NDLI: Non-exhaustive Learning for Bacteria Detection

Content Provider	IEEE Xplore Digital Library
Author	Akova, F. Hirleman, D. Bhunia, A.K. Rajwa, B. Dundar, M.M.
Copyright Year	2009
Abstract	Technologies for rapid detection and classification of bacterial pathogens are crucial for securing the food supply. A light-scattering sensor recently developed for real-time detection and identification of colonies of multiple pathogens has shown great promise for distinguishing bacteria cultures at the genus and species level for Listeria, Staphylococcus, Salmonella, Vibrio, and Escherichia. Unlike traditional testing methods, this new technology does not require a labeling reagent or biochemical processing. The classification approach currently used with this technology relies on supervised learning. For an accurate detection and classification of bacterial pathogens, the training library used to train the classifier should consist of samples of all possible forms of the pathogens. Construction of such a training library is impractical if not impossible due to the high mutation rate that characterizes some of the infectious agents. In this study we propose a Bayesian approach to advance this sensor technology to allow for the detection of new classes/subclasses of bacteria, which do not exist in the training library. Learning with a nonexhaustive training library is an ill-defined problem. We assume Gaussian distributions for bacteria subclasses and implement a maximum likelihood classifier. A pair of conjugate priors based on Wishart distribution is defined and the covariance matrices are estimated by the posterior mean. A new sample is classified into one of the existing set of classes if the maximum of the likelihoods is above a designated threshold. If not, the sample is considered a novelty, i.e. a sample of a potentially new class. We compare the proposed approach with a benchmark support estimation technique as well as a simulated Bayesian modelling approach recently proposed.
Starting Page	206
Ending Page	211
File Size	234998
Page Count	6
File Format	PDF
ISBN	9781424447466
DOI	10.1109/NBiS.2009.79
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pathogens Non-exhaustive learning Microorganisms Bayesian methods Food technology Supervised learning Genetic mutations Libraries Biosensors Labeling Novelty detection Testing
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Learning with a non-exhaustive training dataset: a case study: detection of bacteria cultures using optical-scattering technology

Kernel-based semi-supervised learning for novelty detection

Analysis of phylogenetic profiles using Bayesian decomposition

Detection of hlyA Gene of Listeria Monocytogenes with Electrochemical DNA Biosensor

Adhesin-Specific Nanomechnical Cantilever Biosensors for Detection of Microorganisms

Multi-functional biochip for biological agent detection

Bayesian networks classifiers applied to documents

Detection of pathogenic bacteria using immunofiltration and lightscribe technology

Microarray detection of labeled NASBA products for the specific identification of pathogenic bacteria using tmRNA as a target

Non-exhaustive Learning for Bacteria Detection

Similar Documents

Learning with a non-exhaustive training dataset: a case study: detection of bacteria cultures using optical-scattering technology

Kernel-based semi-supervised learning for novelty detection

Analysis of phylogenetic profiles using Bayesian decomposition

Detection of hlyA Gene of Listeria Monocytogenes with Electrochemical DNA Biosensor

Adhesin-Specific Nanomechnical Cantilever Biosensors for Detection of Microorganisms

Multi-functional biochip for biological agent detection

Bayesian networks classifiers applied to documents

Detection of pathogenic bacteria using immunofiltration and lightscribe technology

Microarray detection of labeled NASBA products for the specific identification of pathogenic bacteria using tmRNA as a target

Non-exhaustive Learning for Bacteria Detection