In the dynamic global economy, the accuracy in forecasting the foreign currency exchange rates is of crucial importance for any future investment. The use of computational intelligence based techniques for forecasting has been proved extremely successful in recent times. The aim of this study is to identify a neural network model which has ability to predict the US Dollar against Sri Lankan Rup...

Direct Sequence Code Division Multiple Access (DS-CDMA) is a schemewhere several users transmit their data simultaneously over common wireless communication channel,by spreading each by distinct codes. At the receiver, individual are detected appropriate decoding. In this paper, new smart receiver proposed for detecting DS-CDMA signals based on multi-layer Feed Forward Neural Network (FFNN). Th...

Abstract The present study uses a wavelet-based clustering technique to identify spatially homogeneous clusters of groundwater quantity and quality data select the most effective input for feed-forward neural network (FFNN) model predict level (GL), pH HCO3? in groundwater. In second stage this methodology, first, GL, time series different piezometers were de-noised using threshold-based wavele...

Proper analysis of building energy performance requires selecting appropriate models for handling complicated calculations. Machine learning has recently emerged as a promising effective solution solving this problem. The present study proposes novel integrative machine model predicting two parameters residential buildings, namely annual thermal demand (DThE) and weighted average discomfort deg...

This work addresses an efficient neural network (NN) representation for the phase-field modeling of isotropic brittle fracture. In recent years, data-driven approaches, such as networks, have become active research field in mechanics. this contribution, deep networks—in particular, feed-forward (FFNN)—are utilized directly development failure model. The verification and generalization trained m...

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neura...

Video-based face recognition is a very challenging problem as there is a variation in resolution, illumination, pose, facial expressions and occlusion. In this paper, we have presented an approach for resolution variation video-based face recognition system using the combination of local binary pattern (LBP), principal component analysis (PCA) and feed forward neural network (FFNN). We used, st...

Feed Forward Neural Networks (FFNNs) are computational techniques inspired by the physiology of the brain and used in the approximation of general mappings from one nite dimensional space to another. They present a practical application of the theoretical resolution of Hilbert's 13 th problem by Kolmogorov and Lorenz, and have been used with success in a variety of applications. However, as the...

The present study applies two different machine learning (ML) algorithms to predict the stress-strain mapping for non-linear behaviour of thermoplastic materials: a Long Short-Term Memory (LSTM) algorithm and Feed-Forward Neural Network (FFNN). approach this work requires generation curve specific material parameters. training data are obtained from von Mises law Ramberg-Osgood equation. four c...