Early diagnosis of breast cancer is essential to save lives of patients. Usually, medical datasets include a large variety of data that can lead to confusion during diagnosis. The Knowledge Discovery on Database (KDD) process helps to improve efficiency. It requires elimination of inappropriate and repeated data from the dataset before final diagnosis. This can be done using any of the feature ...

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted under the assumption of mean squared error loss. Under this loss function optimal forecasts should be unbiased and forecast errors serially uncorrelated at the single period horizon with increasing variance as the forecast horizon grows. Using analytical results we show that standard properties of o...

Over the last few years, support vector machines (SVMs) have shown a great potential as classifiers for remotely sensed data. Generally, these have been used to perform conventional hard classification where each pixel is allocated to only one class. Remote sensing images, particularly at coarse spatial resolutions, are contaminated with mixed pixels that contain more than one class on the grou...

This paper reports on the empirical evaluation of five machine learning algorithm such as J48, BayesNet, OneR, NB and ZeroR using ten performance criteria: accuracy, precision, recall, F-Measure, incorrectly classified instances, kappa statistic, mean absolute error, root mean squared error, relative absolute error, root relative squared error. The aim of this paper is to find out which classif...

BACKGROUND The complexity of reactions and kinetic is the current problem of photodegradation processes. Recently, artificial neural networks have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the non-linear relationships between variables in complex systems. In this study, an artificial neural network was applied for modeling...

We consider the problem of estimating a measure of daily volatility from intermittent high-frequency data that are subject to market microstructure effects. We show that a simple Newey-West type modification of the realized variance (RV) yields an unbiased measure of volatility for the ‘open’ part of the day. The modified RV is unbiased even if 1-minute intra-day returns are used. Further, with...

The proposed method uses multi layered perceptron neural network and support vector machine to classify the normal subjects. Data used for training, testing and cross validation was recorded from normal persons (without any heart disease) within thirty six months, in the interval of 10/15 days. Ten hybrid features were extracted from the recorded signals by using wavelet transform. The classifi...

optical coherence tomography (oct) uses the spatial and temporal coherence properties of optical waves backscattered from a tissue sample to form an image. an inherent characteristic of coherent imaging is the presence of speckle noise. in this study we use a new ensemble framework which is a combination of several multi-layer perceptron (mlp) neural networks to denoise oct images. the noise is...