In this paper we are working on the Neural Network based classifier that solves the classification problem. The paper describes the multilayer perception approach to describe the neural network architecture. For this classifier we use the Fisher’s Iris Database (Fisher, 1936) available in MATLAB and on the Internet. This database is the preprocessed and is the best database in the pattern recog...

The class of mapping networks is a general family of tools to perform a wide variety of tasks; however, no unifying framework exists to describe their theoretical and practical properties. This paper presents a standardized, uniform representation for this class of networks, and introduces a simple modification of the multilayer perceptron with interesting practical properties, especially well ...

This paper deals with a method for removing a ghost target that is not a real object from the output of a multiple object-tracking algorithm. This method uses an artificial neural network (multilayer perceptron) and introduces a structure, learning, verification, and evaluation method for the artificial neural network. The implemented system was tested at an intersection in a city center. Resul...

Recently the ‘Million Song Dataset’, containing audio features and metadata for one million songs, was made available. In this paper, we build a convolutional network that is then trained to perform artist recognition, genre recognition and key detection. The network is tailored to summarize the audio features over musically significant timescales. It is infeasible to train the network on all a...

In this paper, simulated annealing guided traingularized encryption using multilayer perceptron generated session key (SATMLP) has been proposed for secured wireless communication. Both sender and receiver station uses identical multilayer perceptron and depending on the final output of the both side multilayer perceptron, weights vector of hidden layer get tuned in both ends. After this tunnin...

This work studies the entity-wise topical behavior from massive network logs. Both the temporal and the spatial relationships of the behavior are explored with the learning architectures combing the recurrent neural network (RNN) and the convolutional neural network (CNN). To make the behavioral data appropriate for the spatial learning in CNN, several reduction steps are taken to form the topi...

This paper investigates the efficiency of in-door next location prediction by comparing several prediction methods. The scenario concerns people in an office building visiting offices in a regular fashion over some period of time. We model the scenario by a dynamic Bayesian network and evaluate accuracy of next room prediction and of duration of stay, training and retraining performance, as wel...

Various theoretical results show that learning in conventional feedforward neural networks such as multilayer perceptrons is NP-complete. In this paper we show that learning in min-max modular (M3) neural networks is tractable. The key to coping with NP-complete problems in M3 networks is to decompose a large-scale problem into a number of manageable, independent subproblems and to make the lea...

Electroencephalography is a technique for recording the brain’s electrical activity – the electroencephalogram or EEG. It is an important procedure in the diagnosis of several brain disorders, as well being a valuable physiological tool for studies of normal brain function. However, the EEG is often contaminated by numerous artefacts such as eye-blinks, muscle activities, and eye-movements. Thi...

Consider a multilayer perceptron (MLP) with d inputs, a single hidden sigmoidal layer and a linear output. By adding an additional d inputs to the network with values set to the square of the rst d inputs, properties reminiscent of higher-order neural networks and radial basis function networks (RBFN) are added to the architecture with little added expense in terms of weight requirements. Of pa...