Anupam Tarsauliya

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Financial forecasting has been challenging problem due to its high non-linearity and high volatility. An Artificial Neural Network (ANN) can model flexible linear or non-linear relationship among variables. ANN can be configured to produce desired set of output based on set of given input. In this paper we attempt at analyzing the usefulness of artificial(More)
Financial time series forecast has been classified as standard problem in forecasting due to its high non-linearity and high volatility in data. Statistical methods such as GARCH, GJR, EGARCH and Artificial Neural Networks (ANNs) based on standard learning algorithms such as backpropagation have been widely used for forecasting time series volatility of(More)
Financial time series has been standard complex problem in the field of forecasting due to its non-linearity and high volatility. Though various neural networks such as backpropagation, radial basis, recurrent and evolutionary etc. can be used for time series forecasting, each of them suffer from some flaws. Performances are more varied for different time(More)
Forecasting in financial market has been difficult task due to its non linearity and high volatility. Effective modeling of forecasting is a major concern for financial market participants. Artificial Neural Network (ANN) is a statistical technique under the non linear regression model. ANNs do consider the non parametric aspects like semantics, emotions(More)
Face and Lip recognition has been benchmark problems in the field of biometrics and image processing. Various Artificial neural networks have been used for recognition purpose. This paper attempts at improving the recognition result for individuals by fusion of facial parts and lip using modular artificial neural network which employs parallel local experts(More)
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