Seismic post-stack inversion is one of the best techniques for effective reservoir characterization. This study intends to articulate application Model-Based Inversion (MBI) and Probabilistic Neural Networks (PNN) identification properties i.e. porosity estimation. MBI technique applied observe low impedance zone at porous formation. PNN a geostatistical that transforms volume into volume. Inve...

Our text-independent Probabilistic Neural Network (PNN)-based speaker verification system, referred to as WCL-1, a participant in the One-Speaker Detection Task of the 2003 NIST Speaker Recognition Evaluation (SRE), is presented here. The WCL-1 system is built on modular structure with a distinct PNN for each enrolled user. A gender-dependent universal background model, representing the world o...

In this paper, a wavelet-based neural network classifier for recognizing power quality disturbances is implemented and tested under various transient events. The discrete wavelet transform (DWT) technique is integrated with the probabilistic neural network (PNN) model to construct the classifier. First, the multi-resolution analysis (MRA) technique of DWT and the Parseval’s theorem are employed...

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 ...

A system that segments and labels tabla strokes from real performances is described. Performance is evaluated on a large database taken from three performers under different recording conditions, containing a total of 16,834 strokes. The current work extends previous work by Gillet and Richard (2003) on categorizing tabla strokes, by using a larger, more diverse database that includes their dat...

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...

The present work proposes towards flow pattern prediction in a liquidliquid microchannel flow through a circular channel. Mass transfer in a microchannel mainly depends on the flow regime inside the channel. The liquid-liquid two phase flow regime in a microchannel depends on the flow velocity and the junction characteristics. Hence, the prediction of patterns has a superior role for the charac...

Perineuronal Nets (PNNs) are specialized extracellular matrix structures of the brain that are found around specific neurons. PNNs play a role in structural and developmental plasticity, it remains unknown how they are affected by drugs of addiction. In the US, drug use has a very profound impact medically, economically, and socially. We used a conflict model which mimicked human drug seeking b...

BACKGROUND An effective screening tool for colorectal cancer is still lacking. Analysis of the volatile organic compounds (VOCs) linked to cancer is a new frontier in cancer screening, as tumour growth involves several metabolic changes leading to the production of specific compounds that can be detected in exhaled breath. This study investigated whether patients with colorectal cancer have a s...

The function of a protein is of great interest in the cutting-edge research of biological mechanisms, disease development and drug/target discovery. Besides experimental explorations, a variety of computational methods have been designed to predict protein function. Among these in silico methods, the prediction of BLAST is based on protein sequence similarity, while that of machine learning is ...