Probabilistic Neural Network

In this work, Classical Turkish Music songs are classified into six makams. Makam is a modal framework for melodic development in Classical Turkish Music. The effect of the sound clip length on the system performance was also evaluated. The Mel Frequency Cepstral Coefficients (MFCC) were used as features. Obtained data were classified by using Probabilistic Neural Network. The best correct recognition ratio was obtained as 89,4% by using a clip length of 6 s.

In this paper, wavelets and fuzzy support vector machines are used to automated detect and classify power quality (PQ) disturbances. Electric power quality is an aspect of power engineering that has been with us since the inception of power systems. The types of concerned disturbances include voltage sags, swells, interruptions, switching transients, impulses, flickers, harmonics, and notches. Fourier transform and wavelet analysis are utilized to denoise the digital signals, to decompose the signals and then to obtain eight common features for the sampling PQ disturbance signals. A fuzzy support vector machines is designed and trained by 8-dimension feature space points for making a decision regarding the type of the disturbance. Simulation cases illustrate the effectiveness.

A study is presented to compare the performance of three types of artificial neural network (ANN), namely, multi layer perceptron (MLP), radial basis function (RBF) network and probabilistic neural network (PNN), for bearing fault detection. Features are extracted from time domain vibration signals, without and with preprocessing, of a rotating machine with normal and defective bearings. The extracted features are used as inputs to all three ANN classifiers: MLP, RBF and PNN for two- class (normal or fault) recognition. Genetic algorithms (GAs) have been used to select the characteristic parameters of the classifiers and the input features. For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions. The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine. The roles of different vibration signals and preprocessing techniques are investigated. The results show the effectiveness of the features and the classifiers in detection of machine condition.

Digitizing tablets provides a faster and accurate method to input handwritten characters to the computer. Recognition of Urdu handwritten characters is a difficult task due to large character set of Urdu and redundancy of the strokes. In this paper we present a method for online recognition of single stroke handwritten Urdu characters. Novel features have been proposed to ease classification. Digitizing tablet has been used to obtain the handwriting online.

Infant cry is a non-stationary, loud, high-pitched signal made by infants in response to certain situations. This acoustic signal can be used to identify physical or psychology status of infant. The aim of this work is to compare the performance of Daubechies wavelet family in infant cry classification. The orders of db1, db3, db4, db6 and db10 are chosen randomly for this investigation. Infant cry signals are decomposed into five levels using wavelet packet transform. Energy and entropy features are computed at different sub bands. Two different case studies such as, normal versus asphyxia and normal versus hypoacoustic are performed. Two different types of radial basis artificial neural networks namely, Probabilistic Neural Network (PNN) and General Regression Neural Network (GRNN) are used to classify the infant cry signals. The results emphasized that the proposed features and classification algorithms can be used to aid the medical professionals for diagnosing pathological status of infant cry.

Recently, high profile cases of financial statement fraud have been dominating the news. This paper uses data mining techniques such as Multilayer Feed Forward Neural Network (MLFF), Support Vector Machines (SVM), Genetic Programming (GP), Group Method of Data Handling (GMDH), Logistic Regression (LR), and Probabilistic Neural Network (PNN) to identify companies that resort to financial statement fraud. Each of these