Loss rate is one of the most important Quality of Service (QoS) requirements in a packet communication network carrying multimedia traac. This paper presents a method for estimating loss rates as a function of a feature vector, x, based on a maximum likelihood principle. Two backpropagation networks, a multi-layer perceptron (MLP) network and a radial basis function (RBF) network, are applied t...

The proper prognosis of treatment response is crucial in any medical therapy to reduce the effects disease and medication as well. mortality rate due hepatitis c virus (HCV) high Pakistan well all over world. During disease, prediction against particular medicine difficult. This paper focuses on predicting a drug: “L-ornithine L-Aspartate (LOLA)” patients. We have used various machine learning ...

This paper investigates the ability of several models of Support Vector Machines (SVMs) with alternate kernel functions to predict the probability of occurrence of Essential Hypertension (HT) in a mixed patient population. To do this a SVM was trained with 13 inputs (symptoms) from the medical dataset. Different kernel functions, such as Linear, Quadratic, Polyorder (order three), Multi Layer P...

the objective of this study is development of driver’s sleepiness using visually evoked potentials (vep). vep computed from eeg signals from the visual cortex. we use the steady state veps (ssveps) that are one of the most important eeg signals used in human computer interface systems. ssvep is a response to visual stimuli presented. we present a classification method to discriminate between cl...

Artificial Neural Network (ANN)-based diagnosis of medical diseases has been taken into great consideration in recent years. In this paper, two types of ANNs are used to classify effective diagnosis of Parkinson's disease. Multi-Layer Perceptron (MLP) with back-propagation learning algorithm and Radial Basis Function (RBF) ANNs were used to differentiate between clinical variables of sampl...

Interference in neural networks occurs when learning in one area of the input space causes unlearning in another area. These interference problems are especially prevalent in on-line applications where learning is directed by training data that is currently available rather than some optimal presentation schedule of the training data. We propose a procedure that enhances a learning algorithm by...

A general framework for minimal distance methods is presented. Radial Basis Functions (RBFs) and Multilayer Perceptrons (MLPs) neural networks are included in this framework as special cases. New versions of minimal distance methods are formulated. A few of them have been tested on a real-world datasets obtaining very encouraging results.

In the present study, prediction of Alumina recovery efficiency (A.R.E), the amount of produced red mud (A.P.R), red mud settling rate (R.S.R) and bound-soda losses (B.S.L) in Bayer process red mud has been carried out for the first time in the field. These predictions are based on Lime to bauxite ratio and chemical analyses of bauxite and lime as Bayer process feed materials. Radial basis func...

-------------------------------------------------------------------ABSTRACT---------------------------------------------------------------Prediction of rainfall for a region is of utmost importance for planning, design and management of irrigation and drainage systems. This can be achieved by different approaches such as deterministic, conceptual, stochastic and Artificial Neural Network (ANN)....