Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractAssessment of Tannin Tolerant Non-Saccharomyces Yeasts Isolated from Miang for Production of Health-Targeted Beverage Using Miang Processing Byproducts    Next AbstractGC-MS identification of gaseous volatiles in wastewater »

IEEE Trans Inf Technol Biomed


Title:Artificial odor discrimination system using electronic nose and neural networks for the identification of urinary tract infection
Author(s):Kodogiannis VS; Lygouras JN; Tarczynski A; Chowdrey HS;
Address:"Centre for Systems Analysis, School of Computer Science, University of Westminster, London HA1 3TP, UK. kodogiv@wmin.ac.uk"
Journal Title:IEEE Trans Inf Technol Biomed
Year:2008
Volume:12
Issue:6
Page Number:707 - 713
DOI: 10.1109/TITB.2008.917928
ISSN/ISBN:1558-0032 (Electronic) 1089-7771 (Linking)
Abstract:"Current clinical diagnostics are based on biochemical, immunological, or microbiological methods. However, these methods are operator dependent, time-consuming, expensive, and require special skills, and are therefore, not suitable for point-of-care testing. Recent developments in gas-sensing technology and pattern recognition methods make electronic nose technology an interesting alternative for medical point-of-care devices. An electronic nose has been used to detect urinary tract infection from 45 suspected cases that were sent for analysis in a U.K. Public Health Registry. These samples were analyzed by incubation in a volatile generation test tube system for 4-5 h. Two issues are being addressed, including the implementation of an advanced neural network, based on a modified expectation maximization scheme that incorporates a dynamic structure methodology and the concept of a fusion of multiple classifiers dedicated to specific feature parameters. This study has shown the potential for early detection of microbial contaminants in urine samples using electronic nose technology"
Keywords:"Algorithms Artificial Intelligence Diagnostic Techniques, Urological/*instrumentation Electronics, Medical Fuzzy Logic Humans *Neural Networks, Computer Odorants/*analysis Point-of-Care Systems Robotics/instrumentation/methods Smell Urinary Tract Infectio;"
Notes:"MedlineKodogiannis, Vassilis S Lygouras, John N Tarczynski, Andrzej Chowdrey, Hardial S eng 2008/11/13 IEEE Trans Inf Technol Biomed. 2008 Nov; 12(6):707-13. doi: 10.1109/TITB.2008.917928"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 17-11-2024