In this project we present the concept of effective classification for background subtracted videos by using learning classifier-feed forward neural network with back propagation to conquer the open problem in the context of the complex scenarios.eg:while picturing the videos in some application like cloudy (or) misty areas the object in video will be less clarity with naked eye even after the ...

The back-propagation (BP) training scheme is widely used for training network models in cognitive science besides its well known technical and biological short-comings. In this paper we contribute to making the BP training scheme more acceptable from a biological point of view in cognitively motivated prediction tasks overcoming one of its major drawbacks. Traditionally, recurrent neural networ...

Card games are interesting for many reasons besides their connection with gambling. Bridge is being a game of imperfect information, it is a well defined, decision making game. The estimation of the number of tricks to be taken by one pair of bridge players is called Double Dummy Bridge Problem (DDBP). Artificial Neural Networks are Non – Linear mapping structures based on the function of the h...

A single layer feed forward neural network algorithm using back propagation, gradient descent and weight decay is proposed for the purpose of wind speed forecasting using only the observed hourly wind speeds, directions, temperatures and pressures observed at at a single site. The site data used for this experiment was 10 years worth of hourly ASOS data from the Bismarck North Dakota Regional A...

This paper deals with artificial neural network (ANN) architecture, the multilayer Feed-forward (MLFF) network with back propagation learning. The training of an artificial neural network involves two passes. In the forward pass, the input signals propagate from the network input to the output. In the reverse pass the calculated error signals propagate backwards through the network where they a...

The recurrent neural network is a feed-forward network ascribed to a parent neural network with feed-back connections (or in another term, oriented cycles). Its adaptation is performed by an analog of the standard back-propagation adaptation method. The recurrent neural network approach is illustrated by prediction and classification of 13C NMR chemical shifts in a series of monosubstituted ben...

Rivers are dynamic geological agents on the earth which transport weathered materials of continent to sea. Estimation suspended sediment yield (SSY) is essential for management, planning, and designing in any river basin system. SSY critical due its complex nonlinear processes, not captured by conventional regression methods. Rainfall, temperature, water discharge, SSY, rock type, relief, catch...

Real-world traffic situations, including smart monitoring, automated parking systems, and car services are increasingly using vehicle license detection systems (VLDS). Vehicle plate identification is still a challenge with current approaches, particularly in more complicated settings. The use of machine learning deep algorithms, which display improved classification accuracy resilience, has bee...

In this paper is presented an investigation of the speech recognition classification performance. This investigation on the speech recognition classification performance is performed using two standard neural networks structures as the classifier. The utilized standard neural network types include Feed-forward Neural Network (NN) with back propagation algorithm and a Radial Basis Functions Neur...