If the training dataset is not very large, image recognition is usually implemented with the transfer learning methods. In these methods the features are extracted using a deep convolutional neural network, which was preliminarily trained with an external very-large dataset. In this paper we consider the nonparametric classification of extracted feature vectors with the probabilistic neural net...

Recognition and classification of Power Quality Distorted Signals (PQDSs) in power systems is an essential duty. One of the noteworthy issues in Power Quality Analysis (PQA) is identification of distorted signals using an efficient scheme. This paper recommends a Time–Frequency Analysis (TFA), for extracting features, so-called "hybrid approach", using incorporation of Multi Resolution Analysis...

Over the last decade, the world has witnessed the rapid development of networking applications of different kinds, and network domains have become more and more advanced regarding with their level of heterogeneity, complexity and the size. Some obstacles such as availability, flexibility and insufficient scalability have affected the existing centralized network management systems, as networks ...

In this paper, a prototype wavelet and probabilistic based neural network classifier for recognizing rotor bar defects is implemented and tested under various transient signals. The wavelet transform (WT) technique is integrated with the neural network model to extract rotor fault features. Firstly, the multiresolution analysis technique of WT and the particle swarm optimization (PSO) theorem a...

This paper presents the design of classifiers with neural network approach based on the method used Expectations Maximum (EM). The decision rule of Bayes classifier using the Minimum Error to the classification of a mixture of multitemporal imagery. In this particular, the multilayer perceptron neural network model with Probabilistic Neural Network (PNN) is used for nonparametric estimation of ...

Microarrays are being used to express thousands of genes at a time which is helpful to diagnose and cure many diseases with higher accuracy using diagnostic classifiers. However, 90% of the time gene expression datasets contain multiple missing values because of slide scratches, hybridization error, image corruption and etc. These missing values affect classifiers accuracy as most of the classi...

This paper presents the artificial neural network approach namely Back propagation network (BPNs) and probabilistic neural network (PNN). It is used to classify the type of tumor in MRI images of different patients with Astrocytoma type of brain tumor. The image processing techniques have been developed for detection of the tumor in the MRI images. Gray Level Co-occurrence Matrix (GLCM) is used...

An intelligent approach for high impedance fault (HIF) detection in power distribution feeders using advanced signal-processing techniques such as time–time and time–frequency transforms combined with neural network is presented. As the detection of HIFs is generally difficult by the conventional over-current relays, both time and frequency information are required to be extracted to detect and...

In this contribution we describe a neural approach to classify vehicles based on sound emitted by them. Engines and the carriageable devices are the sources of signal. The used methodology doesn't require the detailed analysis which part of object is responsible for the components of signal. The sound is preprocessed using wavelet method to obtain feature vectors to be used by a neural classifi...