Improved recognition of E. coli 0157:H7 bacteria from pulsed-field gel electrophoresis images through the fusion of neural networks with fuzzy training data
E. coli 0157:H7 is a particularly toxic strain of the E. coli bacteria group. Fast and accurate methodologies for recognizing this strain of bacteria are desired. In this paper, we demonstrate that the fusion of neural network outputs using (possibly redundant) banding pattern features from pulsed-f...
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creator | Keller, J.M. Dayou Wang Carson, C.A. McAdoo, K.K. Bailey, C.W. |
description | E. coli 0157:H7 is a particularly toxic strain of the E. coli bacteria group. Fast and accurate methodologies for recognizing this strain of bacteria are desired. In this paper, we demonstrate that the fusion of neural network outputs using (possibly redundant) banding pattern features from pulsed-field electrophoresis images, where the learning was done with fuzzy labeled training data, produced superior results over the single and crisp counterparts. |
doi_str_mv | 10.1109/ICNN.1995.488858 |
format | Conference Proceeding |
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Fast and accurate methodologies for recognizing this strain of bacteria are desired. 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Fast and accurate methodologies for recognizing this strain of bacteria are desired. In this paper, we demonstrate that the fusion of neural network outputs using (possibly redundant) banding pattern features from pulsed-field electrophoresis images, where the learning was done with fuzzy labeled training data, produced superior results over the single and crisp counterparts.</description><subject>Biochemistry</subject><subject>Biological cells</subject><subject>Capacitive sensors</subject><subject>DNA</subject><subject>Electrokinetics</subject><subject>Image recognition</subject><subject>Microorganisms</subject><subject>Neural networks</subject><subject>Pathogens</subject><subject>Training data</subject><isbn>9780780327689</isbn><isbn>0780327683</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1995</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNp9j0FLw0AUhBdEqGju4un9gcbEdN2N11JpLz15L2vysnl1kw1vN5b2t_hjXbBnh4HvMDDDCPFYFnlZFvXzbr3f52Vdy3yltZb6RmS10kVy9aJedb0QWQjHImklZaXknfjZDRP7b2yBsfF2pEh-BN_BJofGO4KilOptq-DTNBGZDHTsB5hmF7BddoSuBYsO0GET2U-9ZwwUgAZjMUDs2c-2T0To5nDtHnFm4xLiyfNXgBPFPsWXyxkiGxpptNCaaB7EbWfSUHblvXh633yst0tCxMPEaYTPh7-r1b_hLxDvWtA</recordid><startdate>1995</startdate><enddate>1995</enddate><creator>Keller, J.M.</creator><creator>Dayou Wang</creator><creator>Carson, C.A.</creator><creator>McAdoo, K.K.</creator><creator>Bailey, C.W.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1995</creationdate><title>Improved recognition of E. coli 0157:H7 bacteria from pulsed-field gel electrophoresis images through the fusion of neural networks with fuzzy training data</title><author>Keller, J.M. ; Dayou Wang ; Carson, C.A. ; McAdoo, K.K. ; Bailey, C.W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_4888583</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1995</creationdate><topic>Biochemistry</topic><topic>Biological cells</topic><topic>Capacitive sensors</topic><topic>DNA</topic><topic>Electrokinetics</topic><topic>Image recognition</topic><topic>Microorganisms</topic><topic>Neural networks</topic><topic>Pathogens</topic><topic>Training data</topic><toplevel>online_resources</toplevel><creatorcontrib>Keller, J.M.</creatorcontrib><creatorcontrib>Dayou Wang</creatorcontrib><creatorcontrib>Carson, C.A.</creatorcontrib><creatorcontrib>McAdoo, K.K.</creatorcontrib><creatorcontrib>Bailey, C.W.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Keller, J.M.</au><au>Dayou Wang</au><au>Carson, C.A.</au><au>McAdoo, K.K.</au><au>Bailey, C.W.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Improved recognition of E. coli 0157:H7 bacteria from pulsed-field gel electrophoresis images through the fusion of neural networks with fuzzy training data</atitle><btitle>Proceedings of ICNN'95 - International Conference on Neural Networks</btitle><stitle>ICNN</stitle><date>1995</date><risdate>1995</risdate><volume>4</volume><spage>1606</spage><epage>1610 vol.4</epage><pages>1606-1610 vol.4</pages><isbn>9780780327689</isbn><isbn>0780327683</isbn><abstract>E. coli 0157:H7 is a particularly toxic strain of the E. coli bacteria group. Fast and accurate methodologies for recognizing this strain of bacteria are desired. In this paper, we demonstrate that the fusion of neural network outputs using (possibly redundant) banding pattern features from pulsed-field electrophoresis images, where the learning was done with fuzzy labeled training data, produced superior results over the single and crisp counterparts.</abstract><pub>IEEE</pub><doi>10.1109/ICNN.1995.488858</doi></addata></record> |
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subjects | Biochemistry Biological cells Capacitive sensors DNA Electrokinetics Image recognition Microorganisms Neural networks Pathogens Training data |
title | Improved recognition of E. coli 0157:H7 bacteria from pulsed-field gel electrophoresis images through the fusion of neural networks with fuzzy training data |
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