An artificial neural network (NN) is a computer programmed model that attempts to mimic our understanding of the information processing and pattern matching that occurs in the brain. Our biological learning process centers around receiving certain input from our environment and shaping our response (the output) based upon positive and negative feedback supplied during our training. For example,...

Predicting terminals movements in mobile networks is useful for more than one reason, in particular for routing management. A way to do such prediction is to learn the movement patterns of mobile nodes passing by an access router. In this paper, the information (e.g. layer 2 measurements) related to the different paths followed by mobiles are learned using a hidden Markov model. Simulations hav...

In the past, conventional i-vectors based on a Universal Background Model (UBM) have been successfully used as input features to adapt a Deep Neural Network (DNN) Acoustic Model (AM) for Automatic Speech Recognition (ASR). In contrast, this paper introduces Hidden Markov Model (HMM) based ivectors that use HMM state alignment information from an ASR system for estimating i-vectors. Further, we ...

Method for visualization of learning processes for back propagation neural network is proposed. The proposed method allows monitor spatial correlations among the nodes as an image and also check a convergence status. The proposed method is attempted to monitor the correlation and check the status for spatially correlated satellite imagery data of AVHRR derived sea surface temperature data. It i...

Recently an incremental algorithm referred to as incremental extreme learning machine (I-ELM) was proposed by Huang et al. [G.-B. Huang, L. Chen, C.-K. Siew, Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks 17(4) (2006) 879–892], which randomly generates hidden nodes and then analytically determines the output weig...

This paper outlines the application of the multi-layer perceptron artificial neural network (ANN), ordinary kriging (OK), and inverse distance weighting (IDW) models in the estimation of local scour depth around bridge piers. As part of this study, bridge piers were installed with bed sills at the bed of an experimental flume. Experimental tests were conducted under different flow conditions an...

As demand for deployment and usage is increased in WLAN environment, achieving satisfactory throughput is one of the challenging issues. Initially as WLAN environment is data centric, the best effort delivery based protocol serve the purpose up to certain extent. With multimedia traffic protocol fails to deliver required traffic. The requirement of satisfactory network performance is delivery o...

In addition to a simple NN-based classifier developed and assessed in the cross-validation study (see the Systems and Methods section of the main body of the paper), we also developed a multistage protocol for enhanced prediction of transmembrane (TM) helices. For the final predictor we do not consider the MA-based representation, which is shown using cross-validation to yield a lower accuracy ...