potentials of some Benzoxazines were predicted from their structural molecular descriptors by using quantitative structure-property relationship (QSAR) approaches. The dataset consist the half-wave potential of 40 benzoxazine derivatives which were obtained by DC-polarography. Descriptors which were selected by stepwise multiple selection procedure ar...

This study shows the usefulness of Artificial Neural Network (ANN) in maintenance planning and man-agement. An ANN model based on the multi-layer perceptron having three hidden layers and four processing elements per layer was built to predict the expected downtime resulting from a breakdown or a maintenance activity. The model achieved an accuracy of over 70% in predicting the expected downtime.

reinforced concrete (rc) slabs exhibit complexities in their structural behavior under load due to the composite nature of the material and the multitude and variety of factors that affect such behavior. current methods for determining the load-deflection behavior of reinforced concrete slabs are limited in scope and are mostly dependable on the results of experimental tests. in this study, an ...

This research deals with the development and comparison of two data-driven models, i.e., Artificial Neural Network (ANN) and Adaptive Neuro-based Fuzzy Inference System (ANFIS) models for estimation of 28-day compressive strength of concrete for 160 different mix designs. These various mix designs are constructed based on seven different parameters, i.e., 3/4 mm sand, 3/8 mm sand, cement conten...

a sensitive and selective method using combination of two chemometrics methods, principal component analysis (pca) and artificial neural network (ann), and uv-visible spectroscopy has been developed for the determination of indomethacin (idm) in plasma samples . initially the absorbance spectra were processed using pca to noise reduction and data compression. the scores of these pcs were used a...

a novel approach to determine optimal sampling locations under parameter uncertainty in a water distribution system (wds) for the purpose of its hydraulic model calibration is presented. the problem is formulated as a multi-objective optimisation problem under calibration parameter uncertainty. the objectives are to maximise the calibrated model accuracy and to minimise the number of sampling d...