In most of previous works on neural network based language models (NNLMs), the words are represented as 1-of-N encoded feature vectors. In this paper we investigate an alternative encoding of the word history, known as bag-of-words (BOW) representation of a word sequence, and use it as an additional input feature to the NNLM. Both the feedforward neural network (FFNN) and the long short-term me...

the goal of this study was to predict the moisture content of paddy using machine vision and artificial neural networks (anns). the grains were dried as thin layer with air temperatures of 30, 40, 50, 60, 70, and 80°c and air velocities of 0.54, 1.18, 1.56, 2.48 and 3.27 ms-1. kinetics of l*a*b* were measured. the air temperature, air velocity, and l*a*b* values were used as ann inputs. the res...

The differential diagnosis of erythemato-squamous diseases is a difficult problem in dermatology. Artificial Neural Network (ANN) classifies the given samples when trained and nearly 98 % classification accuracy is achieved. Generalized Feed Forward Neural Network (FFNN) can solve the multivariable classification problem of determination of skin disease. ANN approach is studied to determine the...

Abstract: This paper presents a dual neural network based technique for detecting and classifying the power quality disturbances. In the proposed method, Adaptive Linear Neural Network is used to extract the rms voltage for harmonics and Interharmonics estimations. With the help of these indices, PQ disturbances such as Sag, Swell, Outages are detected and classified, Harmonics and Interharmoni...

In this paper, optimal control for stochastic nonlinear singular system with quadratic performance is obtained using neural networks. The goal is to provide optimal control with reduced calculus effort by comparing the solutions of the matrix Riccati differential equation (MRDE) obtained from the well-known traditional Runge–Kutta (RK) method and nontraditional neural network method. To obtain ...

production of highly viscous tar sand bitumen using steam assisted gravity drainage (sagd) with a pair of horizontal wells has advantages over conventional steam flooding. this paper explores the use of artificial neural networks (anns) as an alternative to the traditional sagd simulation approach. feed forward, multi-layered neural network meta-models are trained through the back-error-propaga...

EEG analysis aims to help scientists better understand the brain, physicians diagnose and treatment choices of brain-computer interface. Artificial neural networks are among most effective learning algorithms perform computing tasks similar biological neurons in human brain. In some problems, network model's performance might significantly degrade overfit due irrelevant features that negatively...

In this study, applicability of Artificial Neural Network (ANN) methods for river flow estimation is investigated. Accurate modeling and forecasting of hydrological processes such as rainfall, rainfall-run off relationship, runoff, is important for management and planning of water resources. To illustrate the capability of ANN for modeling of water resources, Great Menderes River, locate in the...

Aerodynamic parameter estimation is critical in the aviation sector, especially design and development programs of defense-military aircraft. In this paper, new results application Artificial Neural Networks (ANN) to field aircraft are presented. The performances Feedforward Network (FFNN) with Backpropagation FFNN using Recursive Least Square (RLS) investigated for aerodynamic estimation. meth...

Sentence boundary identification is an important step for text processing tasks, e.g., machine translation, POS tagging, text summarization etc., in this paper, we present an approach comprising of Feed Forward Neural Network (FFNN) along with part of speech information of the words in a corpus. Proposed adaptive system has been tested after training it with varying sizes of data and threshold ...