Listen, Attend and Spell (LAS) network is one of the end-to-end approaches for speech recognition, which does not require an explicit language model. It consists two parts; encoder part receives acoustic features as inputs, decoder produces character at a time step, based on output attention mechanism. Multi-layer recurrent neural networks (RNN) are used in both parts. Hence, LAS architecture c...

Concepts with defining features have sufficiently rigid boundaries that examples of one concept are rarely confused with another concept, whereas probabilistic concepts have vague boundaries, producing frequent misclassifications. It has been argued that, although neural learning might account for probabilistic (or fuzzy) concepts, it is incapable of accounting for acquisition of concepts with ...

accurate estimation of evaporation is important for design, planning and operation of water systems. in arid zones where water resources are scarce, the estimation of this loss becomes more interesting in the planning and management of irrigation practices. this paper investigates the ability of artificial neural networks (anns) technique to improve the accuracy of daily evaporation estimation....

background: we designed an algorithmic model based on the logistic regression analysis and a non-algorithmic model based on the artificial neural network (ann). materials and methods: the ability of these models was compared together in clinical application to differentiate malignant from benign breast tumors in a study group of 161 patients' records. each patient’s record consisted of 6 s...

this paper presents the prediction of vehicle's velocity time series using neural networks. for this purpose, driving data is firstly collected in real world traffic conditions in the city of tehran using advance vehicle location devices installed on private cars. a multi-layer perceptron network is then designed for driving time series forecasting. in addition, the results of this study a...

the paper deals with data envelopment analysis (dea) and artificial neural network (ann). we believe that solving for the dea efficiency measure, simultaneously with neural network model, provides a promising rich approach to optimal solution. in this paper, a new neural network model is used to estimate the inefficiency of dmus in large datasets.

in general, amount of sludge will definitely increase in near future and composting processes, optimum composting conditions and compost use as fertilizer and soil amendment will then be significant research topics. the present study was conducted for o2 parameter estimation by multiple regression and artificial neural networks methods. daily temperature, ch4, h2s, co2 and o2 measurements were ...

today, stock investment has become an important mean of national finance. apparently, it is significant for investors to estimate the stock price and select the trading chance accurately in advance, which will bring high return to stockholders. in the past, long-term trading processes and many technical analysis methods for stock market were put forward. however, stock market is a nonlinear sys...

in recent decades artificial neural networks (anns) have shown great ability in modeling and forecasting non-linear and non-stationary time series and in most of the cases especially in prediction of phenomena have showed very good performance. this paper presents the application of artificial neural networks to predict drought in yazd meteorological station. in this research, different archite...

This paper discusses an efficient approach to captioning a given image using combination of Convolutional Neural Network (CNN) and Recurrent Networks (RNN) with Long Short Term Memory Cells (LSTM). Image is realm deep learning computer vision which deals generating relevant captions for input image. The research in this area includes the hyperparameter tuning generate are as accurate possible. ...