A novel technique for speaker independent automated speech recognition is proposed. We take a segment model approach to Automated Speech Recognition (ASR), considering the trajectory of an utterance in vector space, then classify using a modified Probabilistic Neural Network (PNN) and maximum likelihood rule. The system performs favourably with established techniques. Our system achieves in exc...

In this paper, we propose a new method for face detection from cluttered images. We use a polynomial neural network (PNN) for separation of face and non-face patterns while the complexity of the PNN is reduced by principal component analysis (PCA). In face detection, the PNN is used to classify sliding windows in multiple scales and label the windows that contain a face. The PNN is shown to be ...

In this paper, a novel and simple iris feature extraction technique is proposed for iris recognition of high performance. We use one dimensional circular ring to represent iris features. The reduced and significant features afterward are extracted by Sobel operator and 1D wavelet transform. So as to improve the accuracy, this paper combines Probabilistic Neural Network (PNN) and Particle Swarm ...

This paper presents transient stability assessment of electrical power system using probabilistic neural network (PNN) and principle component analysis. Transient stability of a power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the IEEE 9-bus test system considering three phase faults on th...

Probabilistic Neural Network has received considerable attention nowadays and obtained many successful application. This type of neural system has shown marvelous higher recognition capability compare with that of Back-Propagation neural system. However, this neural has shown some drawbacks, especially on determining the value of its smoothing parameter and its neural structure optimization whe...

The aim of the present study was to design, implement, and evaluate a software system for discriminating between metastases, meningiomas, and gliomas on MRI. The proposed classifier is a modified probabilistic neural network (PNN), incorporating a second degree least squares features transformation (LSFT) into the PNN classifier. Thirty-six textural features were extracted from each one of 75 T...

The perineuronal net (PNN) represents a lattice-like structure that is prominently expressed along the soma and proximal dendrites of parvalbumin- (PV-) positive interneurons in varied brain regions including the cortex and hippocampus. It is thus apposed to sites at which PV neurons receive synaptic input. Emerging evidence suggests that changes in PNN integrity may affect glutamatergic input ...

Previous studies have implemented Backpropagation Feedforward Neural Networks (BPNNs) as an alternative technique to Linear Discriminant Analysis (LDA) to predict bond ratings using relatively small data sets due to the limited number of bond ratings data available. Although these studies report the superiority of BPNNs over the LDA to predict bond ratings, they seem to ignore that BPNNs suffer...

To accurately and reliably predict the occurrence of rockburst disasters, a intensity level prediction model based on FA-SSA-PNN is proposed. Crding to internal external factors occurrence, six influencing (σθ, σt, σc, σc/σt, σθ/σc, Wet) were selected build index system. Seventy-five sets typical case data at home abroad collected, original preprocessed factor analysis (FA), comprehensive index...

The planning of an adequate nutritional support for meeting the metabolic requirements of sick neonates is a tedious time consuming calculation, needs practical expert knowledge and involves the risk of introducing possibly fatal errors. We therefore developed the interactive support system VIE–PNN for calculating the composition of parenteral nutrition solutions (PNS) for neonates at intensive...